7/27/2019 Gmid Associates- SME Loan Portfolio Review

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Case Study | Gmid Associates

www.thegmid.com

enquiries@thegmid.com | +91 020 4239596

Secured/ Unsecured SME Loan Portfolio: Asset

Valuation, Stress Testing and Lifetime Loss Forecast

Objective

Our client, one of Indias premier financial services conglomerates, had a growing and

profitable SME loan portfolio (both secured and unsecured). After three years o

cautious growth since inception- putting policies, procedures and best practices in

place; the management decided they had a solid platform to go for a steep growth

curve. With this context, they wanted an end to end review of portfolio credit quality

with the following objectives-

Credit quality review with respect to future default behavior and

recommendations for quality improvement.

Portfolio Stress Test Simulator to preempt the effect that macroeconomic stress

scenarios would have The stress scenarios were-

1. GDP Growth Slowdown,

2. Tougher Access to Finance.

Lifetime loss Prediction of the existing portfolio.

Methodology

Merged all the business datasets- application data (profile, demographics, financia

history of business and promoters etc.), transactional details (payment/ delinquency/

cheque bounce details), product information and other relevant information sources

(top-up/ cross-sell etc.) to compile a single view dataset so that each account had a

single unique record with all the information related to it.

CASE STUDY

7/27/2019 Gmid Associates- SME Loan Portfolio Review

2/2

Secured/ Unsecured SME Loan Portfolio: Asset Valuation, Stress Tesing and Loss forecasts

Case Study | Gmid Associates

www.thegmid.com| +91 0120 4239596

Conducted data mining analyses (clustering, segmentation, KDA etc.) to identify trends

and patterns in the loss making accounts, if any. Identified pockets of portfolio that

were significantly riskier than others.

Based on historical default data, developed a behavioral scorecard for default prediction

on active portfolio. Developed a survival model to predict lifetime at account levelCombined these two models for the lifetime loss forecast.

Basis various portfolio performance analyses, it was found that monthly portfolio

performance had a great correlation to account vintage and some macroeconomic

parameters. Devised a statistical relationship to correlate portfolio performance

(nonpayment behavior) with macroeconomic changes and built this into an easy to use

parameterized Macro enabled MS Excel tool. The tool was to be used by the CEOs

office and they wanted an easy to use and parameterized tool.

Shared a detailed report regarding portfolio leakages, distress trends and

recommendations, Life Time Loss Forecast results along with stress testing tool.

Impact

Management used the inputs to tighten leakages in credit policy, identify areas (i.e

regions/ industries) and parameter cut-offs which were the source of risky acquisitions

This helped the company to solidify the credit foundation and then go for aggressive

expansion while making sure portfolio quality doesnt go down.

The company was able to increase the monthly portfolio growth rates by 50% and at thesame time reduce the default rates (which were already pretty good) by over 10%.