An innovative way to assess client credit-worthiness with EFL
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This document describes EFL, a company that uses psychometric testing and big data to assess creditworthiness for lending to individuals and small businesses. EFL administers a 20-45 minute online test that measures variables like ability, willingness, ethics and business skills. It uses the results to generate a credit score that lenders can use to make more informed lending decisions. Partner institutions that have used EFL's scores have seen significant impacts - increasing lending by 176% while maintaining default rates, and decreasing defaults by 46% while maintaining acceptance rates. The document argues that EFL's approach provides a more objective, scalable and cost-effective alternative to traditional credit assessment methods.
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An innovative way to assess client credit-worthiness with EFL
- 1. EFL: An innovative way to assess client credit-worthiness
- 2. Section 1 Introduction to EFL
- 3. 2 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
- 4. 3 Our global footprint +$275 million disbursed | 125,000 assessments | 28 languages | 27 countries
- 5. Section 2 Understanding the need
- 6. 5 MSMEs – the drivers of economies
- 7. 6 Traditional loan decision making Lack of information
- 8. 7 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.
- 9. 8 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
- 10. 9 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 2 What 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
- 11. 10 The emerging market lending opportunity 2.2 Billion People $2.5 Trillion 90% of Unbanked in Emerging Markets Development crisis & big business opportunity
- 12. Section 3 EFL product: The solution
- 13. ~30x 12 EFL results Nigeria Indonesia 6% 4% 2% 30% 20% 10% $275 Million disbursed Peru Pakistan 20% 10% 0% Q1 Default rate Q4 ~9x Q2 Q3 40% 20% 0% Default rate EFL quartiles Q3 ~11x Q1 Q2 Q4 0% Default rate Q2 Q3 Q4 Q1 0% Q2 Q3 Default rate ~4x Q1 Q4 125,000 applications 27 countries Worst 25% 26-50% 51-75% Best 25% Q1 Q2 Q3 Q4 + Risk - + Risk -
- 14. 13 Measuring the individual Ability Willingness Ethics & Honesty Attitudes & Beliefs Business Skills Fluid Intelligence Systematize & validate LO’s intuition
- 15. 14 The EFL application Ethics & Honesty What percentage of people are likely to steal? 5% 20% 50% Agree Disagree Business skills Which of the following you should take into account when calculating your costs? Attitudes & Beliefs A big part of success is luck Fluid intelligence Remember this number for 5 seconds: 823460 Risk Inventory Rent Correct Incorrect Motivation Through metadata points (time, answer sequence, etc.) Developed based on pre-employment screening tools
- 16. 15 Description of the process Partner institution applies the electronic survey 1 ~30 minutes PC, Tablet or smartphone With or without internet Remote or in-site
- 17. 16 Description of the process EFL 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
- 18. 17 Applications of the EFL Score No File Clients Current Approvals Thin File Clients Existing Customers
- 19. Section 4 EFL results & impact
- 20. 19 EFL arranges applicants by risk – willingness to pay ID more good prospects, reduces exposure on bad ones 0.6% 7.8% 450 400 350 300 250 200 150 100 50 0 8% 7% 6% 5% 4% 3% 2% 1% 0% # clients 0.7% 350-399 4.0% 250-299 1.4% EFL score 6.6% <200 200-249 300-349 >400 Default rate 13x Default rate Good Bad 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
- 21. 20 Typical partner results India +176% Without EFL With EFL Loans Default rate Increase lending Increase lending by ~180% while maintaining target default rates -46% With EFL Without EFL Loans Default rate Reduce default Peru Decrease default by ~50% while maintaining acceptance rate
- 22. 21 Using innovation to serve the next generation of entrepreneurs
- 23. 22 To download the presentation, use the following QR code luis.sanchez@eflglobal.com
Editor's Notes
- Hi, my name is Luis Sanchez, and I am the head of the Entrepreneurial Finance Lab for Mexico and Centroamerica
- Go through each point: Credit scoring company uses psychometric data Create quantitative understading of individual risk & opportunity in small business & consumer financings 20-45 minute survey 20 top banks, MFIS, credit bureaus
- Where are we: We started as research project in Harvard, we spun out of the university in 2010 and became a private company, yet hold or research there In Mexico we work with: Bancomer, Te Creemos, Credito Real Our partners have disbursed almost 300 M USD by using our tool, 125k tests, presence in 27 countries
- Introduce you to your clients Meet some our partners’ customers –entrepreneurs in Mexico, Kenya and Indonesia that have more in common than it seems. Successful businesspeople for over a decade Upstanding members of their community Have borrowed money to grow –from informal money lenders and family They are shut outside of the formal financial system
- Why? On one word: lack of information Financial institutions across the word use the same pieces of information to gauge willingness and ability to pay: Formal financial statements Credit history, credit bureau reports and borrowing history Collateral Official identification. The problem is, in emerging markets, many of these ingredients are difficult, if not impossible, to possess – making lending into this segment seem risky, and impossible. In addition to the lack of information, traditional methods used to evaluate bigger and more formal entrepreneurs are ill suited for this market
- Criteria currently used to evaluate this segment, prevents many entrepreneurs from getting a loan yet this criteria does not correlate with a lower risk If you see at these graphs, from one of our partners… By asking this question This criteria is particularly hard on disadvantage groups
- But not only the traditional scoring method leaves many entrepreneurs out, also the traditional operational model that relies heavily on LO needs revamping Three key areas for improvement: -Subjective -Time consuming -Costly
- So just how big, this opportunity is? Mckinsey & WorldBank put the estimate at 2.2 B people, & or a financing gap of 2.5 T 90% of these unbanked in emergin markets Development crisis 3/4th GDP as high as 90% of employment in these markets Big business opportunity We just needed to find the solution for the lack of information and ineffective operational model
- At EFL, we’ve helped Financial Institutions solve this problem over 125,000 times, across 27 countries, with over 200 million USD having been lent to MSMEs who would have otherwise not qualified for a bank application. Here we have four examples of our work in Nigeria, Indonesia, Peru, Pakistan
- How have we done this? By measuring something everyone has: an individual’s willingness and ability to repay a loan. For ability we measure things like their business skills not whether or not they have a PhD, but do they possess the basic skills and understandings to make sound business decisions – the difference between sales and profits, inventory management, do they know what their competition is doing Second, fluid intelligence, which is not education level, but the same analysis that goes into an IQ assessment – memory, numeracy, and the ability to quickly process information. For willingness to repay, a big driver of loan losses is willing default and fraud. Ethics and honesty are key in predicting someone’s behavior – if they can repay a loan, will they? These criteria has always played a large role in intuitive banking – are they driven? Optimistic? We systematize & validate Los intution
- Let’s show a quick example of some of the items in the application. Go through them No clear right or wrong answers The answers contribute to an understanding of the individual Pre employment
- Process is very simple 30 min PC, tablet, smarthphone With or without internet Remote or in site
- Get the answers Compare them to our database of 150k & the performance of those loans Get a 3 digit score
- “no file” customers little or no borrowing history, or requirements which satisfy the bank’s lending requirements. For “thin-file” clients Short of the prerequisites For current approvals Extra measure of confidence in the applicant Reduce NPLs by screening out the most risky applicants Assign more favorable terms to the best customers. Even with existing customers Improve portfolio performance Retain their best customers Better price for risk
- What are the results Arrange applicants by risk ID good prospects Reduce exposure on bads Explain graph X axis EFL score Larger EFL score, lower default rate Population distributes similar to a bell curve Dramatically differentiate risk Best score defaulted 13x less often than worst Whole portfolio had a default rate of 3% With EFL we split that 3% even further Some 8% and some 0.6%
- How does this translate to partners Typical partner results Increase lending 180%, yet mantaine default rates Reduce defaul by 50% yet maintaine volume Tool can be adjusted to whichever your need is
- With the help of EFL, banks have been able to look beyond information and infrastructure limitations in emerging markets, and entrepreneurs have been able to effectively demonstrate their creditworthiness. To give credit where credit’s due. An example of the use of innovation to serve the next generation of entrepreneurs