The Test of Entrepreneurship -...

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The Test of Entrepreneurship REVOLUTIONIZING MSME FINANCE


What we do

EFL provides knowledge for financial institutions

about individuals

using psychometrics

enabling them to expand portfolios

and improve control over risk


The emerging market lending opportunity1

1. Map from McKinsey Insights & Publications: Counting the world’s unbanked

Creating a $2.5 Trillion Financing Gap for MSMES

2.2 Billion People around the world do not have access to formal financial services


EFL Results

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NIGERIA

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CENTRAL AMERICA

75,000 applications

$250 million

>20 countries


Psychometric credit screening

Developed based on:

40 years of academic research on entrepreneurs

Pre-employment screening tools used successfully by over a third of US companies

Psychometric testing “can lower default rates by 25–40%” and “without any banker supervision, the cost of the assessment is 45% of traditional assessment measures.”

McKinsey & Company Lowers Defaults Measures Credit Risk

“Psychometric evaluations … measure credit risk without depending on formal financial accounts, business plans, or collateral.”

The World Bank

“Traditional banking models fall short in serving SMEs effectively and profitably … bank’s sales and service models, which are optimized for larger clients, are often uneconomical when applied to SMEs.”

Increases Profitability


EFL’s Historical Footprint

Historical Partner

EFL Offices

+$250 million disbursed | 75,000 assessments | 28 languages | 26 countries


Traditional Loan Decision-making

Income Statements

Collateral Borrowing History

Formal Financial Records


Analysis of a borrower

Attitudes & Beliefs

Ethics & Honesty

Fluid Intelligence

Business Skills

Willingness Ability


EFL’s software


Partner applications

Thin File Clients Existing Customers

Current Approvals No File Clients

The EFL Score can be used as a stand-alone or supplementary tool for a variety of different borrower profiles


3 Case Studies

1. Large, low risk microfinance in India

2. Credit bureau partnership in Peru

3. New product and market entry in Kenya


EFL Case Study: Indian MFI


India Case Study: Project Overview • Leading Indian Microfinance Bank engaged EFL’s credit scoring

methodology to control risk in low-information borrowing population.

• EFL survey administered alongside existing application materials to determine predictive power in pilot phase.

• Partner bank administered more than 6,000 EFL surveys and disbursed more than 3,000 loans, allowing EFL to track and evaluate the performance of the EFL tool in the Indian microfinance segment.


India Case Study: Results

Borrowers who scored in the bottom 25% were 24x more likely to default* than borrowers

who scored in the top 25%

*default defined as 30+ DPD in pilot phase

1.71%

1.21%

0.36% 0.07% 0.0%0.2%0.4%0.6%0.8%1.0%1.2%1.4%1.6%1.8%

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EFL scores


EFL Case Study: Peruvian Credit Bureau


Peru Case Study: Results

What the Credit Scoring Firm Saw Without EFL



What the Credit Scoring Firm Saw With EFL



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52%

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EFX EFX + EFL

Acceptance Rate Default Rate

Using EFL, Scoring Firm could Increase Lending by 160% while maintaining target default



Using EFL, Scoring Firm could Reduce Defaults by 50% while maintaining acceptance rate

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EFX EFX + EFL

Acceptance Rate Default Rate


EFL Case Study: Kenyan Commercial Bank


Africa Results

35,000+ tests

18,000+ loans

Modelled Countries

EFL has calibrated models in all major African countries, allowing banks to make informed and profitable decisions of who to accept and on what terms.

NO OTHER EXISTING TOOLS/MODELS CAN AS ACCURATELY ASSESS & HELP ACCESS THE ENORMOUS & UNTAPPED SME MARKET IN AFRICA.


Africa Case Study: Project Overview

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Defaults and Volume by Score Bucket

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Borrowing Population as a whole was 4.5x more likely to default than the top two

score buckets

• Stanbic Kenya administered 10,000 EFL surveys, disbursing more than 4,000 loans and $120m to SMEs over the course of 2 years.


Africa Case Study: Results

All buckets based on loans at month 12 and include only fully matured loans. Loans displayed were disbursed between May 2011 and July 2012.

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Kenya 01 Jun-Dec 2011

Value Disbursed Default Rate

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Value Disbursed Default Rate

Improving Credit Models allowed Stanbic to improve portfolio performance over time


EFL in Key African Markets

Kenya Start: January-2011

Total Loans Disbursed:

4,658 221m ZAR

Ghana Start: June-2011


4,634 456m ZAR

Zambia Start: January-2012


944 116m ZAR

Def

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Rat

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* Bad90 ever at 12 months

Model Performance Model Performance Model Performance

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EFL Risk Buckets EFL Risk Buckets EFL Risk Buckets


What is the Opportunity?

Lessons Learned

Lessons Learned with Standard Bank

• The biggest risk in terms of fraud or gaming

is staff, not clients

• The test is not a silver bullet and needs to be couched in robust bank processes (collections, verifications)

• This is a HUGE market

• Models take a long time to customize / calibrate to specific countries and it costs money to do so

• Staff flags and other EFL controls are now

built and tested to manage these risks

• While not a cure-all, EFL can add significant quantifiable value to a portfolio

• A conservative approach can still yield outstanding results

• We now have accurate working models and better ability to judge entrepreneurs “out of the gate” in Kenya

• EFL can offer more than just scores • EFL has built up dashboards, identity

verification support and GIS platforms that assist in risk management

The Test of Entrepreneurship -...

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