Business Process Services. White Paper. Quantifying the Economic Benefits of Application Scoring for Banks and Financial Institutions

Transcription

1 Business Process Services White Paper Quantifying the Economic Benefits of Application Scoring for Banks and Financial Institutions

2 About the Author Dr.Subarna Roy Manager Subarna is a Subject Matter Expert in the Analytics and Insights group at TCS. She has over seven years of experience developing several solutions across diverse industry verticals focusing on risk and marketing analytics. She holds a doctorate in Economics from IGIDR, India. She has published papers in international journals and holds a patent to her credit.

3 Lending institutions use application scoring to decide whether to grant credit to an applicant, determine the ceiling, and price the loan at the time of origination. This expedites the decision-making process and enhances profitability by measuring risk with increased accuracy. It also helps in meeting the lender's regulatory requirements for capital. However, there is no comprehensive guide to quantifying the economic benefits from efficient application scoring. Therefore, managers remain uncertain about the possible financial and operational impacts of a new scorecard implementation. This paper provides a framework to measure economic benefits that accrue from the implementation of an application scoring model. The framework can also help lending institutions manage risks associated with an already implemented scorecard and formulate mitigating strategies from the perspective of customer acquisition.

4 Contents Introduction 5 Benefits of Application Scoring 5 Quantifying the Benefits of Application Scoring: Proposed Approach 7 Periodic Validation of Benefits to Manage Model Risk 9 Conclusion 10

5 Introduction Credit scoring helps banks and financial institutions in limit-setting and pricing decisions. While every project undertaken in credit scoring is targeted at a specific outcome, in terms of changes in approval rate, loss-rate or cure-rate, etc., there is not much clarity on the revenue impact of the implementation. There is no uniform or consistent approach that exists within the industry to address this issue. The purpose of a framework is to act as a comprehensive guide to measure economic benefits realized from implementation of application scoring models. Standard reporting of benefits of scoring models is in terms of discriminatory power based mainly on statistical measures such as the Gini ratio, ROC index, or Kolmogorov- Smirnov statistic (KS). The proposed approach measures the impact in dollar terms and enables stakeholders to make an informed decision regarding the implementation. The approach can also be used to measure on-going benefits of an already implemented model. The continuous monitoring of economic benefits of the implemented models can also help in model risk management as these models have financial and operational consequences. Benefits of Application Scoring There can be several business objectives for developing an application scorecard. An application scorecard can be developed to reduce the loss rate, while using a score-cut-off to match the existing approval rate. It can also be developed to increase the approval rate, while using a score cut-off that maintains the existing percentage of bad accounts. The dollar benefit calculation would differ based on the objectives set a priori by businesses. The possible benefits from implementing an application scorecard can be broadly categorized as follows: Increase in Profitability Greater Regulatory Compliance Benefits of Application Scoring Reduction in Cost of Manual Assessment Reduction in Loss Figure 1: Key Benefits of Application Scoring 5

6 1. Increased Profitability: Implementing an application scorecard allows institutions to approve more loans without undertaking higher risk. In some cases institutions can increase approval volumes as well as reduce risk, and therefore reduce losses in terms of bad debt by maintaining loss at the same level in absolute terms. This is the common practice in markets where an institution intends to undertake rapid expansion in volumes of credit offered. 2. Reduced Loss: In geographies where the credit market is saturated and there is not much scope for expansion in terms of volume, benefits from application redevelopment can reduce losses by reducing bad-debt. Depressed markets with increasing default-rates can also benefit from redeveloping an application scorecard. 3. Reduced assessment cost: In markets where the credit decision process is not automated and a substantial amount of manual assessment is involved, implementation of an application scorecard can help reduce manual assessment cost. As assessors progressively rely on the score, they expend less time on each application assessment. Therefore, businesses can approve more applications without any substantial increase in assessment costs. For portfolios and markets using first or second generation scorecards, this benefit can be significant as businesses in these segments look for rapid expansion in volumes. 4. Assured regulatory compliance: Lending institutions using the advanced internal rating based (A-IRB) approach need to have internal estimates of probability of default (PD), exposure at default (EAD), and loss given default (LGD) for their assets. These measures are used to derive regulatory and economic capital and also for provisioning requirements. The PD estimate for accounts which are on books for a very short period of time is often derived based on the application score, due to unavailability of sufficient history for developing a fullfledged PD model. 6

7 Quantifying the Benefits of Application Scoring: Proposed Approach In Figure 2, the three benefits cover the financial and operational aspects of development, implementation and management of a scoring model. There is a fourth benefit that is purely regulatory in nature and will not be the focus of this paper. The key components of incremental economic benefit from application scorecards that replace previous underwriting criteria are as given in Figure 2: % Change in Approval Rate Profitability Component 1 % Change in Default Rate Loss Savings Component 2 Incremental Benefit % Change in referral Rate Operational Cost Savings Component 3 Figure 2: Contributions to Incremental Benefits through Application Scoring A powerful scoring model helps identify profitable applications from similar p rofiles of those that have been rejected previously due to Type-1 errors. This can be achieved by appropriate choice of reject inference (RI) techniques. Post RI, an application model development involves building a model that includes defaulters and nondefaulters, as well as inferred defaulters and non-defaulters based on rejected population. The effectiveness of the final scorecard and the actual economic benefit is due to the precision in inference. It is easy to measure certain metrics before and after implementing an application scoring model. The proposed model uses these to quantify the benefits of the implementation and translate them into dollar terms. 7

8 The metrics used are approval rates, loss rates, number of applications and approvals, average limits, referral rates, cost of assessment per application, probability of default, loss given default, and exposure at default databases among others. These can be used to measure the three components in Figure Profitability increase: An effective scorecard allows higher number of application approvals with little or no increase in the loss-rate, thereby improving bottom-line. Profitability is essentially the difference in the number of approvals using the new underwriting criterion as compared to the old one. Profitability = (New Approval Rate - Old Approval Rate) * Number of Applications * Average Limit 2. Loss saving: RI also helps to eliminate loss inducing applications, similar profiles of which were approved by previous underwriting criteria. They are replaced by some of the potentially better performing loan applications, similar to the profiles that were rejected. Therefore, the number of approved applications remains the same; however, the portfolio loss is expected to decrease with the implementation of the new scorecard. As demonstrated by component 2 in Figure 2, estimation of loss saving requires working with the change in default rate. The change in default rate is a combination of probability of default, loss given default, and exposure at default databases. Loss Saving = (New Loss Rate-Old Loss Rate) * Number of Approvals * Loss Given Default * Exposure at Default 3. Operational cost saving: Newly developed application score reduces the number of referrals as it is based on well-defined, pertinent characteristics that enhance reliability of automated decision making. Automated decisions reduce both high-side and low-side overrides, and lead to substantial operational cost saving, especially when first or second generation scorecards are implemented. Component 3 in Figure 2 explains the estimation process of operational cost savings. Operational Cost Saving = (New Referral Rate Old Referral Rate) * Number of Applications * Assessment Cost per Applications Illustrative Example of Realizing Benefits with Effective Model Development Scenario: A bank implements improved application scoring to reduce default rate. Personal loan application scoring models were developed for a bank to formulate more effective acquisition strategies for the bank's personal loan business. [1] Glenlake Publishing, Handbook of Credit Scoring, Elizabeth Mays,

9 A single scorecard for all account holders had been developed earlier. The scorecard was not designed to cater specifically to the 'new to bank' segment. As a result, the approval-rate was particularly low in this segment. The quality of the approved applications was also not of the desired level. The main objective of the model development exercise was to reduce default rate in both the existing and 'new to bank' segments, by providing better risk ranking of customers in each segment. Solutions: Two different models were built for personal loan portfolio; model 'A' for the existing account-holders of the bank and model 'B' for account holders new to the bank. A discrimination of 66% (Gini) was achieved using the newly developed combined models; a 5% improvement over the existing model. The framework described above was used to measure the benefit from using application scorecard. The framework estimated the benefit to be around seven million dollars, of which annual loss savings was around six million and revenue impact was one million dollars approximately. Periodic Validation of Benefits to Manage Model Risk Once a model is implemented, the focus should shift to monitoring and periodic validation of the model. This is 2 required for managing model risk. One of the ways to strengthen the governance process is to measure and report incremental benefits periodically. These reports can complement the regular model validation reports that monitor the Gini, ROC, KS and population stability index (PSI) over time, with additional insights on periodic dollar impact. The impact can be decomposed into financial and operational benefits, and tracked separately. Financial benefits include periodic comparison of loss saving and profitability increase, while operational benefits include periodic comparison of savings in cost due to the implemented scorecard as compared to the development benchmark. These metrics can be used to develop a 'traffic light' highlighting of model risk and help formulate necessary mitigation strategies. The metrics also highlight the area that the mitigation strategies should focus on financial or operational. Figure 3 shows an illustrative monthly monitoring report for operational and financial incremental benefits as compared to a developmental benchmark. While the bank continues to gain financially from the implemented scorecard, it incurs losses from an operational point of view. [2] The Risk Management Association, Elizabeth Mays and Balvinder Sangha, Trends in Model Risk Management, 2012, accessed Jan-25, 2013, 9

10 10% 8% 6% 4% 2% 0% -2% Jan 12 Feb 12 Mar 12 Apr 12 May 12 Jun 12 Jul 12 Aug 12 Sep 12 Oct 12 Nov 12 Dec 12-4% % incremental benefit(financal) % incremental benefit(operational) Figure 3: Illustrative Monthly Monitoring Report Conclusion Application scoring models are widely used by banks and financial institutions for credit assessment of new applicants. However, quite often the quantitative benefits of implementing these models remain unaccounted for leaving stakeholders uncertain about the extent of their impact. Additionally, no systematic approach exists to monitor the incremental benefit of these models before monitor. Thus, during the monitoring phase, it becomes difficult to ascertain exactly when a model needs to be rebuilt or modified. The framework proposed in the paper has been developed primarily to quantify benefits associated with implementation and management of application scoring models. It provides an approach to compute both operational and financial benefits of the models in dollar terms. This ensures that managers can judge the impact easily and with certainty. This can be extended to other areas of credit risk modeling, including behavior and collection scoring. Similar frameworks can be developed to estimate economic benefits from interest rate risk, term-structure risk, and liquidity risk modeling among others. These would essentially provide a risk-manager in a lending institution with an array of tools to manage model risk based on quantitative measures. [2] The Risk Management Association, Elizabeth Mays and Balvinder Sangha, Trends in Model Risk Management, 2012, accessed Jan-25, 2013, 10

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12 About TCS Business Process Services (BPS) Business Process Services (BPS) at TCS is about managing and executing business operations. Our domain expertise helps deliver core business processing across industries, analytics & insights and support processes such as accounting, HR and supply chain management. TCS partners with customers to accelerate co-transformation, and generates business value for customers through delivery excellence, risk management and through innovative models such as Platform BPS which delivers process as a service. With annual BPS revenues of greater than US$ 1.4 billion, TCS is one of the largest BPS providers with 47,500+ employees servicing 225+ customers across the globe. Contact For more information about TCS consulting services, contact bps.connect@tcs.com Subscribe to TCS White Papers TCS.com RSS: Feedburner: About Tata Consultancy Services (TCS) Tata Consultancy Services is an IT services, consulting and business solutions organization that delivers real results to global business, ensuring a level of certainty no other firm can match. TCS offers a consulting-led, integrated portfolio of IT and IT-enabled infrastructure, engineering and TM assurance services. This is delivered through its unique Global Network Delivery Model, recognized as the benchmark of excellence in software development. A part of the Tata Group, India s largest industrial conglomerate, TCS has a global footprint and is listed on the National Stock Exchange and Bombay Stock Exchange in India. For more information, visit us at IT Services Business Solutions Consulting All content / information present here is the exclusive property of Tata Consultancy Services Limited (TCS). The content / information contained here is correct at the time of publishing. No material from here may be copied, modified, reproduced, republished, uploaded, transmitted, posted or distributed in any form without prior written permission from TCS. Unauthorized use of the content / information appearing here may violate copyright, trademark and other applicable laws, and could result in criminal or civil penalties. Copyright 2014 Tata Consultancy Services Limited TCS Design Services I M I 01 I 14