Solving the Catch-22 in Small Business Credit
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solving the Catch-22 in small Business CreditUse analytics and decision automation to balance risk, cost and compliance for more profitable originations
Expanding credit to small and medium-size enterprises (SMEs) is becoming a global priority. From
China to Spain, United Arab Emirates to the US, governments are recognizing the importance of
SMEs as an engine of employment and economic growth. They’re actively promoting—through
mandates, meetings and financial assistance—expansion in the availability of credit to SMEs.
None of these efforts are likely to achieve sustainable success, however, unless creditors can solve
the “Catch-22” problem in SME credit: Loan/lease amounts are often too small for creditors to earn
enough to justify a lengthy, costly originations process. Creditors must still make careful decisions,
since smaller businesses may be riskier than larger companies. And creditors must comply with
regulations for treating applicants fairly and accurately estimating capital risk reserves, which apply
to smaller amounts of financing too.
This paper discusses how decision automation and analytics (such as risk scoring tailored to smaller
businesses) solve this fundamental profitability problem of SME credit markets. We answer:
• How can you reduce originations time and cost—without giving up
anything in risk management and regulatory compliance?
• How can different types of creditors, operating under very different market
conditions, best leverage these technologies?
• Can SME credit markets maintain their traditionally relationship-based
nature in the age of speed, ease and globalization?
Along the way, we share experiences of FICO clients around the world using
automation and analytics for fast, smart SME originations.
Number 77
Learn how a leasing company accelerated decisions from 24 hours to right now for 80% of applicants
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Solving the Catch-22 in Small Business Credit
» insights
Across the globe, governments are trying different ways to increase the flow of credit to smaller
businesses, widely viewed as engines of economic recovery and development.
In the UK, the Bank of England has shifted the focus of its funding for lending scheme (FLS) from
the mortgage market to SME lending. The Chinese government has issued a mandate for banks
to increase SME lending while ensuring that they remain Basel compliant. Germany is providing
funding to help EU neighbors Spain and Ireland combat unemployment and spur growth by
giving SMEs in those countries access to low-interest loans. The United Arab Emirates is creating
a national registry of business assets that will streamline SME loan collateralization. The US Small
Business Administration is working to increase funding sources by expanding its lender network
to include more non-banks, including nonprofits, community development financial institutions
and even venture funds.
These are examples of the high-level commitments being made to increase credit access for
smaller businesses. But these efforts are aimed at more than creating a momentary boost in
credit availability. The larger objective is to ensure that the engine of economic growth doesn’t
sputter and stall. SME credit markets need to work well enough to provide a sustained, reliable
flow of credit for fueling smaller business job generation, entrepreneurship and innovation.
Prospects for achieving this larger objective, however, depend as much on improving creditor
processes as on government commitment. Creditors will be able to expand access to SME credit
in a sustainable way only if they can reduce the time and cost involved in current originations
processes, which can’t be justified for smaller amounts of credit.
Analytics and decision automation enable creditors to shrink originations time and cost
down to a level proportionate to smaller credit amounts—without giving up anything in
risk management and regulatory compliance. In fact, creditors can implement accelerated
originations processes that actually improve their performance in both of these dimensions.
Accelerated SME originations processes are, of course, advantageous to borrowers as well.
Currently in some Asian markets, for instance, the length of time it takes to get answers to
credit applications is a huge problem for business owners. Entrepreneurial opportunities
emerge quickly and require action—often far too quickly for the slowly turning gears of bank
originations. Even in cases where the SME has an existing relationship with the bank, a decision
can take weeks, as decision processes are rarely set up to facilitate analysis of historical data,
especially across accounts.
» Meeting Demands for Expanded SME Access to Credit
May 2014
SustainableSME Growth
Not TemporaryFixes
Figure 1: Aims of government activism
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Solving the Catch-22 in Small Business Credit
» insights » insights
SMEs are experiencing similar delays and frustrations
in markets where credit processes have been largely
automated for decades. Many US banks, for instance, reacted
to the financial downturn by discontinuing automated
decisioning, and stopping or reducing their reliance on
small business risk scores. Qualifying for credit got tougher
for all businesses but, according to a study by the Federal
Reserve Bank of Cleveland, “while banks have loosened credit
standards for big businesses during the recent economic
recovery, they have maintained tight standards for small
companies.” (See Figure 3.)
As a result, an increasing number of SME owners find
themselves in a situation similar to that of an entrepreneur
who told his story to The Wall Street Journal.1 This owner of a
dozen nail salons in the Philadelphia area wanted to open
another location. Knowing his credit score had dropped
during the downturn, he dreaded going through weeks of
aggravation with his bank that might end in a rejection. He
opted instead for an online short-term lender, which wired
the money to him within a few days. Opening the salon soon
afterward, he was able to repay the principal and nearly 15%
interest over the six-month loan term.
Bank retrenchment has opened the door to alternative
sources of SME credit, including peer-to-peer lending
networks and online/mobile finance companies that
extend credit for a flat fee or a share of receivables. It’s also
encouraged more aggressive competition from leasing
companies and manufacturer captive leasing programs.
Recognizing this, some banks are gradually reinvigorating
their SME activity. But this time, they are using a wider range
of data and analytics, while following best practices that
improve risk and compliance controls.
Even their non-bank competitors are under pressure to
improve originations processes. Alternative creditors face
a tougher market as banks come out of retrenchment.
They need more analytic sophistication in order to make
sharper risk assessments that enable them to lower prices.
Analytic insight is helping them to make better originations
decisions as well as providing the risk transparency required
in refinancing SME portfolios, thereby reducing the cost of
funding.
0% 20% 40% 60% 80% 100%
Updating systems to complywith new regulations
Increasing capital to complywith new regulations
Growing current/depositaccount base
Growing profitabilityof existing customers
Improving risk managementprocesses and systems
Reducingfraud losses
Improving the customerexperience
Increasingmortgage lending
Increasing lending tosmall businesses
Increasing lending toconsumers
Figure 2: SME lending is a growing priority
What do you see as the top risk priorities for your bank in 2014?
Top priorityPriorityNot a priority
Source: European Credit Risk Outlook, January 2014, FICO & EFMA
While many factors affect the business strategies and priorities of financial institutions, government activism may be having some impact. In Europe, a recent survey of credit risk managers showed that increasing lending to small businesses was a priority or top priority for 76% of respondents. A similar survey of North American risk managers put that figure at 72%.
1 “ Alternative Lenders Peddle Pricey Commercial Loans,” The Wall Street Journal, January 2014
PERC
ENT
–60
–40
–20
0
20
40
60
80
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Figure 3: Tight lending standards persist in the US for SMEsNet tightening of lending standards (% of banks tightening lending standards minus % loosening them)
Large Business LoansSmall Business Loans
Source: Federal Reserve Bank of Cleveland
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Solving the Catch-22 in Small Business Credit
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Given the diversity of creditor types and market conditions, paths to greater success in SME markets
vary. Here are some considerations, along with examples of successful steps FICO clients are taking.
Financial institutions currently relying on manual originations processes
To make smart decisions faster, financial institutions using manual originations need to begin
introducing decision automation and analytics into their processes. In most cases, a good first
step for automating processes is a business rules management system (BRMS). A good first step in
analytics is an originations score tailored to smaller enterprises.
Even community banks and branches—where officers may know local businesses well enough
to make quick, astute credit decisions manually—can benefit from automated processes and
scoring. These practices can help financial institutions address regulatory compliance concerns—by
improving decision transparency and reporting consistency—and spend less time and money
proving it. They help answer questions like:
• Is a new small business applicant being treated fairly compared to an
existing customer with similar characteristics?
• Are credit decisions being made in a consistent, objective manner by all
officers across all branches?
• Are they adhering to risk mitigation practices that ensure the safety and
soundness of the institution?
Using a BRMS, creditors improve the speed, efficiency and consistency of
their decision processes by implementing originations policies and best
practices in the form of business rules. These business rules can power
automated processes, but they can also improve manual processes by
guiding credit staff. They can orchestrate processes with an appropriate mix
of automated and manual steps given factors such as credit amount and
applicant risk level.
With the ability to quickly modify business rules, creditors adjust processes
as regulations, credit products and economic conditions change. The BRMS
runtime decision engine also captures operational data and outcomes for
test-and-learn cycles, while creating an audit trail that helps demonstrate
regulatory compliance.
Originations scores provide the means to rank-order credit applicants by risk
of serious delinquency. With this empirical method of separating applicants,
financial institutions can apply different treatments fairly and consistently.
They can also apply decision automation selectively. By writing business
rules that set score and policy thresholds, they tell decision engines to
automatically process applications above or below thresholds, and send
others into queues for monitoring or manual adjudication by underwriters.
A Pacific Rim bank, for instance, is using automation to eliminate manual
review of the approximately 30% of low-scoring (high-risk) applicants.
Bankers also spend less time assessing high-scoring (low-risk) applications,
enabling them to focus their knowledge and experience where it is most
needed, on SME applicants in the middle “gray area.”
» Taking the Next Step to Greater Success
A Chinese bank responds to the government mandate on SME lending
Chinese banks are under a mandate to increase sME lending
while remaining Basel compliant. in response, one bank
worked with FiCO to eliminate some traditional obstacles
to sME lending—lack of data, slow and costly underwriting
processes, and higher risk exposure. Custom sME application
scoring and collection models now enable the bank to
automate and accelerate loan decisions, while improving risk
management.
since available data for this sector is not as complete or reliable
as for other areas of retail lending, model development relied
heavily on FiCO innovation and expertise. the analytic team
also followed international risk management best practices.
Models incorporate Basel risk parameters and assign loans into
different capital pools based on risk levels.
www.fico.com page 5
Solving the Catch-22 in Small Business Credit
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The US Small Business Administration finds this kind of scoring so efficient that it has
implemented score-based pre-screening in some of its programs. The agency is using the
FICO® Small Business Scoring ServiceSM (SBSSSM) Solution. A minimum acceptable score, set
by the SBA, is required for 7(a) program loans in amounts up to and including $350,000 to be
eligible for federal guarantees.
Small business originations scores are output by application risk models that can blend data
from multiple sources, including information about the business and the business principal(s).
Creditors choose which data sources to use based not only on availability of the data, but on
cost and regulatory considerations as well.
For example, a financial institution extending small amounts of credit may decide to reduce
data costs by scoring only on the business principal’s consumer credit bureau information
(which can capture as much as 80% of the potential risk of a small business). Another institution
extending larger amounts of credit may value the additional predictive lift from incorporating
application characteristics, business repository characteristics (where available) and business
financial information into the score.
This flexibility enables originations scoring to adapt to market differences. In many markets,
business bureau SME credit performance data is not available for incorporation into scores. In
some markets, business financials may be difficult to verify and, therefore, should be weighted
in the score as relatively less important than other characteristics. Data availability also affects
which type of model can be used for scoring. Figure 4 summarizes the choices.
Figure 4: Different types of models and their advantages
Generic Models Pooled Models Expert Models Custom Models
$ $$ $$$ $$$$
Days to install Weeks to install Weeks to install Months to install
no additional data needed no additional data needed no data at start, data needed later needs lots of data now
independent validation independent validation Ongoing model maintenance Ongoing model maintenance
good performance Very good performance Better performance Best performance
Available from credit bureaus in some markets. they’re built from a combination of consumer and business trade line performance data covering many types of credit products.
Off-the-shelf models built from “pools” of business and business owner profiles, and performance data. in some markets, creditors can opt for highly specific pooled models for each business type (e.g., term loan, with segmentation for criteria such as size of business, geographic location and financial product).
Built by expert model developers based on their experience with similar types of models in similar markets. in some markets, ready-made sME expert models are available; in others, they need to be developed. these models are a good starting place when data is not sufficient initially for custom model development. Many creditors, for example, don’t have large enough sME portfolios to include enough “bad” (seriously delinquent) accounts for predictive modeling.
Built for a particular portfolio from the creditor’s own applicant and performance data. they provide the highest predictive power.
www.fico.com page 6
Solving the Catch-22 in Small Business Credit
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Financial institutions that pulled back from automation and scoring
Companies that constricted SME credit in the economic crisis need to re-embrace automation and
scoring if they’re going to regain market share. Savvy institutions, however, are also increasing risk
controls by implementing model management best practices, and adding new data sources and
analytics to obtain a more complete picture of risk exposure and reward potential.
One reason for the pull-back from automated SME credit decisions during the crisis is that financial
institutions not conducting regular model validations saw score predictive accuracy deteriorate.
By properly monitoring score performance, they could have adjusted score thresholds and updated
models in alignment with changing economic conditions, thereby avoiding or mitigating losses.
Since the crisis, lack of such controls has also become an increasing concern of regulators in
many markets.
Today, centralized model management (Figure 5) helps creditors maintain the accuracy of risk
predictions by automatically tracking the performance of all deployed models. Essential capabilities
include automatic initiation of model validations according
to schedules, or when triggered by a decline in performance
or stability metrics. Automated management fully documents
the ensuing validation processes, including actions taken in
response to findings. It also creates an audit trail, making it
easier for creditors to demonstrate regulatory compliance.
As financial institutions reinstate automation and analytics
for SME originations, they are also taking steps to improve
understanding of customer relationships. Knowing the
customer means knowing total exposure and profit potential
across all business and consumer accounts. FICO is helping
a North American bank, known for holistic customer
management in its consumer operations, to extend its
originations system across SME operations. A single view and
coordinated approach will enable the bank to make better
decisions, while providing more personalized and consistent
service.
Some financial institutions are increasing insights into SME
credit risk and potential reward by analyzing data from a
wider range of sources. They’re tapping commercial databases
on small business performance and, following the lead of
alternative creditors, using unstructured data analytics to
mine social media for insights into how SMEs are regarded
by their customers.
But the lure of more data can also work against the goal
of bringing originations costs in line with SME credit
amounts. FICO is helping a North American leasing and asset
management company make judicious choices by analyzing
which data sources provide the most predictive value for each
of their SME segments.
Figure 5: Centralized model management improves analytic performance and regulatory compliance
Companies are increasingly turning to solutions like FICO® Model Central™ Solution to centralize and improve model management. Model Central provides three tiers of capabilities across the entire model lifecycle: in analytics development, deployment and management.
ModelData Mart
Tracking
Monitoring
OngoingValidation
ManagementReporting
Alerts
DecisionSimulation
DecisionExecution
ScoringServices
DecisionOptimization
Development&
Calibration
Deployment&
Verification
ModelData Mart
ADVANCED
P
RO
FESSIONAL
DEC
IS
IONING
DEV
ELOPMENT
FO
UN
DA
TION
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Solving the Catch-22 in Small Business Credit
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Alternative creditors that need to become more competitive
Alternative SME creditors face a tougher market than before. Banks are coming out of retrenchment,
and other companies that have traditionally financed commercial loans and leases are expanding
their focus to include smaller businesses.
But alternative creditors aren’t sitting still. By supplementing small business scores with additional
analytics—extracting insights, for example, from unstructured data in blogs, product reviews and
tweets—they can increase segmentation of applicant populations and reduce prices for lower-risk
SMEs. By adopting advanced methods of designing, testing and optimizing the decision strategies
driving their automated origination processes, they can respond faster to competitive threats. They
can also learn faster about what works and doesn’t work in changing markets.
A North American online creditor providing small business financing and working
capital is a good example. FICO is helping this company upgrade its originations
system. The system will include capabilities that have long been strengths of credit
decisioning, such as running champion-challenger test cycles (in-market contests
between an existing decision strategy and proposed improvements). Analytic learning
loops will accelerate these improvement cycles and drive performance gains.
The company will be able to quickly assess operational data to understand the
strategy impact on key performance indicators for each customer segment and, just
as quickly, modify strategies to generate the next challenger. Other priorities include
analyzing both structured and unstructured data from online marketplaces and
social media, and incorporating the resulting insights into mathematically optimized
decision strategies. The company will be able to make more precise decisions on
incoming applications. It will also have the means to proactively identify existing
customers, as well as some previously declined applicants, as being eligible for funds.
Lessors that want to improve the profitability of point-of-sale decisions
Some leasing companies have increased their activity in smaller business financing since the
economic crisis. This expansion has been prompted partly by the slowdown in equipment
purchasing by large companies during the economic crisis and partly by the opportunity created
by bank retrenchment. At the same time, some equipment manufacturers are expanding their own
captive leasing programs to focus more on smaller businesses.
All of these companies understand that decision automation and scoring are the keys to providing
outstanding point-of-sale service, as well as to making these transactions the beginning of longer-
term, increasingly valuable relationships.
A North American leasing company, for instance, captured a large national account by
demonstrating it could accurately make automatic instant decisions for 80% of SME equipment
purchases. (Previously most decisions had required up to 24 hours.) The originations process uses
the FICO® SBSSSM score and considers additional business factors (industry, time in business, number
of employees, etc.) to make the decision automatically or direct the application to an underwriter.
Accurate analytic segmentation of applicant populations enables point-of-sale financing offers that
will be profitable and likely to be accepted by the customer. It also improves credit risk transparency
for securitization, facilitating sale of the debt on secondary markets.
www.fico.com page 8
Solving the Catch-22 in Small Business Credit
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A North American equipment manufacturer is expanding its captive leasing program for SMEs. The
company is motivated by the prospect of leases adding a couple of points to its bottom line, as well
as by the opportunity to resell assets after expiration of lease terms. It also sees a major opportunity
to use analytics to learn about SME customers in ways that will enable it to offer additional products,
financing and affinity programs at just the right time for each business. FICO is helping the company
implement an analytics-intensive automated originations system for building personalized, highly
profitable long-term relationships—and to do it across at least 35 countries.
We finished the last section by talking about an equipment manufacturer that aims to automate
SME leasing across at least 35 countries. That vision is about as far as one could get from a
community financial institution or local branch where small business owners sit down to talk with a
lending officer who understands their business.
Yet an essential piece of this company’s strategy is to leverage equipment leases to open doors
to wider, ongoing relationship-based sales and services. The better the company understands
the SME customer’s business, the more it will see opportunities to build relationship value. There’s
nothing new about that dynamic—what’s new is using data analysis to get to that deep business
understanding.
This lessor knows that many SME owners still prefer working with companies that
understand their business and with whom they can establish long-term relationships.
Yet they also want speed and ease in these interactions. Using decision automation and
scoring, the leasing company intends to give its SME customers both.
Indeed, FICO expects that, with the exception of some community financial institutions,
most SME creditors will have to combine speed and ease with personalized, business-
knowledgeable, relationship-based service.
Many creditors are moving in that direction. A case in point is an Asian bank that
has an aggressive program of global expansion, particularly in the US, one of its top
targeted markets. Improving not only decision accuracy and speed, but also local
market knowledge will be critical to its success. The company needs a solution that
includes scoring and other analytics, as well as an analytic learning loop for capturing
operational decisions and outcomes. In this way, the bank can quickly understand the
impact of its credit policies on different kinds of SMEs in particular regions.
While helping companies like these act like local institutions, we are also helping
them manage the global scope of their operations with consistency and efficiency.
Geographic reach, of course, brings complexity to decision processes that must
accommodate disparate data sources, regulations, and SME customs and behaviors.
In addition, while creditors moving into new markets can take advantage of pooled
models or ready-made expert models where available, creditors need to build new
expert models where they are not. Nevertheless, it’s possible to calibrate all models so
that a common set of scoring metrics can be reported, understood and compared.
» Relationship Lending and Leasing in the Age of Speed, Ease and Globalization
Can a large global company be “the bank next door”?
One international banking group’s Us super-
regional subsidiary thinks so. the bank’s local
business development officers run sME risk scores
and look at additional data from laptops in their
cars before making calls. As a result, they don’t
waste time pursuing new accounts that won’t
be approved, and when they walk into a small
business, they’re able to talk knowledgeably about
it with the owner.
Solving the Catch-22 in Small Business Credit
» insights
The Insights white paper series provides briefings on research findings, technology innovations and recommended best practices
from FICO. To subscribe, go to www.fico.com/insights.
FICO, Small Business Scoring Service, SBSS, Model Central and “Make every decision count” are trademarks or registered trademarks of Fair Isaac Corporation in the United States and in other countries. Other product and com-pany names herein may be trademarks of their respective owners. © 2014 Fair Isaac Corporation. All rights reserved.3097WP 05/14 PDF
All over the world, governments are taking steps to drive economic growth by helping make
more credit available to small and medium-size businesses. For these efforts to work, however,
most creditors need to adopt or expand their use of decision automation, scoring and other
analytics. These processes can remove impediments to SME credit expansion by enabling
creditors to make careful, compliant originations decisions quickly and at very low cost. They’re
the means of offering smaller businesses speed, ease and relationship-based service across
virtually any geographic footprint.
To learn more about the latest in analytics advances and best practices for lending, visit the FICO
Banking Analytics Blog or download these FICO white papers:
• Satisfying Customers and Regulators: Five Imperatives (No. 75)
• Cloud Democratizes Access to Big Data Analytics (No. 74)
• A New Perspective on Small Business Growth with Scoring (Jan. 2013)
» Conclusion: A New Era in SME Credit
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