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Operations in Financial Services—An Overview
Emmanuel D. (Manos) HatzakisGoldman Sachs Asset Management,
Goldman, Sachs & Co., New York, New York 10282-2198, USA,
[email protected]
Suresh K. NairDepartment of Operations and Information
Management, School of Business, University of Connecticut, Storrs,
Connecticut 06269-1041, USA,
[email protected]
Michael L. PinedoDepartment of Information, Operations and
Management Science, Leonard N. Stern School of Business, New York
University,
New York, New York 10012-1106, USA, [email protected]
We provide an overview of the state of the art in research on
operations in financial services. We start by highlighting anumber
of specific operational features that differentiate financial
services from other service industries, and discusshow these
features affect the modeling of financial services. We then
consider in more detail the various different researchareas in
financial services, namely systems design, performance analysis and
productivity, forecasting, inventory and cashmanagement, waiting
line analysis for capacity planning, personnel scheduling,
operational risk management, and pricingand revenue management. In
the last section, we describe the most promising research
directions for the near future.
Key words: financial services; banking; asset management;
processes; operationsHistory: Received: November 2009; Accepted:
July 2010 by Kalyan Singhal, after 2 revisions.
1. IntroductionOver the past two decades, research in service
oper-ations has gained a significant amount of attention.Special
issues of Production and Operations Managementhave focused on
services in general (Apte et al. 2008),and various researchers have
presented unified theories(Sampson and Froehle 2006), research
agendas (Roth andMenor 2003), literature surveys (Smith et al.
2007),strategy ideas (Voss et al. 2008), and have discussedthe
merits of studying service science as a new discipline(Spohrer and
Maglio 2008). A few books and a specialissue of Management Science
have focused on the oper-ational issues in financial services in
particular (seeHarker and Zenios 1999, 2000, Melnick et al.
2000).However, financial services have still been given
scantattention in much of the literature relative to otherservice
industries such as transportation, health care,entertainment, and
hospitality. The dilution of focus, byconcentrating on more general
distinguishing featuresdoes not do justice to financial services
where some ofthese characteristics are not central. (The more
generalfeatures that are typically being considered
includeintangibility, heterogeneity, contemporaneous produc-tion
and consumption, perishability of capacity, waitinglines (rather
than inventories), and customer participa-tion in the service
delivery.)
In this overview, we mean by financial servicesprimarily firms
in retail banking, commercial lending,
insurance (other than health), credit cards, mortgagebanking,
brokerage, investment advisory, and assetmanagement (mutual funds,
hedge funds, etc.).
1.1. Importance of Financial ServicesFinancial services firms
are an important part of theservice sector in an economy that has
been growingrapidly over the past few decades. These firms
pri-marily deal with originating or facilitating
financialtransactions. The transactions include creation,
liqui-dation, transfer of ownership, and servicing ormanagement of
financial assets; they could involveraising funds by taking
deposits or issuing securities,making loans, keeping assets in
custody or trust, ormanaging them to generate return, pooling of
risk byunderwriting insurance and annuities, or
providingspecialized services to facilitate these transactions.
Services is a large category that encompasses firmsas diverse as
retail establishments, transportationfirms, educational
institutions, consulting, informa-tion, legal, taxation, and other
professional, real estate,and healthcare. Even within financial
services, there isa wide variety of firms which are characterized
byunique production processes and specialized skills.The processes
and skills required for banking arequite distinct from
solicitations for credit cards, ac-quisitions of new insurance
accounts, or the handlingof equity dividends and proxy voting, for
example.
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Even though services account for about 84% of thetotal
employment in the economy, only about 4% ofthis workforce is
employed in financial services. Thismight come as a surprise to
some because financialservices transactions in one form or another
are soubiquitous in our lives. Not surprisingly, however, thenumber
of financial services firms is about 7% of thetotal non-farm firms
and contributes about 13% of to-tal non-farm sales. Only wholesale
trade has a similaremployment and number of firms with a larger
con-tribution to sales.
Table 1 provides employment information for thesub-codes within
financial services. As can be seen,retail banks, insurance
companies, and insurance bro-kers together employ about two-thirds
of the financialservices workforce. A cursory look at the table
gives asense of the diversity of the services sector.
Clearlyoperations management problems and approachesused to solve
them have to be customized for partic-ular types of services—we
already know that whatworks for manufacturing may not work for
services,but by looking at Table 1 we can also realize that
whatworks for retail trade or recreational services may notwork for
financial services. A quick glance throughMonster.com’s job
openings for operations managersin financial service firms shows a
wide variety of ti-tles, responsibilities, and ‘‘products’’ related
to suchjobs. This is shown in Table 2, and gives a sense of thewide
swath of topics that could be covered in aca-demic research on
financial services operations. Asfinancial services are such an
important segment of
the services economy, we wish to explore whetheroperations in
financial services are indeed unique, orshare several
characteristics with services in general.That is the motivation for
this special issue.
1.2. Distinctive Characteristics of Operations inFinancial
ServicesThere are several unique operational characteristicsthat
are specific to the financial services industry andthat have not
been given sufficient attention in thegeneral treatment of services
in the extant literature.We list below a number of these unique
operationalcharacteristics and elaborate on them in what
follows:
� Fungible products with an extensive use oftechnology
� High volumes and heterogeneity of clients� Repeated service
encounters� Long-term contractual relationships between
customers and firms� Customers’ sense of well-being closely
inter-
twined with services� Use of intermediaries� Convergence of
operations, finance, and marketing.
1.2.1. Fungible Products with an Extensive Useof Technology. One
obvious difference betweenoperations in financial services and
operations inmanufacturing and in other service industries is
thatthe ‘‘widgets’’ in financial services are money, or
relatedfinancial instruments. As there is a declining use of
thephysical vestiges of money such as coins, currency,
Table 1 Employment within Financial Services
NAICS code Title within financial services
Total employees
Number %
5211 Monetary authorities-central bank
(Federal Reserve banks, etc.)
21,510 0.4
5221 Depository credit intermediation
(banks, credit unions, etc.)
1,816,300 30.7
5222 Non-depository credit intermediation
(credit cards, mortgage lending, etc.)
659,930 11.2
5223 Activities related to credit intermediation
(brokers for lending)
294,910 5.0
5231 Securities and commodity contracts
intermediation and brokerage
516,010 8.7
5232 Securities and commodity exchanges 8,010 0.1
5239 Other financial investment activities
(mutual funds, etc.)
344,950 5.8
5241 Insurance carriers 1,258,050 21.3
5242 Agencies, brokerages, and other
insurance-related activities
907,880 15.3
5251 Insurance and employee benefit funds 47,730 0.8
5259 Other investment pools and funds 41,190 0.7
5,916,470 100.0
Source: Bureau of Labor Statistics, stat.bls.gov/oes/home.htm,
May 2008.
Table 2 A sample of Financial Service Operations Job Titles,
Responsi-bilities, and Products
Titles Products
Vice President, Operations, Opera-
tions Manager, Financial Operations
Supervisor, Foreclosure and Bank-
ruptcy Operations Manager, Risk
Operations Team Manager, Team
Manager Ops Control-Fixed Income,
National Director Operations, Hedge
Fund Operations Specialist, VP/
Director Of Operations
Premiums, Claims, Refunds, Cash
flow and treasury management,
Customer statements, Loan servi-
cing and support, Trade
confirmations, Reconciliations, Tax
reporting, Security settlements,
Mortgages
Nature of work/responsibility
Brokerage operations, Improve customer service, resolves
customer issues,
Review security pricing, Vendor support, Authorize net
settlement, Hand-off of
data, ensure data integrity, Verifying transactions, Tracking
missing transactions,
Leverage technology, Maintain ops controls, update policies,
procedures, Back-
office support, Understand regulations, Ensure compliance,
Attain profit and
revenue benchmarks, Reduce risk, Improve quality, Six sigma,
Operational
processing efficiency, Problem solving, Ensure best practices,
Streamline
activities, Manage key expenses, Work management tools, Monitors
work flow,
Production/testing
Source: Monster.com jobs listings during the week of March 8,
2009.
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bond, or stock certificates, much of the transactions arein the
form of bits and bytes. Thus inventory is fungibleand can be
transported, broken up, and reconstituted(facilitating
securitization, e.g.) in malleable ways thatare simply not possible
in manufacturing or in otherservice industries (see, e.g., the
recharacterization ofbank reserves in Nair and Anderson 2008).
The increased use of online transactions (in broker-ages, credit
card payments, retail banking, and retire-ment accounts, e.g.) are
forcing fundamental changes inthe way operations managers think
about capacity is-sues (for statement mailing and remittance
processing,or for transfer of ownership in securities, e.g.). The
factthat adoption of online transactions is still growing andhas
not yet matured and leveled off makes capacityplanning a big
challenge. Yet we are aware of very littleresearch that would help
managers deal with this issue.
1.2.2. High Volumes and Heterogeneity of Clients.Financial
services are characterized by very highvolumes of customers and
transactions. Furthermore,customers are not all alike. In many
firms, a smallfraction of the customers generate most of the
profits,giving the firms an incentive to view them differentlyand
provide differential treatment, given the firms’limited resources.
For example, high net worthindividuals may be treated differently
by assetmanagement firms; banking clients who keep highbalances in
checking accounts and transact heavily maybe handled differently
from depositors who keep almostall their funds in savings accounts
and certificates ofdeposits (CDs) and transact minimally; revolvers
(i.e.,customers who carry over balances from one month tothe next)
may be regarded differently from transactors(customers who do not
revolve balances) by a creditcard firm. In most non-financial
services, because ofa limited number and sporadic interactions
withcustomers (e.g., in restaurants and amusement parks),one
customer is considered, for the most part, similar tothe next one
in terms of margins and attention required.
1.2.3. Repeated Service Encounters. In contrast toother service
industries, where research typicallyfocuses on a single encounter
(‘‘the moment oftruth,’’ ‘‘when the rubber hits the road’’),
financialservices are characterized by repeated serviceencounters
or potential encounters between the firmand its customers due to
regular monthly statements,year-end statements, buy/sell
transactions, insuranceclaims, money transfers, etc. Anecdotal
evidence fromthe brokerage and investment advisory industrysuggests
that clients with low asset balances andtransaction volumes
contribute the least to firm revenueand the most to operational
cost through calls forcustomer service. One online bank discouraged
calls tocustomer service because it found that just a few calls
by
a client could wipe out all the profit from the client’ssavings
account. Very little research in service operationsmanagement has
focused on this issue. New customersconstitute another major group
that is more likely tomake calls with billing questions or
inquiries regardingtheir statements. Should billing and
statementing to newcustomers be handled differently, perhaps with
morecare, than to existing customers? Obviously, if thisobservation
is true, differential handling can reduce thetraffic to call
centers. Existing call center research usuallyassumes the call
volume to be a given, for the most part,and the focus is on
‘‘managing’’ the traffic. This is akin totraditional manufacturing
where it was assumed thatlarge setup times were a given, and a good
way to‘‘manage’’ would be to use an optimal batch size. Onelearns
to live with such a constraint. Not until just intime (JIT)
manufacturing came along did managersquestion why setup times were
large and what could bedone to reduce them.
At one credit card company, operations managersstruggled for
years to cope with volatile demand inbill printing, mailing,
remittance processing, and callcenter operations. Daily volumes
could fluctuateeasily between half a million and one and a
halfmillion pieces of mail in remittance processing, andmanagers
were reconciled to high overtimes and idletimes because they felt
they had no control overwhen customers mailed in their checks, and
reduc-ing float was important. Call volumes at the callcenters were
similarly volatile, as were volumes inthe bill printing and mailing
operations. This situa-tion continued until someone recognized that
allthese problems were interconnected and to a largeextent within
the control of the firm. As it happens,the portfolio of current
customers is distributed intoabout 25 cycles, one for each working
day of themonth. For example, customers in the 17th cycle arebilled
on the 17th working day of the month. Care istaken to ensure that
customers in the same zip codeare put in the same cycle so
volume-mailing dis-counts from the US postal service can be
obtained,and the cycles are level loaded. However, this allo-cation
to cycles was carried out several years backand over time some
customers had closed theiraccounts while new customers had been
added toexisting cycles, resulting in large differences inthe
numbers of customers in the various cycles,and in a wide
variability in the printing and mailingof monthly statements. On
the remittance side, ananalysis found that there was less
randomness incustomer payment behavior than one would expect.There
were broadly four cohorts of customers:one that sent in payments on
receipt of the state-ment, a second that mailed checks based on
duedate, a third that acted based on salary paymentdate, and a
fourth that acted randomly. The first two
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cohorts, the largest ones, were in fact dependent onthe cycles
that the firm had set up many years back.Similarly, billing calls
were also heaviest soon afterthe statement was received by the
customer, againtraffic that was determined by the cycles created
bythe firm.
If the cycles could now be level loaded, many ofthese problems
would disappear (similar to whathappened in manufacturing when
setup times weredramatically reduced thereby enabling lean
opera-tions). But there was a problem—the firm needed toinform each
customer if their cycles were moved, forgood reason because
customers needed to plan theirfinances. However, this notification
was not neces-sary if the move was to be within � 3 days fromtheir
current cycle. An optimization model foundthat this constrained
move was sufficient to take careof the vast majority of moves that
were initiallythought to be necessary.
This example illustrates how stepping back andtaking a broader
view of the situation and collabo-rating across processes can have
a major impact onfinancial service operations, something that is
lack-ing in the current literature.
1.2.4. Long-Term Contractual Relationships BetweenCustomers and
Firms. Connected to the previouscharacteristic of repeat encounters
is the recognitionthat, unlike in other services, in financial
services thefirm and the customer have a relatively
long-termcontractual arrangement. However, technology and
in-formation availability makes comparison shoppingeasy, resulting
in easy switching between firms, andtherefore high attrition. This
loss of customers makes theacquisition process very important to
the continuedgrowth and profitability of the firm. Similarly,
loyaltyprograms (such as rewards and balance transferprograms in
the credit card business) are important tostanch the bleeding. The
design and execution of theseprograms are based on complicated
processes that needto consider risks, costs, redemptions,
incremental sales,scheduling and sequencing of offers, etc.
Researchers infinancial services operations, by not making
theirpresence felt in these areas, are missing the boat withregard
to issues that are the most important (‘‘must do’’activities) for
the firm, and may be paying instead toomuch attention to relatively
mundane and low-impactissues (‘‘good to do’’ activities).
Just as the above processes aim at increasing rev-enue, there
may be other processes that are put inplace to reduce unnecessary
costs. In the insurancebusiness, for example, the claims processes
may pri-marily revolve around a call center, which hasattracted
sufficient attention in the literature as wewill see later. But
unnecessary costs can be reducedby fraud prevention and detection,
and subrogation
activities (money the firm pays out but is owedto it by other
carriers). Timely intervention canavoid expiry of opportunities to
collect dollars owed,and more attention could be paid to even
smallopportunities. There is an extensive literature in riskand
insurance journals on scoring for fraud pre-vention and detection,
but leveraging that informa-tion in the claims process can benefit
from anoperations perspective.
Another example from the insurance industryconcerns worker’s
compensation claims, where theprocess for handling workplace injury
can havelong tails spanning several years before the claimis
closed. The process is complicated with inter-actions between the
worker, the employer, medicalpractitioners, hospitals, state
authorities, and law-yers (both on the staff of the insurance firm
andpanel counsel, i.e., lawyers who are hired on anad hoc basis).
There are several opportunitieshere (Jewell 1974) to speed up the
process (andspeed up the workers’ return to work, which is inthe
insurance firm’s interest), reduce costs, detectfraud, ensure that
review triggers are not over-looked, increase the utilization of
staff counselcompared with panel counsel by better schedulingof
appearances for hearings, etc. We are unawareof any recent
operations management literature inthis area.
1.2.5. Customers’ Sense of Well-Being CloselyIntertwined with
Services. Along with the ease ofmanipulating the putty at the core
of the financialservices process comes the responsibility of
workingwith something that is so close to the customer’s senseof
well-being and worth. Poor operations manage-ment that results in
delays, quality issues, or sloppi-ness can and will attract
regulatory scrutiny andunfavorable publicity, and will generate
immediaterebukes from the customer in the form of calls,complaints,
and because the account can be easilymoved around, customer
attrition. At least two factorsmake the detection of errors due to
operational faultsand their exposure to the clients relatively easy
infinancial services:
(i) the amount, frequency, and detail of communi-cation and
disclosure as required by regulation,and
(ii) the clients’ heightened propensity and incentiveto check
for error in something so closely linkedto their livelihood and
sense of security.
Because of the above, tolerance for error is signifi-cantly
lower than in other industries. For example,faulty processes
resulting in incorrect calculations ofinterest amounts in savings,
mortgage loan, or creditcard accounts, or in inappropriate handling
of stock
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dividend payments, become obvious immediatelyafter monthly
statements are sent out.
The above customer service issues are distinctfrom the perceived
quality of the performance of in-dividual customer brokerage
portfolios, retirementaccounts, annuities, mutual funds, and
interest accruedin retail banking. With the plethora of
informationavailable comparing a customer’s firm with others inthe
same space, moving an account to the competitionis only a few
clicks away. Even though performancemay depend on the economy, the
stock market, invest-ment research, and fund manager
performance,recognizing the costs (Schneider 2010) and
capacityissues (the increased transactions during market
tur-bulence, e.g.) are important operations managementconcerns that
have received little attention.
1.2.6. Use of Intermediaries. This is an importantaspect of the
financial services industry. In some casesa direct-to-consumer
approach is used (credit cards);in other cases most of the customer
facing work isdone by intermediaries (financial advisors,
insuranceagents, annuity sales through banks, etc.); and in
stillother cases the firm’s employees and its agents haveto
collaborate with one another (insurance). Workingthrough an
intermediary entails a set of issues notnormally seen in other
services that function withoutintermediaries. For example,
financial product andservice design and delivery get filtered
through theprism of what the agent feels is in his or her own
bestinterest. At times, the relationship between the firmand the
intermediary is not exclusive, hereby adding alayer of complexity
because the customer may choosebetween products from competing
firms. Therefore,what gets planned in the corporate offices of
thefinancial services firms and what is seen by thecustomer may be
quite different. The operationsmanagement literature, to our
knowledge, has notpaid attention to product and service design in
suchsituations, because the implications on customerlifetime
interactions with the firm go much beyondinitial pricing, product
features, and the inventory ofbrochures left with the agents.
1.2.7. Convergence of Operations, Finance, andMarketing. There
is probably no other industry wherethis convergence is more
pronounced. These functionsare supported and enabled by a healthy
dose ofstatistics, technology, and optimization. By focusingonly on
back-office operations such as call centers,researchers in service
operations are leaving a lot on thetable. There is very little
research in the serviceoperations literature that leverages this
convergence,which requires a choreography, as described by Vosset
al. (2008), who put it in a more limited context ofoperations and
marketing. For example, the client
acquisition process in full-service retail brokerage
andinvestment advisory firms begins at the corporate level,where it
draws resources from marketing, strategy,information technology,
and operations, and is ulti-mately implemented through the sales
force of brokers/financial advisors. Customer acquisition at a
credit cardcompany is a competitive differentiator and a
complexprocess focused on direct mail campaigns. At manylarge
firms, the budgets for direct mail run into severalhundred million
dollars annually. By focusing on thebilling mailroom, collections,
call centers, and billingcall centers, researchers in service
operations are work-ing on a problem akin to quality inspectors at
the end ofthe production line—by then it is too late, the volumesof
mail and calls are baked in during the mailingcampaign creation,
while their skills could have madethe mailings more effective and
targeted (given theminuscule response rates), resulting in fewer
delin-quent accounts (requiring fewer outbound collectioncalls),
and perhaps also fewer billing calls to inboundcall centers.
At a more sophisticated level, very few credit cardfirms use
contact history in mailing solicitations,which may result in
repeated mailings to chronicnon-responders. The managers developing
campaignstrategies may not have the analytical background thata
researcher in service operations can bring to bear,and the cycle
time for campaign creation is typicallyso long and complicated that
much attention getsexpended on scoring for credit and response,
filetransfers from credit bureaus and data vendors, scrub-bing of
data, etc. These complicated processes leavelittle time to
incorporate experience from a previousmailing because a reading of
the results of that mailingtakes time (prospective customers may
not respondimmediately, even if they do respond), and file
struc-tures may not have been designed to carry informationabout
previous contacts and the response to them.
Another indication of this convergence is that themajority of
the undergraduate and MBA hiring at aninvestment bank in the
greater New York City areafrom one of the region’s business schools
is in theCOO’s operation, whether the student concentra-tions are
in finance, marketing, information systems,or operations.
The foregoing does not imply that no significantwork has been
done in the research on financialservice operations, just that
areas of work have had anarrow focus. The purpose of this article
and thisspecial issue is to begin to expand that focus andencourage
research in neglected or emerging areas infinancial service
operations. We will survey existingresearch next, not only where
the attention is only onfinancial service operations, but also
where researchin service industries in general has substantial
appli-cation in financial service operations. In Appendix
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A, we provide an overview of the various operationsprocesses in
financial services and highlight theones that have been addressed
in operations man-agement literature.
This survey paper is organized as follows. Sections2 through 9
go over eight research directions that areof interest from an
operations management point ofview. The first couple of sections
consider the moregeneral research topics, whereas the later
sections gointo more specific topics and more narrowly
orientedresearch areas. Section 2 focuses on process and sys-tem
design in financial services, while section 3considers performance
measurements and analysis.Section 4 deals with forecasting, because
forecastingplays a major role in virtually every segment of
thefinancial services industry. The next section focuseson cash and
liquidity management; this section re-lates cash management to
classic inventory theory.Sections 6 and 7 deal with waiting line
managementand personnel scheduling in retail banking and incall
centers. Even though these two topics are stronglydependent on one
another, they are treated separately;the reason being that the
techniques required for deal-ing with each one of these two topics
happen to bequite different from one another. Section 8 focuseson
operational risk in financial services. This areahas become very
important over the last decade andthis section describes how this
area relates to otherresearch areas in operations management, such
as to-tal quality management (TQM). Section 9 considersproduct
pricing and revenue management issues. Thelast section, section 10,
presents our conclusions anddiscusses future research
directions.
2. Financial Services System DesignService systems design has
attracted quite a bit ofattention in the academic literature. It is
clear thatservice design has to be as rigorous an activity
asproduct design, because the customer experiences theservice first
hand, much like a product, and comesaway with impressions regarding
the quality of ser-vice. Although the quality of service delivery
dependson a number of factors, such as associate
training,technology, traffic, neighborhood customer profile,access
to the service (channel access), and quality ofresource inputs, the
service experience gets baked intothe process at the time of the
service design itself, andtherefore a proper service design is
fundamental tothe success of the customer experience.
2.1. Aspects of Service DesignService research has usually
focused on capacity man-agement (type of customer contact,
scheduling, anddeployment) and the impact of the response to
vari-ability on costs and quality. For long the nature ofcustomer
contact has influenced service design think-
ing by creating front-office/back-office functions(Sampson and
Froehle 2006, Shostack 1984). Shostackalso pioneered the use of
service blueprinting foridentifying fail points where the firm may
face qualityproblems. She illustrated this methodology for a
dis-count brokerage and correctly identified that many ofthe
operational processes are not seen by the customer;she then focused
on the telephone communication step,the only one with client
contact. This focus on clientcontact tasks, whether in the front
office or in the backoffice, is widespread in services research in
general andin research on financial services operations in
particular.One reason may be that service researchers have foundit
necessary to motivate their work by differentiatingservices from
products (whether it is service marketingvs. product marketing, or
service design vs. productdesign), and client contact is an obvious
differentiator.
From the outset, it has been clear that serviceprocesses are
subject to a significant amount of ran-domness from various
sources. Frei (2006) discussesthe various sources of randomness in
service processesand how firms react to them in the design of
theirservices. She identifies five types of variability—customer
arrival variability, request variability, cus-tomer capability
variability, customer effort variability,and customer preference
variability. She states thatfirms design services to factor in this
variability bytrying either to accommodate the variability at a
highercost (cross training of employees, increased automa-tion,
variable staffing) or to reduce the variability witha view to
increasing efficiency rather than cost (offpeak pricing, standard
option packages, combo meals).
2.2. Focus on Single EncountersMuch of the services literature,
however, focuses onsingle service encounters, which are common in
ser-vices such as fast food. Even if a customer repeatedlyvisits
the same restaurant, there is not the kind ofstickiness to the
relationship as can be found infinancial services. Retail banking
seems to haveattracted the most attention among financial
serviceswith respect to service design, but here again thefocus is
on disparate single visits to the branch orAutomated Teller Machine
(ATM), rather than as partof a life cycle of firm–customer
interactions. Otherthan meeting the branch manager when opening
anaccount, there is usually no other recognition of thestage of the
relationship in the delivery of service.Perhaps this will change
with time as more firms startexperimenting with their service
delivery design asBank of America has been doing (Thomke 2003).
2.3. Descriptive vs. Prescriptive Studies of
FinancialServicesSeveral descriptive studies have focused on
retailbanking (Menor and Roth 2008, Menor et al. 2001),
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substitution of labor with information technology(Fung 2008),
the use of customer feedback to improvecustomer satisfaction
(Krishnan et al. 1999), the useof distribution channels (Lee et al.
2004, Xue et al.2007), self-service technologies (such as ATMs,pay
at the pump, see Campbell and Frei 2010a, b,Meuter et al. 2000),
online banking (Hitt andFrei 2002), and e-services in general (see
Boyer et al.2002, Ciciretti et al. 2009, Clemons et al. 2002,
Furstet al. 2002, Menor et al. 2001). These studies talk aboutthe
types of customers who use the various differentchannels and how
firms have diversified their deliv-ery of services using these new
channels as newertechnologies have become available. However,
theyare usually descriptive, rather than prescriptive, inthat they
speak about how existing firms and cus-tomers have already adopted
these technologies,rather than what they should be doing in the
future.For example, there are few quantitative metrics tomeasure a
product (e.g., its complexity vis-a-viscustomer knowledge), a
process (e.g., face to face vs.automated), and proximity (on-site
or off-site) to helpa manager navigate financial service operations
strat-egies from a design standpoint based on where herfirm is now.
In that sense, financial service systemdesign still has ways to go
to catch up with productdesign (product attributes, customer
utility, pricing,form and function, configuration, product
develop-ment teams, etc.) and manufacturing process design(process
selection, batch/line, capacity planning,rigid/flexible automation,
scheduling, location analy-sis, etc.). Because batching and lot
sizing issues havebeen of considerable interest in the history of
thestudy of manufacturing processes, and because onlinetechnologies
have made the concept of batching con-siderably less important, it
would be interesting to seehow research in service systems design
unfolds in thefuture. One paper with prescriptive
recommendationsfor service design in the property casualty
insuranceindustry is due to Giloni et al. (2003).
3. Financial Services PerformanceMeasurement and Analysis
3.1. Best Practices and Process ImprovementMany service firms
are measuring success by factorsother than profitability, using
such factors as customerand employee loyalty, as measured by
retention,depth of relationship, and lifetime value (Heskettet al.
1994). Chen and Hitt (2002), in an empiricalstudy on retention in
the online brokerage industry,found that ease of use, breadth of
offerings, and qual-ity reduce customer attrition. Balasubramanian
et al.(2003) find that trust is important for online transac-tions,
because physical appearance of branches, etc.no longer matter in
such situations. Instead, perceived
environmental security, operational competence, andquality of
service help create trust.
In general, service quality is difficult to manage andmeasure
because of the variability in customer expec-tations, their
involvement in the delivery of theservice, etc. In general, there
may be two differentmeasures of service quality that are commonly
used:the first refers to and measures the actual service pro-vided
(e.g., customer satisfaction, resolution, etc.), thesecond may
refer to the availability of service capac-ity/personnel (e.g.,
service level, availability, waitingtime, etc.). The first type of
quality measure is not asnebulous in financial services where the
output isgenerally related to monetary outcomes. If there is
anerror in the posting of a transaction, or if quarterlyreturns
from a mutual fund are below industry per-formance, there is an
immediate customer reactionand the points in the service design
that caused suchfailures to occur is apparent, whether it is in
remit-tance processing or in the hiring of a fund manager.Quality
in financial services is not influenced by suchmatters as the mood
of the customer, as may be thecase in other services. This makes
ensuring quality infinancial services more doable and one of the
foci ofthe research in operational risk management whichwe will
discuss later.
Roth and Jackson (1995) found that market intelli-gence and
imitation of best practices can be aneffective way of improving
service quality, and thatservice quality is more influenced by
service processchoices and the cumulative impact of investmentsthan
by people’s capabilities. Productivity measure-ment in services is
also a challenge (Sampson andFroehle 2006). Bank performance as a
result of processvariation has been studied by Frei et al.
(1999).
This current special issue of Production and Opera-tions
Management provides some interesting newcases of process
improvement in financial services.The paper by Apte et al. (2010),
‘‘Analysis andimprovement of information-intensive services:
Evi-dence from insurance claims handling operations,’’presents a
classification of information-intensiveservices based on their
operational characteristics;this paper proposes an empirically
grounded concep-tual analysis and prescriptive frameworks that can
beused to improve the performance of information- andcustomer
contact-intensive services. The paper by DeAlmeida Filho et al.
(2010) focuses on collection pro-cesses in consumer credit. They
develop a dynamicprogramming model to optimize the collections
pro-cess in consumer credit. Collection processes havebeen the
Cinderella of consumer lending research,because psychologically
lenders do not enjoy analyz-ing their mistakes, and also once an
accounting loss isascribed to a defaulted loan, there had been
littleincentive for senior managers to keep track of how
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much will be subsequently collected. The paper byBuell et al.
(2010) investigates why self-servicecustomers are more reluctant to
change their serviceprovider. This paper’s primary contribution is
toinvestigate how satisfaction and switching costs con-tribute to
retention among self-service customers. Thisis a particularly
important issue in the financial ser-vices industry where
considerable investments havebeen made in developing self-service
distributionchannels, and migrating customers to them.
3.2. An Example of Best Practices: AssetManagementAsset
management provides an interesting example ofan area within the
financial services sector that hasbeen receiving an increasing
amount of researchattention with regard to best practices from
variousoperations management perspectives. The body ofresearch on
operations management in asset manage-ment is growing, however, not
always produced byoperations management researchers, but often
bythose in the finance world (Black 2007, Brown et al.2009a, b,
Kundro and Feffer 2003, 2004, Stulz 2007),who examine operational
risk issues in hedge funds.A collection of operations management
researchpapers in asset management can be found in a recentbook by
Pinedo (2010). Alptuna et al. (2010) present abest practices
framework for the operational infra-structure and controls in asset
management and arguethat it is possible to effectively implement
such aframework in organizations that enjoy a strong,
prin-ciple-based governance. They examine conditionsunder which the
cost-effective strategy of outsourc-ing asset management operations
can be successfulfor asset managers and their clients. Figure 1,
whichhas been adapted from Alptuna et al. (2010), shows
the multiple constituent parts that must work togetherin order
for a typical asset management organizationto function effectively.
Figure 2, also adapted fromAlptuna et al. (2010), lists the
functions in the invest-ment management process according to their
distancefrom the end client. Typically, the
operations-intensivefunctions reside in the middle and back
offices;accordingly, the untapped research potential of oper-ations
in asset management must be sought there. Onecan create a similar
framework, as shown in Figures 1and 2, for a typical retail bank,
credit card issuer,mortgage lender, brokerage, trust bank, asset
custo-dian, life or property/casualty insurer, among others,none of
which is less complex than an asset manager.Outsourcing operations
adds to the complexity by in-troducing elements of quality control
for outsourcedpieces and coordination between the main
organiza-tion and the third-party provider (State Street 2009).To
develop their framework, Alptuna et al. (2010)draw heavily on asset
management industry resourceson best practices, namely the Managed
Funds Associ-ation’s Sound Practices for Hedge Fund Managers(2009),
the Report of the Asset Managers’ Committee tothe President’s
Working Group on Financial Markets(2009), the Alternative
Investment Management Asso-ciation’s Guide to Sound Practices for
European HedgeFund Managers (2007), and the CFA Institute’s
AssetManager Code of Professional Conduct (2009).
Schneider (2010) provides a framework for assetmanagement firms
to analyze their costs. Arfelt (2010)proposes an adaptation of the
Lean Six Sigma frame-work used in automobile manufacturing for
assetmanagement. Biggs (2010) advocates a decentraliza-tion of risk
management, accountability as well astechnology and expense control
in asset managementfirms. Cruz (2010) argues that the focus of cost
man-
Asset managemento Investment research, management,
and execution o
o
o
Sales and client relationship managementProduct development
Marketing
Independent internal oversight functions
o Compliance, legal and regulatory o Controllers o Credit and
market risk
management o Internal audit o Valuation oversight
Internal support teamso Billing o Human resources o Operations o
Operational risk o Performance o Tax o Technology o Treasury
External service providerso Brokerage, clearing and execution o
Custody and trust services o Fund administrator o Prime brokerage
and financing o Reputable auditor o Valuation (reputable
third-party
valuation firm)
Figure 1 Typical Structure of an Asset Management
Organization
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agement programs at asset management firms shouldbe strategic
and tactical (see also Cruz and Pinedo2009). Nordgard and
Falkenberg (2010) give an ITperspective on costs in asset
management. Campbelland Frei (2010a) examine cost structure
patternsin the asset management industry. Amihud andMendelson
(2010) examine the effect of transactioncosts on asset management,
and study their implica-tions for portfolio construction, fund
design, tradeimplementation, cash and liquidity management,
andcustomer acquisition and development strategies.
3.3. Performance Analysis Through DataEnvelopment Analysis
(DEA)There are numerous studies on performance and pro-ductivity
analyses of retail banking that are based onDEA. DEA is a technique
for evaluating productivitymeasures that can be applied to service
industries ingeneral. It compares productivity measures of
differ-ent entities (e.g., bank branches) within the sameservice
organization (e.g., a large retail bank) to oneanother. Such a
comparative analysis then boils downto the formulation of a
fractional linear program. DEAhas been used in many retail banks to
compareproductivity measures of the various branches withone
another. Sherman and Gold (1985), Sherman andLadino (1995), and
Seiford and Zhu (1999) performedsuch studies for US banks; Oral and
Yolalan (1990)performed such a study for a bank in Turkey;
Vassi-loglou and Giokas (1990), Soteriou and Zenios (1999a),
Zenios et al. (1999), Soteriou and Zenios (1999b),
andAthanassopoulos and Giokas (2000) for Greek banks;Kantor and
Maital (1999) for a large Mideast bank;and Berger and Humphrey
(1997) for various inter-national financial services firms. These
papers discussoperational efficiency, profitability, quality, stock
mar-ket performance, and the development of better costestimates
for banking products via DEA. Cumminset al. (1999) use DEA to
explore the impact oforganizational form on firm performance. They
com-pare mutual and stock property liability companiesand find that
in using managerial discretion and cost-efficiency stock companies
perform better, and in linesof insurance with long payouts mutual
companiesperform better.
Cook and Seiford (2009) present an excellent over-view of the
DEA developments over the past 30 yearsand Cooper et al. (2007)
provide a comprehensivetextbook on the subject. For a good survey
andcautionary notes on the pitfalls of improper interpre-tation and
use of DEA results (e.g., loosely using theresults for evaluative
purposes when uncontrollablevariables exist), see Metters et al.
(1999). Zhu (2003)discusses methods to solve imprecise DEA
(IDEA),where data on inputs and outputs are either bounded,ordinal,
or ratio bounded, where the original linearprogramming DEA
formulation can no longer be used.
Koetter (2006) discusses the stochastic frontieranalysis (SFA)
as another bank efficiency analysisframework, which contrasts to
the deterministic DEA.
Asset management - Investment research - Portfolio and risk
management -
-
Sales and client relationshipmanagementProduct development
Trade execution - Financial
InformationeXchange (FIX) connectivity
- Trade order management and execution
Middleoffice
Investment operations - Billing - Cash administration - Client
data warehouse - Client reporting
- Corporate actions processing
- Data management - OTC derivatives
processing
- Performance and analytics
- Portfolio recordkeepingand accounting
- Reconciliation processing
- Transaction management
Back office Fund accounting - Daily, monthly, and ad-
hoc reporting - General ledger - NAV calculation -
Reconciliation - Security pricing
Global custody - Assets safekeeping - Cash availability - Failed
trade
reporting- Income/tax
reclaims- Reconciliation - Trade settlement
Transfer agency - Shareholder
servicing
Frontoffice
Figure 2 Investment Management Process Functions
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4. ForecastingForecasting is very important in many areas of
thefinancial services industry. In its most familiar form inwhich
it presents itself to customers and the generalpublic, it consists
of economic and market forecastsdeveloped by research and strategy
groups in broker-age and investment management firms. However,
thetypes of forecasting we discuss tend to be more inter-nal to the
firms and not visible from the outside.
4.1. Forecasting in the Management of CashDeposits and Credit
LinesDeposit-taking institutions (e.g., commercial banks,savings
and loan associations, and credit unions) areinterested in
forecasting the future growth of theirdeposits. They use this
information in the process ofdetermining the value and pricing of
their depositproducts (e.g., checking, savings, and money
marketaccounts, and also CDs), for asset–liability manage-ment, and
for capacity considerations. Of specialinterest to these
institutions are demand deposits,more broadly defined as
non-maturity deposits.Demand deposits have no stated maturity and
thedepositor can add to the balance without restriction,or withdraw
from ‘‘on demand,’’ i.e., without warningor penalty. In contrast,
time deposits, also known asCDs, have a set maturity and an amount
establishedat inception, with penalties for early
withdrawals.Forecasting techniques have been applied to
demanddeposits because of their relative non-stickiness due tothe
absence of contractual penalties. A product withsimilar
non-stickiness is credit card loans. Jarrow andVan Deventer’s
(1998) model for valuing demanddeposits and credit card loans using
an arbitrage-freemethodology assumes that demand deposit
balancesdepend only on the future evolution of interest
rates;however, it does allow for more complexity, such
asmacroeconomic variables (income or unemployment),and local market
or firm-specific idiosyncratic factors.Janosi et al. (1999) use a
commercial bank’s demanddeposit data and aggregate data for
negotiable orderof withdrawal (NOW) accounts from the
FederalReserve to empirically investigate Jarrow and Van
Dev-enter’s model. They find demand deposit balances to bestrongly
autoregressive, i.e., future balances are highlycorrelated with
past balances. They develop regressionmodels, linear in the
logarithm of balances, in whichpast balances, interest rates, and a
time trend are pre-dictive variables. O’Brien (2000) adds income to
the setof predictive variables in the regression models. Shee-han
(2004) adds month-of-the-year dummy variables inthe regressions to
account for calendar-specific inflows(e.g., bonuses or tax
refunds), or outflows (e.g., taxpayments). He focuses on core
deposits, i.e., checkingaccounts and savings accounts;
distinguishes betweenthe behavior of total and retained deposits;
and devel-
ops models for different deposit types, i.e., business
andpersonal checking, NOW, savings, and money marketaccount
deposits.
Labe and Papadakis (2010) discuss a propensityscore matching
model that can be used to forecast thelikelihood of Bank of
America’s retail clients bringing innew funds to the firm by
subscribing to promotionalofferings of CDs. Such promotional CDs
carry anabove-market premium rate for a limited period oftime.
Humphrey et al. (2000) forecast the adoption ofelectronic payments
in the United States; they find thatone of the reasons for the slow
pace of moving fromchecks to electronic payments in the United
States is thecustomers’ perceived loss of float. Many electronic
pay-ment systems now address this, by allowing forpayment at the
due date rather than immediately.
Revolving credit lines, or facilities, give borrowersaccess to
cash on demand for short-term funding needsup to credit limits
established at facility inception. Bankstypically offer these
facilities to corporations with in-vestment grade credit ratings,
which have access tocheaper sources of short-term funding, for
example,commercial paper, and do not draw significant amountsfrom
them except:
(i) for very brief periods of time under normalconditions,
(ii) when severe deterioration of their financialcondition
causes them to lose access to thecredit markets, and
(iii) during system-wide credit market dysfunction,such as
during the crisis of 2007–2009.
Banks that offer these credit facilities must set asideadequate,
but not excessive, funds to satisfy the de-mand for cash by
facility borrowers. Duffy et al. (2005)describe a Monte Carlo
simulation model that MerrillLynch Bank used to forecast these
demands for cashby borrowers of their revolver portfolio. The
modeluses industry data for revolver usage by borrowercredit
rating, and assumes Markovian credit ratingmigrations, correlated
within and across industries.Migration probabilities were provided
by a majorrating agency, and correlation estimates were calcu-lated
by Merrill Lynch’s risk group. The model wasused by Merrill Lynch
Bank to help manage liqui-dity risk in its multi-billion portfolio
of revolvingcredit lines.
Forecasting the future behavior and profitability ofretail
borrowers (e.g., for credit card loans, mortgages,and home equity
lines of credit) has become a keycomponent of the credit management
process. Fore-casting involved in a decision to grant credit to a
newborrower is known as ‘‘credit scoring,’’ and its originsin the
modern era can be found in the 1950s. A dis-cussion of credit
scoring models, including relatedpublic policy issues, is offered
by Capon (1982). Fore-
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casting involved in the decisions to adjust credit ac-cess and
marketing effort to existing borrowers isknown as ‘‘behavioral
scoring.’’ The book by Thomaset al. (2002) contains a comprehensive
review of theobjectives, methods, and practical implementation
ofcredit and behavioral scoring. The formal statisticalmethods used
for classifying credit applicants into‘‘good’’ and ‘‘bad’’ risk
classes is known as ‘‘classifi-cation scoring.’’ Hand and Henley
(1997) reviewa significant part of the large body of literature
inclassification scoring. Baesens et al. (2003) examinethe
performance of standard classification algorithms,including
logistic regression, discriminant analysis,k-nearest neighbor,
neural networks, and decision trees;they also review more recently
proposed ones, such assupport vector machines and least-squares
supportvector machines (LS-SVM). They find LS-SVM, andneural
network classifiers, and simpler methods such aslogistic regression
and linear discriminant analysis tohave good predictive power. In
addition to classifica-tion scoring, other methods include:
(i) ‘‘response scoring,’’ which aims to forecast aprospect’s
likelihood to respond to an offer forcredit, and
(ii) ‘‘balance scoring,’’ which forecasts the pros-pect’s
likelihood of carrying a balance if theyrespond.
To improve the chances of acquiring and maintainingprofitable
customers, offers for credit should be mailedonly to prospects with
high credit, response, and bal-ance scores. Response and balance
scoring models aretypically proprietary. Trench et al. (2003)
discuss amodel for optimally managing the size and pricing ofcard
lines of credit at Bank One. The model usesaccount-level historical
transaction information to selectfor each cardholder, through
Markov decision processes,annual percentage rates, and credit lines
that optimizethe net present value of the bank’s credit
portfolio.
4.2. Forecasting in Securities Brokerage, Clearing,and
ExecutionIn the last few decades, the securities brokerageindustry
has seen dramatic change. Traditional wire-houses charging fixed
commissions evolved or werereplaced by diverse organizations
offering full service,discount, and online trading channels, as
well as re-search and investment advisory services. Thisevolution
has introduced a variety of channel choicesfor retail and
institutional investors. Pricing, servicemix and quality, and human
relationships are keydeterminants in the channel choice decision.
Firms areinterested in forecasting channel choice decisions
byclients, because they greatly impact capacity planning,revenue,
and profitability. Altschuler et al. (2002) dis-cuss simulation
models developed for Merrill Lynch’s
retail brokerage to forecast client choice decisions
onintroduction of lower-cost offerings to complementthe firm’s
traditional full-service channel. Clientchoice decision forecasts
were used as inputs in theprocess of determining the proper pricing
for thesenew offerings and for evaluating their potentialimpact on
firm revenue. The results of a rational eco-nomic behavior (REB)
model were used as a baseline.The REB model assumes that investors
optimize theirvalue received by always choosing the
lowest-costoption (determined by an embedded optimizationmodel that
was solved for each of millions of clientsand their actual
portfolio holdings). The REB model’sresults were compared with
those of a Monte Carlosimulation model. The Monte Carlo simulation
allowsfor more realistic assumptions. For example,
clients’decisions are impacted not only by price
differentialsacross channels, but also by the strength and
qualityof the relationship with their financial advisor,
whorepresented the higher-cost options.
Labe (1994) describes an application of forecastingthe
likelihood of affluent prospects becoming MerrillLynch’s priority
brokerage and investment advisoryclients (defined as clients with
more than US$250,000in assets). Merrill Lynch used discriminant
analysis, amethod akin to classification scoring, to select
highquality households to target in its prospecting efforts.
The trading of securities in capital markets involveskey
operational functions that include:
(i) clearing, i.e., establishing mutual obligations
ofcounterparties in securities and/or cash trades, aswell as
guarantees of payments and deliveries,and
(ii) settlement, i.e., transfer of titles and/or cash tothe
accounts of counterparties in order to final-ize transactions.
Most major markets have centralized clearingfacilities so that
counterparties do not have to settlebilaterally and assume credit
risk to each other. Thecentral clearing organization must have
robust pro-cedures to satisfy obligations to counterparties,
i.e.,minimize the number of trades for which delivery ofsecurities
is missed. It must also hold adequate, butnot excessive, amounts of
cash to meet payments.Forecasting the number and value of trades
during aclearing and settlement cycle can help the organiza-tion
meet the above objectives; it can achieve this bymodeling the
clearing and settlement operation usingstochastic simulation. A
different approach is used byde Lascurain et al. (2011): they
develop a linear pro-gramming method to model the clearing and
settlementoperation of the Central Securities Depository ofMexico
and evaluate the system’s performance throughdeterministic
simulation. The model’s formulation in deLascurain et al. (2011) is
a relaxation of a mixed integer
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programming (MIP) formulation proposed by Güntzeret al. (1998),
who show that the bank clearing problemis NP-complete. Eisenberg
and Noe (2001) includeclearing and settlement in a systemic
financial riskframework.
4.3. Forecasting of Call Arrivals at Call CentersForecasting
techniques are also used in various otherareas within the financial
services sector; for example,the forecasting of arrivals in call
centers, which is acrucial input in the personnel scheduling
processin call centers (to be discussed in a later section). Toset
this in a broader context, refer to the framework ofThompson (1998)
for forecasting demand for services.Recent papers that focus on
forecasting call centerworkload include a tutorial by Gans et al.
(2003), asurvey by Aksin et al. (2007), and a research paper
byAldor-Noiman et al. (2009).
The quality of historical data improves with thepassage of time
because call centers become increas-ingly more sophisticated in
capturing data with everynuance that a modeler may find useful or
interesting.Andrews and Cunningham (1995) describe the
auto-regressive integrated moving average (ARIMA) fore-casting
models used at L.L. Bean’s call centers. Timeseries data used to
fit the models exhibit seasonalitypatterns and are also influenced
by variables such asholiday and advertising interventions.
Advertisingand special calendar effects are addressed by Antipovand
Meade (2002). More recently, Soyer andTarimcilar (2008) incorporate
advertising effects bymodeling call arrivals as a modulated Poisson
processwith arrival rates being driven by customer calls thatare
stimulated by advertising campaigns. They use aBayesian modeling
framework and a data set from acall center that enables tracing
calls back to specificadvertisements. In a study of Fedex’s call
centers,Xu (2000) presents forecasting methodologies usedat
multiple levels of the business decision-makinghierarchy, i.e.,
strategic, business plan, tactical, andoperational, and discusses
the issues that each meth-odology addresses. Methods used include
exponentialsmoothing, ARIMA models, linear regression, andtime
series decomposition.
At low granularity, call arrival data may have toomuch noise.
Mandelbaum et al. (2001) demonstratehow to remove relatively
unpredictable short-termvariability from data and keep only
predictable vari-ability. They achieve this by aggregating data
athigher levels of granularity, i.e., progressively mov-ing up from
minute of the hour to hour of the dayto day of the month and to
month of the year. Theelegant textbook assumption that call
arrivals follow aPoisson process with a fixed rate that is known or
canbe estimated does not hold in practice. Steckley et al.(2009)
show that forecast errors can be large in com-
parison to the variability expected in a Poissonprocess and can
have significant impact on the pre-dictions of long-run
performance; ignoring forecasterrors typically leads to
overestimation of perfor-mance. Jongbloed and Koole (2001) found
that the callarrival data they had been analyzing had a
variancemuch greater than the mean, and therefore did notappear to
be samples of Poisson distributed randomvariables. They addressed
this ‘‘overdispersion’’ byproposing a Poisson mixture model, i.e.,
a Poissonmodel with an arrival rate that is not fixed but ran-dom
following a certain stochastic process. Brownet al. (2005) found
data from a different call center thatalso followed a Poisson
distribution with a variablearrival rate; the arrival rates were
also serially corre-lated from day to day. The prediction model
proposedincludes the previous day’s call volume as an
auto-regressive term. High intra-day correlations werefound by
Avramidis et al. (2004), who developedmodels in which the call
arrival rate is a randomvariable correlated across time intervals
of the sameday. Steckley et al. (2004) and Mehrotra et al.
(2010)examine the correlation of call volumes at later peri-ods of
a day to call volumes experienced earlier in theday for the purpose
of updating workload schedules.
Methods to approximate non-homogeneous Pois-son processes often
attempt to estimate the arrival rateby breaking up the data set
into smaller intervals.Henderson (2003) demonstrates how a
heuristic thatassumes a piecewise constant arrival rate over
timeintervals with a length that shrinks as the volumeof data grows
produces good arrival rate functionestimates. Massey et al. (1996)
fit piecewise linear ratefunctions to approximate a general
time-inhomoge-neous Poisson process. Weinberg et al. (2007)
forecastan inhomogeneous Poisson process using a Bayesianframework,
whereby from a set of prior distributionsthey estimate the
parameters of the posterior dis-tribution through a Monte Carlo
Markov Chainmodel. They forecast arrival rates in short intervalsof
15–60 minutes of a day of the week as the productof a day’s
forecast volume times the proportion ofcalls arriving during an
interval; they also allow for arandom error term.
Shen and Huang (2005, 2008a, b) developed modelsfor inter-day
forecasting and intra-day updating ofcall center arrivals using
singular value decomposi-tion. Their approach resulted in a
significant dimen-sionality reduction. In a recent empirical study,
Taylor(2008) compared the performance of several univari-ate time
series methods for forecasting intra-day callarrivals. Methods
tested included seasonal autore-gressive and exponential smoothing
models, and thedynamic harmonic regression of Tych et al.
(2002).Results indicate that different methods perform bestunder
different lead times and call volumes levels.
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Forecasting other aspects of a call center with asignificant
potential for future research include, forexample, waiting times of
calls in queues, see Whitt(1999a, b) and Armony and Maglaras
(2004).
5. Inventory and Cash Management
5.1. Cash Inventory Management UnderDeterministic and Stochastic
DemandOrganizations, households, and individuals need cashto meet
their liquidity needs. In the era of checks andelectronic
transactions, an amount of cash does nothave to be in physical
currency, but may correspondonly to a value in an account that has
been set up forthis purpose. To meet short-term liquidity needs,
cashmust be held in a riskless form, where its value doesnot
fluctuate and is available on demand, but earnslittle or no
interest. Treasury bills and checkingaccounts are considered
riskless. Cash not needed tomeet short-term liquidity needs can be
invested inrisky assets whereby it may earn higher returns, butits
value may be subject to significant fluctuations anduncertainty,
and could become wholly or partiallyunrecoverable. Depending on the
type of risky asset,its value may or may not be quickly recoverable
andrealizable at a modest cost (as with a public equitythat is
listed in a major stock exchange). Determiningthe value of certain
types of risky assets (e.g., privateequity, real estate, some hedge
funds, and asset-backed fixed income securities) may require
special-ized valuation services, which could involvesignificant
time and cost. Risky assets can also besubject to default, in which
case all or part of the valuebecomes permanently unrecoverable.
Researchers have produced over the last few decadesa significant
body of work by applying the principles ofinventory theory to cash
management. We review thecash management literature from its
beginnings sowe can put later work in context, and we have
notidentified an earlier comprehensive review that accom-plishes
this purpose. Whistler (1967) discussed astochastic inventory model
for rented equipment thatwas formulated as a dynamic program; this
workserved as a model for the cash management problem.One of the
early works produced an elegant result thatbecame known as the
Baumol–Tobin economic modelof the transactions demand for money,
independentlydeveloped by Baumol (1952) and Tobin (1956).The model
assumes a deterministic, constant rate ofdemand for cash. It
calculates the optimal ‘‘lot sizes’’ ofthe risky asset to be
converted to cash, or the optimalnumbers of such conversions, in
the presence of trans-action and interest costs. Tobin’s version
requires aninteger number of transactions and therefore
approxi-mates reality more closely than Baumol’s, which allowsthat
variable to be continuous.
The concept of transactions demand for money,addressed by the
Baumol–Tobin model, is related to,but subtly different from,
precautionary demand forcash that applies to unforeseen
expenditures, oppor-tunities for advantageous purchases, and
uncertaintyin receipts. Whalen (1966) developed a model with
astructure strikingly similar to the Baumol–Tobinmodel, capturing
the stochastic nature of precaution-ary demand for cash. Sprenkle
(1969) and Akerlof andMilbourne (1978) observed that the
Baumol–Tobinmodel tends to under-predict demand for money,partly
because it fails to capture the stochastic natureof precautionary
demand for cash. Sprenkle’s paperelicited a response by Orr (1974),
which in turnprompted a counter-response by Sprenkle (1977).
Robichek et al. (1965) propose a deterministic short-term
financing model that incorporates a great degreeof realistic detail
involved in the financial officer’sdecision-making process, which
they formulate andsolve as a linear program. They include a
discussionon model extensions for solving the financing prob-lem
under uncertainty. Sethi and Thompson (1970)proposed models based
on mathematical control the-ory, in which demand for cash is
deterministic butdoes vary with time. In an extension of the
Sethi–Thompson model, Bensoussan et al. (2009) allow thedemand for
cash to be satisfied by dividends anduncertain capital gains of the
risky asset, stock.
In what became known as the Miller–Orr modelfor cash management,
Miller and Orr (1966) extendedthe Baumol–Tobin model by assuming
the demand forcash to be stochastic. The cash balance can
fluctuaterandomly between a lower and an upper boundaccording to a
Bernoulli process, and transactions takeplace when it starts moving
out of this range; units ofthe risky asset are converted into cash
at the lowerbound, and bought with the excess cash at the
upperbound. Transaction costs were assumed fixed,i.e., independent
of transaction size. In a critique ofthe Miller–Orr model, Weitzman
(1968) finds it to be‘‘robust,’’ i.e., general results do not
change muchwhen the underlying assumptions are modified.
Eppen and Fama (1968, 1969, 1971) proposed cashbalance models
that are embedded in a Markovianframework. In one of their papers,
Eppen and Fama(1969) presented a stochastic model, formulated as
adynamic program, with transaction costs proportionalto transaction
sizes. Changes in the cash balance canfollow any discrete and
bounded probability distribu-tion. In another one of their papers,
Eppen and Fama(1968) developed a general stochastic model
thatallowed costs to have a fixed as well as a variable com-ponent.
They showed how to find optimal policies forthe infinite-horizon
problem using linear programming.In their third paper, Eppen and
Fama (1971) proposed astochastic model with two risky assets,
namely ‘‘bonds’’
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and ‘‘stock’’; the stock is more risky but has a higherexpected
return. They also discussed using ‘‘bonds’’(the intermediate-risk
asset) as a ‘‘buffer’’ between cashand the more risky asset. Taking
a similar approach,Daellenbach (1971) proposed a stochastic cash
balancemodel using two sources of short-term funds. Girgis(1968)
and Neave (1970) presented models with bothfixed and proportional
costs and examined conditionsfor policies to be optimal under
different assumptions.Hausman and Sanchez-Bell (1975) and Vickson
(1985)developed models for firms facing a compensating-balance
requirement specified as an average balanceover a number of
days.
Continuous-time formulations of the cash manage-ment problem
were based on the works of Antelmanand Savage (1965), and Bather
(1966), who used aWiener process to generate a stochastic demand
intheir inventory problem formulations. Their approachwas extended
to cash management by Vial (1972),whose continuous-time formulation
had fixed andproportional transaction costs, and linear holding
andpenalty costs, and determined the form of the optimalpolicy
(assuming one exists). Constantinides (1976)extended the model by
allowing positive and negativecash balances, determined the
parameters of theoptimal policy, and discussed properties of
theoptimal solution. Constantinides and Richard (1978)formulated a
continuous-time, infinite-horizon,discounted-cost cash management
model with fixedand proportional transaction costs, linear holding
andpenalty costs, and the Wiener process as thedemand-generating
mechanism. They proved that therealways exists an optimal policy
for the cash manage-ment problem, and that this policy is of a
simple form.Smith (1989) developed a continuous-time model witha
stochastic, time-varying interest rate.
5.2. Supply Chain Management of PhysicalCurrencyPhysical cash,
i.e., paper currency and coins, remainsan important component of
the transactions volumeeven in economies that have experienced a
significantgrowth in checks, credit, debit and smart cards,
andelectronic transactions. Advantages of cash includeease of use,
anonymity, and finality; it does notrequire a bank account; it
protects privacy by leavingno transaction records; and it
eliminates the need toreceive statements and pay bills.
Disadvantagesof cash include ease of tax evasion, support of
an‘‘underground’’ economy, risk of loss through theft ordamage,
ability to counterfeit, and unsuitability foronline
transactions.
Central banks provide cash to depository institu-tions, which in
turn circulate it in the economy. Thereare studies on paper
currency circulation in variouscountries. For example, Fase (1981)
and Boeschoten
and Fase (1992) present studies by the Dutch centralbank on the
demand for banknotes in the Netherlandsbefore the introduction of
the Euro. Ladany (1997)developed a discrete dynamic programming
model todetermine optimal (minimum cost) ordering policiesfor
banknotes by Israel’s central bank. Massoud (2005)presents a
dynamic cost minimizing note inventorymodel to determine optimal
banknote order size andfrequency for a typical central bank.
The production and distribution of banknotes, andthe required
infrastructure and processes, have alsobeen studied. Fase et al.
(1979) discuss a numericalplanning model for the banknotes
operations at acentral bank, with examples from pre-Euro
Nether-lands. Bauer et al. (2000) develop optimization modelsfor
determining the least-cost configuration of the USFederal Reserve’s
currency processing sites given thetrade-off between economies of
scale in processingand transportation costs. In a study of costs
and econ-omies of scale of the US Federal Reserve’s
currencyoperations, Bohn et al. (2001) find that the FederalReserve
is not a natural monopoly. Opening currencyoperations to market
competition and charging feesand penalties for some services
provided for free bythe Federal Reserve at that time could lead to
moreefficient allocation of resources.
The movement of physical cash among centralbanks, depository
institutions, and the public must bestudied as a closed-loop supply
chain (see, e.g.,Dekker et al. 2004). It involves the recirculation
ofused notes back into the system (reverse logistics),together with
a flow of new notes from the centralbank to the public through
depository institutions(forward logistics). The two movements are
so inter-twined that they cannot be decoupled. Rajamani et
al.(2006) study the cash supply chain structure in theUnited
States, analyze it as a closed-loop supplychain, and describe the
cash flow management systemused by US banks. They also discuss the
new cashrecirculation policies adopted by the Federal Reserveto
discourage banks’ overuse of its cash processingservices, and
encourage increased recirculation at thedepository institution
level. Among the practices to bediscouraged was ‘‘cross shipping,’’
i.e., shipping usedcurrency to the Federal Reserve and ordering it
in thesame denominations in the same week. To compareand contrast
new and old Federal Reserve policies forcurrency recirculation,
Geismar et al. (2007) introducemodels that explain the flow of
currency between theFederal Reserve and banks under both sets of
guide-lines. They present a detailed analysis that providesoptimal
policies for managing the flow of currencybetween banks and the
Federal Reserve, and analyzebanks’ responses to the new guidelines
to help theFederal Reserve understand their implications.Dawande et
al. (2010) examine the conditions that
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can induce depository institutions to respond in so-cially
optimal ways according to the new FederalReserve guidelines.
Mehrotra et al. (2010), which is apaper in this special issue of
Production and OperationsManagement, address the problem of
obtaining effi-cient cash management operating policies
fordepository institutions under the new Federal Re-serve
guidelines. The mixed-integer programmingmodel developed for this
purpose seeks to find‘‘good’’ operating policies, if such exist, to
quantifythe monetary impact on a depository institution op-erating
according to the new guidelines. Anotherobjective was to analyze to
what extent the newguidelines can discourage cross shipping and
stimu-late currency recirculation at the depositoryinstitution
level. Mehrotra et al. (2010a) study pricingand logistics schemes
for services such as fit-sortingand transportation that can be
offered by third-partyproviders as a result of the Federal
Reserve’s newpolicies.
5.3. Other Cash Management Applications inBanking and Securities
BrokerageUS banks are required to keep on reserve a
minimumpercentage (currently 10%) of deposits in client
trans-action accounts (demand deposits and othercheckable deposits)
at the Federal Reserve. Until veryrecently, banks had a strong
incentive to keep fundson reserve at a minimum, because these funds
wereearning no interest. Even after the 2006 Financial Ser-vices
Regulatory Relief Act became law, authorizingpayment of interest on
reserves held at the FederalReserve, banks prefer to have funds
available for theirown use rather than have them locked up on
reserve.Money market deposit accounts (MMDA) with check-ing allow
banks to reduce the amounts on reserve atthe Federal Reserve by
keeping deposits in MMDAaccounts and transferring to a companion
checkingaccount only the amounts needed for transactions.Only up to
six transfers (‘‘sweeps’’) per month areallowed from an MMDA to a
checking account, and aclient’s number and amount of transactions
in thedays remaining in a month is unknown. Therefore, thesize and
timing of the first five sweeps must be care-fully calculated to
avoid a sixth sweep, which willmove the remaining MMDA balance into
the checkingaccount. Banks have been using heuristic algorithmsto
plan the first five sweeps. This specialized inven-tory problem has
been examined by Nair andAnderson (2008), who propose a stochastic
dynamicprogramming model to optimize retail accountsweeps. The
stochastic dynamic programming modeldeveloped by Nair and Hatzakis
(2010) introducescushions added to the minimum sweep amounts.
Itdetermines the optimal cushion sizes to ensurethat sufficient
funds are available in the transaction
account in order to cover potential future transactionsand avoid
the need for a sixth sweep.
The impact of the sequence of transaction postingson account
balances and resultant fees for insufficientfunds, similar to the
cost of stock-outs in inventorymanagement, has been studied by Apte
et al. (2004).They investigate how overdraft fees and
non-sufficient funds (NSF) fees interact in such situations.
Brokerage houses make loans to investors who wantto use
leverage, i.e., to invest funds in excess of theirown capital in
risky assets, and can pledge securitiesthat they own as collateral.
In a simple application ofthis practice, known as margin lending,
the brokerageextends a margin loan to a client of up to the valueof
equity securities held in the client’s portfolio. Theclient can use
the loaned funds to buy more equitysecurities. Calculating the
minimum value required ina client’s account for a margin loan can
become com-plex in accounts holding different types of
securitiesincluding equities, bonds, and derivatives, all
withdifferent margin requirements. The complexity in-creases even
more with the presence of long and shortpositions and various
derivative strategies practicedby clients. Rudd and Schroeder
(1982) presented asimple transportation model formulation for
calculat-ing the minimum margin, which represented animprovement
over the heuristics used in practice. Asignificant body of
subsequent work has been pub-lished on this problem, especially by
Timkovsky andcollaborators, which is more related to portfolio
strat-egies and hedging. We believe that the approach in thepaper
by Fiterman and Timkovsky (2001), which isbased on 0–1 knapsack
formulations, is methodolog-ically the most relevant to mention in
this overview.
6. Waiting Line Management in RetailBanks and in Call
Centers
6.1. Queueing Environments and ModelingAssumptionsIn financial
services, in particular in retail banking,retail brokerage, and
retail asset management (pen-sion funds, etc.), queueing is a
common phenomenonthat has been analyzed thoroughly. Queueing
occursin the branches of retail banks with the tellers beingthe
servers, at banks of ATM machines with themachines being the
servers, and in call centers, wherethe operators and/or the
automated voice responseunits are the servers. These diverse
queueing envi-ronments turn out to be fairly different from
oneanother, in particular with regard to the
followingcharacteristics:
(i) the information that is available to the cus-tomer and the
information that is available tothe service system,
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(ii) the flexibility of the service system with regardto
adjustments in the number of serversdependent on the demand,
(iii) the order of magnitude of the number of servers.
Even though in the academic literature the arrivalprocesses in
queueing systems are usually assumed tobe stationary
(time-homogeneous) Poisson processes,arrival processes in practice
are more appropriatelymodeled as non-homogeneous Poisson processes.
Overthe last couple of decades, some research has been doneon
queues that are subject to non-homogeneous Poissoninputs (see,
e.g., Massey et al. 1996). The more theoret-ical research in
queueing has also focused on variousaspects of customer behavior in
queue, in particularabandonment, balking, and reneging. For
example,Zohar et al. (2002) have modeled the adaptive behav-ior of
impatient customers and Whitt (2006) developedfluid models for
many-server queues with abandon-ment. In all three queueing
environments describedabove, the psychology of the customers in the
queuealso plays a major role. A significant amount of researchhas
been done on this topic, see Larson (1987), Katz et al.(1991), and
Bitran et al. (2008). As it turns out, reducingwait times may not
always be the best approach in allservice encounters. For example,
in restaurants andsalons, longer service time may be perceived as
betterservice. In many cases, customers do not like waiting,but
when it comes their turn to be served, would likethe service to
take longer. In still others, for example, ingrocery checkout
lines, customers want a businesslikepace for both waiting and
service. The latter category,which we may call dispassionate
services, are more com-mon in financial service situations, though
the former,which we may call hedonic services, are also
present—forexample, when a customer visits their mortgage brokeror
insurance agent, they would not like to be rushed. Inthe following
subsections, we consider the variousdifferent queueing environments
in more detail.
6.2. Waiting Lines in Retail Bank Branches and atATMsThe more
traditional queues in financial servicesare those in bank branches
feeding the tellers. Sucha queue is typically a single line with a
number ofservers in parallel. There are clearly no priorities
insuch a queue and the discipline is just first come firstserved.
Such a queueing system is typically modeledas an M/M/s system and
is discussed in many stan-dard queueing texts. One important aspect
of thistype of queueing in a branch is that managementusually can
adjust the number of available tellersfairly easily as a function
of customer demand andtime of day. (This gives rise to many
personnel sched-uling issues that will be discussed in the next
section.)
In the early 1980s retail banks began to make ex-tensive use of
ATMs. The ATMs at a branch of a bankbehave quite differently from
the human tellers. Incontrast to a teller environment, the number
of ATMsat a branch is fixed and cannot be adjusted as afunction of
customer demand. However, the tellerenvironment and the ATM
environment do havesome similarities. In both environments, a
customercan observe the length of the queue and can,
therefore,estimate the amount of time (s)he has to wait. Inneither
the teller environment, nor the ATM environ-ment, can the bank
adopt a priority system that wouldensure that more valuable
customers have a shorterwait. Kolesar (1984) did an early analysis
of a branchwith two ATM machines and collected service timedata as
well as arrival time data. However, it becameclear very quickly
that a bank of ATMs is capable ofcollecting some very specific data
automatically (e.g.,customer service times and machine idle times),
butcannot keep track of certain other data (e.g., queuelengths,
customer waiting times). Larson (1990), there-fore, developed the
so-called queue inference engine,which basically provides a
procedure for estimatingthe expected waiting times of customers,
given theservice times recorded at the ATMs as well as themachine
idle times.
6.3. Waiting Lines in Call CentersSince the late 1980s, banks
have started to investheavily in call center technologies. All
major retailbanks now operate large call centers on a 24/7
basis.Call centers have therefore been the subject of exten-sive
research studies, see the survey papers by Pinedoet al. (2000),
Gans et al. (2003), and Aksin et al. (2007).The queueing system in
a call center is actually quitedifferent from the queueing systems
in a teller envi-ronment or in an ATM environment; there are
anumber of major differences. First, a customer nowhas no direct
information with regard to the queuelength and cannot estimate his
waiting time; he mustrely entirely on the information the service
systemprovides him. On the other hand, the service organi-zation in
a call center has detailed knowledge con-cerning the customers who
are waiting in queue. Theinstitution knows of each customer his or
her identityand how valuable (s)he is to the bank. The bank cannow
put the customers in separate virtual queueswith different priority
levels. This new capability hasmade the application of priority
queueing systemssuddenly very important; well-known results
inqueueing theory have now suddenly become moreapplicable, see
Kleinrock (1976). Second, the callcenters are in another aspect
quite different from theteller and the ATM environments. The
numberof servers in either a teller environment or an
ATMenvironment may typically be, say, at most 20,
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whereas the number of operators in a call center maytypically be
in the hundreds or even in the thousands.In the analysis of call
center queues, it is now possibleto apply limit theorems with
respect to the number ofoperators, see Halfin and Whitt (1981) and
Reed(2009). Third, the banks have detailed informationwith regard
to the skills of each one of its operators ina call center
(language skills, product knowledge,etc.). This enables the bank to
apply skills-based-rout-ing to the calls that are coming in, see
Gans and Zhou(2003) and Mehrotra et al. (2009).
In call centers, typically, an enormous amount ofstatistical
data is available that is collected automat-ically, see Mandelbaum
et al. (2001) and Brown et al.(2005). The data that are collected
automatically aremuch more extensive than the data that are
collectedin an ATM environment. It includes the waiting timesof the
customers, the queue length at each point intime, the proportion of
customers that experience nowait at all, and so on.
Lately, many other aspects of queueing in call cen-ters have
become the subject of research. This specialissue of Production and
Operations Management as wellas another recent special issue
contain several suchpapers. For example, Örmeci and Aksin (2010)
foc