EFL Case Study: Controlling Risk in a Microfinance Graduation

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1 JFS customer population Probablitiy of default EFL Case Study: Controlling Risk in a Microfinance Graduation Janalakshmi Financial Services India Executive Overview Janalakshmi Financial Services (JFS), a Top 1 Indian MFI i, engaged EFL in April 213 to control risk and expand individual lending. JFS administered more than 7, EFL surveys and disbursed more than 4 loans in the first year of the EFL engagement. In conjunction with JFS traditional credit underwriting tools, EFL demonstrated the ability to accurately measure credit risk among clients. JFS extended the EFL partnership with a projected 1x increase in EFL application volumes through 216. Exhibit A below demonstrates that EFL s psychometric credit model was able to accurately differentiate credit risk in JFS s Indian borrowing population. Borrowers who received lower scores defaulted more often than those who received higher scores. Specifically, the borrowers in the bottom quartile were nearly 2.5 times more likely to fall behind on their payments as customers in the top quartile. Bad Rate by Score Bucket Quartiles x% 1.6x% 4th 3rd 2nd 2nd 3rd 1st 1st Count of Scores EFL Scores 1.2x% >1 Day Past Due x% 18.% 16.% 14.% 12.% 1.% 8.% 6.% 4.% 2.%.% * Absolute default values have been masked Exhibit A: Risk separation power

2 Partner Overview Understanding the Indian Microfinance Market India s microfinance segment has experienced unparalleled growth in the last decade. The total amount lent to micro enterprises has increased nearly 9% in recent years ii as new players have entered the market and existing players have expanded. Today there are more than 2 registered microfinance institutions with 35 million active borrowers and more than 5 billion dollars in micro-loans across the country, making India the largest microfinance market on earth. iii The enthusiasm for lending to India s working poor is balanced by lessons learned from a microfinance crisis that shook the nation in 21. Ensnared in debt by some MFIs predatory pricing and coercive collections policies, as well as widespread client overborrowing and poorly crafted regulatory efforts, borrowers began defaulting in huge numbers. The events of the last decade have given shape to an Indian microfinance industry that is both highly ambitious and deeply risk averse, by choice and by law. India is among the most saturated microfinance markets on the globe, but for their size and strength, Indian MFIs remain quite conservative: default rates nation-wide are around 1% iv, far lower than most other emerging markets. Figure 1 MFI penetration in India. Graduating group customers to individual loans in information-scarce markets MFIs have practiced group lending methodologies for many years: by disbursing loans to groups and making each group member liable for other group members repayment, MFIs have effectively outsourced the process of credit review onto the borrowers themselves. Knowing that she is responsible for every other group member s loan payments, for example, a borrower will only bring other borrowers into her group that she knows are likely to repay. Many MFIs around the world have been able to scale these simple models, experiencing great success in delivering small loans to a large number of customers in need. More recently, however, microfinance ambitions have begun to shift. As the market for new group facilities begins to dwindle and a middle class in need of larger working capital loans emerges, many of the largest microfinance banks see individual loans as the future of lending in India. The opportunity in individual lending is immense and widely recognized, but it is not without risk. The vast majority of business owners in India lack basic credit criteria that is typically used to assess credit-worthiness: things like borrowing histories, collateral, formal financial records and income statements.

3 Loan Portfolio (millions) Active Borrowers (thousands) Further, as part of the Indian central bank regulations post Andhra Pradesh crisis of 29, the Indian government placed interest rate caps for non-deposit taking MFIs at 26%. And while in many cases this protects borrowers from predatory loan pricing, it also makes it more difficult to serve low income individual borrowers, as the costs associated with credit review and collections in this segment are significantly higher. Because of this, many MFIs have shied away from individual lending, and opted for lower risk and lower return group facilities. An Emerging Leader in Indian Microfinance Established in 23 to serve the needs of India s under-banked, Janalakshmi Financial Services (JFS) has quickly grown into a premier Indian Microfinance Institution. Today, JFS boasts more than 3 million dollars in assets and more than a million active borrowers across 154 branches in 85 cities and 14 states v, making it one of the top 1 largest MFIs in all of India. vi Janalakshmi's Decade of Growth Gross Loan Portfolio (Left Axis) Figure 2 JFS' Decade of Growth. MixMarket.org JFS market-based approach to financial inclusion is defined by three distinct characteristics: 1. Sole focus on servicing the urban poor 2. Tailored customer centric approach to financial products and services design and development 3. Central role of technology and processes for efficiency and scalability JFS has pioneered models to graduate the urban underserved up the ladder into individual lending products, and is one of the only MFIs that has been able to successfully carry out graduation model lending in parallel with its typical group microfinance lending. Like any new lender of individual loans, JFS has faced difficulty in overcoming the challenges of information scarcity to understand a customer s eligibility. JFS individual lending has more than doubled each year for the last three years, but without quantifiable data to measure risk among prospective borrowers JFS has been forced to rely on time and resource intensive measures to extend its individual lending portfolio. This has meant high screening costs and long turnaround times (TAT) for applicants.

4 Project Overview Integrating EFL JFS engaged EFL in April of 213 to determine the feasibility of using EFL s credit scoring methodology to expand individual loan portfolios while controlling risk in the Indian microfinance market. In April 213, JFS began using the EFL application to assess all new and returning customers applying for JFS s individual loan products, JanaOne and EFS. Figure 3: JFS Individual Loan Products JFS integrated EFL into its existing application processes, administering the EFL survey alongside other application materials. For the first year of the engagement, JFS did not use the EFL score for direct loan decision-making, rather it used the score to identify the highest potential candidates that it could fast track through the credit review process and offer preferential terms. By administering the survey to all individual loan applicants and tracking their performance over time, JFS was able to test the predictive power of EFL s scoring technology in the Indian market. Figure 4 Credit Processes for JanaOne and EFS

5 Population Rolling out EFL Over the course of the first year, JFS scaled the EFL tool across 4 branches, training 12 JFS staff members to administer EFL surveys. Figure 5 EFL Rollout Deploying a combination of field testing on Netbooks and branch testing using Desktop computers, JFS administered more than 7, EFL surveys and disbursed more than 4, loans in the first 12 months. EFL Applications and Disbursements in Year One May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Figure 6 EFL Applications and Disbursements

6 JFS customer population Probablitiy of default Results EFL s Predictive Power The quality of any credit-scoring tool lies in its ability to differentiate risk. Those who receive higher scores should perform better, i.e. default less often, than those who score lower. The stronger the credit score, the better it can differentiate between good and bad clients, and the more it can help lenders control risk in their loan portfolios. By tracking the loan performance of JFS applicants who took the EFL survey and received EFS or JanaOne loans, we can evaluate how effective EFL s credit methodology was in measuring risk. In this case, EFL s model performance was measured against applicants who have fallen more than one day past due on their loan repayments. Figure 7 below divides JFS s applicant population into four groups of nearly equal size, or quartiles, based on applicants EFL scores. Those who scored in the bottom 4 th of the population are grouped into the column on the far left, and vice versa. The height of each column indicates the size of the total borrowing population. Borrowers that have fallen more than one day past due are characterized as bad. The bad rate is the ratio of these borrowers in arrears to the total borrowing population in that quartile, and is illustrated by the downward sloping red line. Bad Rate by Score Bucket Quartiles x% 1.6x% 1.2x% x% 4th 3rd 2nd 2nd3rd 1st 1st EFL Scores 18.% 16.% 14.% 12.% 1.% 8.% 6.% 4.% 2.%.% Count of Scores >1 Day Past Due * Absolute default values have been masked Figure 7 Bad Rate by Score Bucket

7 JFS customer population Probability of default Figure 7 demonstrates that EFL s psychometric credit model was able to accurately differentiate credit risk in JFS s Indian borrowing population. Borrowers who received lower scores defaulted more often than those who received higher scores. Specifically, the borrowers in the bottom quartile were nearly 2.5 times more likely to fall behind on their payments as customers in the top quartile. Figure 7 also illustrates the potential to control risk within a borrowing population. By screening out more applicants, a lender can reduce default rates in the resulting loan portfolio. Had JFS chosen to reject the bottom quartile, for example, it would have removed a subset of the borrowers that fell behind on payments 5% more often than the rest of the population. Figure 8 further conveys the opportunity for portfolio segmentation by showing cumulative portfolio performance. Now, rather than seeing each quartile independently, we see them stacked as a running total. The column on the far right shows just the first quartile, the column to its left shows the first and second quartiles, and so on. Similarly, the red line now shows the percentage of bad borrowers to the cumulative populations Cumulative Bad Rate by Score Bucket Cumulative Quartiles 1.5x% 1.28x% 12% 1% x% x% 8% 25 6% 15 4% 5 2% -5 >4th >3rd >2nd 1st EFL scores Count of Scores > 1 Day Past Due % * Absolute default values have been masked Figure 8 Cumulative Bad Rate by Quartile Had JFS chosen to lend only to the top 75%, using the EFL score as a pre-filter to drop the lowest quartile of applicants, it would have seen a nearly 25% reduction in late repayment rates across its portfolio. As the bank chooses to lend to a bigger population it deliberately chooses to take on higher levels of risk, enabling greater value captured through risk-based pricing and loan size adjustments. By increasing the interest rate or reducing the size of loans offered between the 3 rd and 4th quartiles, for example, a lender could mitigate the increased risk of the borrowing population.

8 # of EFL tested applicants Moving Forward Over the course of the one year engagement, EFL s credit scoring methodology proved the ability to accurately differentiate credit risk among JFS individual borrower population. JFS extended the engagement to include an additional two years. EFL Survey Volume Projections in Year 2 vs. Survey Volumes in Year Y1 Cumulative tests Y2 Cumulative tests JFS has ambitious expansion plans for both EFS and JanaOne products over the next year and will maintain the EFL testing mandate for all new applicants. As a result, applicant test volumes are projected to grow four fold in year two of the partnership. JFS also seeks to apply the EFL score to directly inform loan decision-making for its individual loan products. Doing so will allow JFS to grow lending and improve control over risk in the Indian market, as well as increase operational efficiency by cutting down on the costly and time intensive components of the existing credit application process. JFS may also start using EFL scores to lend to new, thin-file Janalakshmi applicants, reducing minimum criteria to expand access to finance and bring new borrowers into the formal financial sector. By increasing efficiency and reducing risk within JFS s individual lending portfolio, the partnership between EFL and JFS will dramatically improve JFS ability to lend to a large portfolio of individual micro-borrowers. Most importantly, the partnership has the potential to change the way that Indian MFIs approach individual lending, a segment that to-date has remained largely out of reach. i Mixmarket.org ii iii iv v vi