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EFL Case Study: Leveraging EFL to Increase Efficiency in MSME Lending

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EFL Case Study: Leveraging EFL to Increase Efficiency in MSME Lending BTPN Indonesia Executive Overview BTPN, Indonesia s 4 th largest MSME lender i, engaged EFL to better leverage credit data analytics
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EFL Case Study: Leveraging EFL to Increase Efficiency in MSME Lending BTPN Indonesia Executive Overview BTPN, Indonesia s 4 th largest MSME lender i, engaged EFL to better leverage credit data analytics and streamline its credit screening processes. EFL worked with BTPN to redesign its loan origination process around the EFL scoring tool, enabling faster decision-making and better resource allocation. This process redesign resulted in a turnaround time reduction of 79%. Encouraged by pilot results, BTPN decided to roll out the EFL-enabled process across its 600+ branch network. Reducing BTPN s reliance on time-intensive and subjective methods of credit evaluation enabled better resource allocation through earlier filtering of high risk applicants. Figure 1 below demonstrates the impact that implementing EFL s credit scoring technology in the early stages of credit review had on BTPN s average end-to-end turnaround time. Figure 1 Impact on EFL Scorecard on End-to-End Turnaround Time EFL s technology allowed BTPN to reduce turnaround time by 79%, from 6.3 days to 1.3 days on average, enabling significant cost savings and projected 20% increase in projected micro portfolio revenues. Partner Overview A Changing Indonesian Market The fourth most populous nation on earth, Indonesia has one of the world s largest and fastestgrowing markets for financial services at the base of the pyramid. As is the case throughout many emerging markets however, Indonesian lenders have traditionally focused on the upper income segment of the Indonesian population, where the risks and costs of servicing clients are proportionally lower. As a result, the lower and middle classes, which are primarily informally or self-employed, have historically been underserved by Indonesia s financial institutions. Indeed, in the poorest 40% of the population, less than 7% of Indonesians access loans from formal financial institutions each year. By contrast, nearly 45% of that same low-income population takes loans from friends and family. ii This disparity suggests that there is a tremendous demand for working capital in Indonesia s burgeoning middle and lower classes, but todate that demand has gone largely unmet. In recent years, however, access to finance has begun to expand. A growing need for financial services in the lower and middle income segments has pushed many financial institutions to shift their strategies and look for new models to serve the bottom of the economic pyramid. T Y P E S O F B O R R O W I N G I N I N D O N E S I A B O T T O M 4 0 % O F I N C O M E ( % A G E ) 93.6% 6.4% Borrowed L O A N F R O M A F I N A N C I A L I N S T I T U T I O N Did Not Borrow 56.2% 43.8% L O A N F R O M F A M I L Y O R F R I E N D S Figure 2 Types of Borrowing in Indonesia The Importance of Turnaround Time in Mass Market Lending Transitioning from servicing relatively few, high net-worth clients to a wider base of lowerticket sized clients presents a tremendous opportunity for lenders, but it also presents major challenges in terms of servicing this down-market population efficiently. More specifically, the pressure for fast turnaround times (TAT) in the micro segment is a central challenge for emerging market lenders because smaller loans are proportionally more costly to service than larger loans. As a report from the Center for Global Development suggests A $100 loan does not cost ten times less to administer than a $1,000 loan. So the poorer the borrower, and the smaller the loan, the higher the cost per dollar lent. These disproportionately high operating costs and long turnaround times stem from two key drivers: Time-Intensive Evaluations: Without automated tools to evaluate the risk of loan applicants, institutions lending to the micro segment are forced to rely on time intensive processes like interviews with neighbors and family to determine who is safe to lend to. Poor Segmentation and Resource Allocation: Because many lenders targeting micro clients rely on just a few lengthy components of credit evaluation, they are unable to screen out particularly risky clients early in the process. This means they incur the full costs of evaluation for all applicants, even those they decline, leading to poor resource allocation and lost time. For the lenders working to capture the enormous opportunity of lending at the base of the pyramid, finding ways to reduce loan TAT is not only key to the bottom line, it is essential to attracting and maintaining high potential borrowers in a saturated marketplace. A Growing Mass-Market Lender Founded in 1958, BTPN has for most of its history focused on providing financial services and loans to pensioners. Following a 2008 acquisition by private equity firm Texas Pacific Group (TPG), BTPN identified a significant growth opportunity in the mass-market, focusing on micro, small and medium enterprises (MSMEs) with loans between USD $400 and $17,000. Lending primarily to the informal businesses that make up nearly two-thirds of the country s labor force iii, BTPN quadrupled the size of its MSME portfolio between 2009 and 2011 to 4.6 trillion rupiah (USD 500 million). iv Today, the bank serves more than 150,000 MSMEs across a network of more than 600 branches, and MSME loans make up nearly 25% of the bank s total lending. v Figure 3 BTPN's Branch Network across Indonesia As BTPN has expanded, it has sought innovative ways of reducing turnaround time by leveraging automated, analytically rigorous tools. Furthermore, with lending operations across a sprawling branch network and nearly 8,000 staff members, the bank has looked for ways to ensure consistency in its credit approval processes. Project Overview BTPN engaged EFL in September of 2012 to help drive better credit analytics and streamline its credit review processes. EFL began by layering its traditional psychometric scoring model with an application scorecard that was built using BTPN s extensive existing borrower database. By integrating psychometrics and BTPN s application scorecard model, EFL was able to capture predictive capabilities beyond those of psychometrics or application data alone. vi More specifically, EFL was able to segment borrowers with a 10x difference in default, enabling better risk assessment and mitigation in BTPN s MSME portfolio. In addition to using the EFL s credit scoring tool to better understand and mitigate the risk of default, BTPN sought to leverage EFL s analytics to reduce its loan disbursement TAT. Dissecting a Mass Market Lender s Credit Review Process EFL and BTPN began by thoroughly analysing every step of the credit review process at the branch level. Like many lenders, BTPN s process comprised three major phases: screening, verification and approval. Together, these phases contributed to a TAT of more than 6 days. Sales and initial screening 3 days: Sales agents collect basic client information and ensure that the client s business meets basic minimum requirements defined by BTPN. Actual time with client: 1 hour (approx.) Principle bottleneck: Availability of Credit Officer to conduct follow-up visit. Verification 2 days: Credit officers verify clients monthly turnover/expenses, capacity to pay, and collateral value. This requires more expertise than the sales visit. Actual time with client: hours (approx.) Principle bottleneck: Backlog of clients limits time to begin approval processing. Approval Processing 1.3 days: Bank staff make a final approval decision, set the terms that will be offered to the client, and disburse the loan. Actual time spent: minutes (approx.) Principle bottleneck: Setting of pricing and terms due to back-and-forth required between Credit Officer and more senior Credit Risk Staff. Figure 4 BTPN's Original Loan Approval Process Leveraging EFL to Streamline Operations EFL and BTPN piloted two separate approaches to increasing efficiency with the EFL tool. Approach 1: Administer EFL Application during the Verification Phase In this approach, the EFL application is administered on a tablet by the credit officer during the verification phase, following an un-changed screening process in which a sales officer collects basic information and checks that applicant meets the bank s minimum criteria. The EFL score is used to inform loan decisions, and set pricing and terms in the final approval phase. Figure 5 Approach 1: Administer EFL Application during Verification Phase Approach 2: Administer EFL Application during the Screening Phase In this approach, the EFL application is administered on a tablet upon first point of contact with the prospective client during the screening phase. Upon completing the application, sales officers receive an EFL decision segmenting the applicant as either high, medium, low, or reject. Those that fall into the latter segment are declined without further review. For those that fall into the three former segments, the sales officer calculates loan terms and pricing through an additional application hosted on the tablet, enabling on-the-spot conditional loan approval. During the subsequent verification phase, the credit officer confirms the information provided by the applicant during the screening phase and submits the application for disbursement. Figure 6 Approach 2: Administering the EFL Application during the Screening Phase Results BTPN piloted both of these approaches in select branches in order to evaluate their impact on turnaround time and overall efficiency. Results of Approach 1: Administer EFL Application during the Verification Phase Introducing the EFL application in the verification phase enabled a significant reduction in the time required during the final approval phase. By using EFL s automated scoring system to set terms and pricing, as opposed to relying on the personal judgment of Credit Risk Staff, BTPN brought down the time of the approval phase from 1.3 days to 0.3 days, a reduction of 77%. This reduction in approval time yielded a 16% reduction in total turnaround time for approved clients, from 6.3 days to 5.3 days. Figure 7 Impact of Approach 1 on Turnaround Time Results of Approach 2: Administer EFL Application during the Screening Phase Introducing the EFL application in the screening phase fundamentally changed each of BTPN s three phases of credit review. Screening phase: By allowing sales officers to make conditional approvals without relying solely on time intensive and subjective evaluation processes, EFL enabled an 83% reduction in screening time, from 3 days to just half a day. Verification phase: Because fewer applicants passed through the screening phase, credit officers were able to manage their time more judiciously among fewer applicants in the verification phase, and rely less on time-intensive methods of verification, enabling a 77% reduction in verification time from 2 days to just half a day. Approval phase: Finally, as was the case in Approach 1, EFL s enhanced credit scoring model allowed BTPN to make faster and more informed decisions in the approval phase, maintaining the same 77% reduction in approval time, from 1.3 days to 0.3 days. Together, these changes translated to a 79% reduction in total turnaround time, from 6.3 days to 1.3 days. Figure 8 Impact of Approach 2 on Turnaround Time The EFL psychometric credit scoring tool drove major gains in efficiency and significant reductions in turnaround time by allowing BTPN to rely less on time-intensive, subjective methods of evaluation and by enabling better resource allocation through the early screening of high risk applicants. Furthermore, the introduction of the EFL system has ensured greater operational consistency across pilot branches, a significant gain for a rapidly growing financial institution with an expansive branch network. Moving Forward Encouraged by the early results, BTPN has chosen to roll out Approach 2 as outlined above, enabling faster turnaround times and greater operational consistency across its branch network. Further, the impementation of the EFL scoring model has reduced BTPN s need for back-office administrative staff, allowing the bank to reallocate resources toward more clientfacing, revenue generating roles. In the next year, this change will enable a 20% increase in sales officers, as well as a projected 20% increase in BTPN s annual micro portfolio revenues. i - based on number of branches serving MSMEs ii iii iv v BTPN Annual Report vi For more information on EFL s work with credit scoring at BTPN, please read EFL s Case Study: Enhancing Application Scorecards with Psychometrics.
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