The Test of Entrepreneurship -...
Transcript of The Test of Entrepreneurship -...
1 | © 2013 EFL Global Ltd. All Rights Reserved
The Test of Entrepreneurship REVOLUTIONIZING MSME FINANCE
2 | © 2013 EFL Global Ltd. All Rights Reserved
What we do
EFL provides knowledge for financial institutions
about individuals
using psychometrics
enabling them to expand portfolios
and improve control over risk
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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
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EFL Results
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LATIN AMERICA
CENTRAL AMERICA
75,000 applications
$250 million
>20 countries
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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
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EFL’s Historical Footprint
Historical Partner
EFL Offices
+$250 million disbursed | 75,000 assessments | 28 languages | 26 countries
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Traditional Loan Decision-making
Income Statements
Collateral Borrowing History
Formal Financial Records
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Analysis of a borrower
Attitudes & Beliefs
Ethics & Honesty
Fluid Intelligence
Business Skills
Willingness Ability
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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
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3 Case Studies
1. Large, low risk microfinance in India
2. Credit bureau partnership in Peru
3. New product and market entry in Kenya
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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.
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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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Peru Case Study: Results
What the Credit Scoring Firm Saw Without EFL
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Peru Case Study: Results
What the Credit Scoring Firm Saw With EFL
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Peru Case Study: Results
20%
52%
8% 9%
EFX EFX + EFL
Acceptance Rate Default Rate
Using EFL, Scoring Firm could Increase Lending by 160% while maintaining target default
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Peru Case Study: Results
Using EFL, Scoring Firm could Reduce Defaults by 50% while maintaining acceptance rate
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EFX EFX + EFL
Acceptance Rate Default Rate
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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.
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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.
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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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Improving Credit Models allowed Stanbic to improve portfolio performance over time
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EFL in Key African Markets
Kenya Start: January-2011
Total Loans Disbursed:
4,658 221m ZAR
Ghana Start: June-2011
Total Loans Disbursed:
4,634 456m ZAR
Zambia Start: January-2012
Total Loans Disbursed:
944 116m ZAR
Def
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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
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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