EFL -- Innovative Ways to Assess Client Credit-Worthiness

EFL: An innovative way to assess client credit-worthiness

Introduction to EFLSection 1

3

Introduction

Who we are

Credit scoring company that uses psychometric variables & big data

Create a deep quantitative understanding of individual risk and opportunity in small business (MSME) and consumer financing

20 to 45 minute flexibly-administered software which supplements a bank’s existing loan application

We help over 20 top banks, retailers, and credit bureaus to measure individuals, create scorecards, and introduce technology into their lending process

What we do

Our product

Our work

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Our global footprint

+$275 million disbursed | 125,000 assessments | 28 languages | 27 countries

Understanding the needSection 2

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MSMEs – the drivers of economies

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Traditional loan decision making

Lack of information

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Traditional sociodemographic evaluation not adequate for most microcredit customers…

Particularly hard on disadvantaged groups: young, elderly, or people with willingness to pay yet bad record due to sickness, etc.

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In addition, current microfinance operational model with key areas for improvement

1 Subjective

• EOM “better” candidates or “more” candidates• Before lunch, fewer approvals• Give two LO same profile, different decision• No feedback loop

2 Time consuming

• Can take a couple of days to create a file• Several visits• Can’t be administered remotely

3 Costly

• High operating cost (training, turnover)• Hard to scale• Subject to corruption & bribery

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Not having the appropriate scoring model nor adequate operational model has led MFIs to wrong conclusions

Wrong answer Correct answer

1 Who is your competition?

Other MFIs Non-consumption

2What is your biggest concern?

Over-indebtedness of clients

Get new clients that nobody’s attending

MFIs are fighting other MFIs for the same clients, when there is an abundance of them in the market

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The emerging market lending opportunity

2.2 Billion People $2.5 Trillion

90% of Unbanked in Emerging

Markets

Development crisis & big business opportunity

EFL product: The solutionSection 3

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EFL results

Nigeria Indonesia

PakistanPeru

0%

10%

20%

Q1

Def

ault

rate

Q4

~9x

Q2 Q3

0%

20%

40%

Def

ault

rate

Q3

~11x

Q1 Q4Q2

0%

2%

4%

6%

Def

ault

rate

Q2 Q4Q3

~30x

Q1

0%

10%

20%

30%

Q3Q2

Def

ault

rate

~4x

Q4Q1

125,000 applications

27 countries

$275 Million disbursed

Worst 25%

26-50%

51-75%

Best 25%

Q1

Q2

Q3

Q4

EFL quartiles

RiskRisk+ - + -

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Attitudes & Beliefs

Ethics & Honesty

Fluid Intelligence

Business Skills

WillingnessAbility

Measuring the individual

Systematize & validate LO’s intuition

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The EFL application

Ethics & Honesty

What percentage of people are likely to steal?

Business skills

Which of the following you should take into account when

calculating your costs?

Attitudes & Beliefs

A big part of success is luck

Remember this number for 5 seconds: 823460

Fluid intelligence

5% DisagreeAgree

IncorrectCorrectRentInventory

Risk

20% 50%

Through metadata points (time, answer sequence, etc.)

Motivation

Developed based on pre-employment screening tools

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1

~30 minutes PC, Tablet or smartphone

With or without internet

Description of the processPartner institution applies the electronic survey

Remote or in-site

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Description of the processEFL analyzes the answers and generates a 3-digit credit score

Credit score algorithm developed based on a database of ~150,000

surveys yet customized for region/clients

3-digit credit score

2

Repayment data uploaded monthly, models are constantly customized and improved

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Applications of the EFL Score

Thin File Clients Existing Customers

Current Approvals No File Clients

EFL results & impactSection 4

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0.6%

7.8%

0

50

100

150

200

250

300

350

400

450

0%

1%

2%

3%

4%

5%

6%

7%

8%

# cl

ient

s

350-399

0.7%

250-299

EFL score

4.0%

6.6%

<200 >400300-349200-249

1.4%

Default rate

13x

Default rate

Bad

Good

Default rate and volume per EFL score

• Dramatically differentiate risk between high and low scoring borrowers

• Best score bucket defaulted 13x less often than worst score bucket

Portfolio default rate

EFL arranges applicants by risk – willingness to payID more good prospects, reduces exposure on bad ones

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Typical partner results

India+176%

With EFLWithout EFL

Default rate

Loans

Increase lending

Increase lending by ~180% while maintaining target default rates

With EFL

-46%

Without EFL

Default rate

Loans

Reduce default

Decrease default by ~50% while maintaining acceptance rate

Peru

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Using innovation to serve the next generation of entrepreneurs

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To download the presentation, use the following QR code

luis.sanchez@eflglobal.com