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

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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

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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.

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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.

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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

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OngoingValidation

ManagementReporting

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DecisionSimulation

DecisionExecution

ScoringServices

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Development&

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Deployment&

Verification

ModelData Mart

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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.

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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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