Behavioural Scoring and Retail Segmentation

Case Study

Information Management Behavioural Scoring and Retail Segmentation

Basel II Solution Implementation The Basel II Accord

The Basel II Accord aims to make international financial systems more stable and to put in Place incentives for banks to adopt better risk management practices.

The information technology and systems changes implied by the Basel II Accord are the Largest and most expensive technology challenges faced by any financial service institution

Background The client is one of the leading banks in Asia Pacific region, and provides a wide range of financial services including personal financial services, private banking, trust services, commercial and corporate banking, corporate finance, capital market activities, treasury services, futures broking, asset management, venture capital management, general insurance and life assurance.

As part of their preparation for Basel II compliance, a data mart for Behavioural Scoring and Retail Segmentation was required. This data mart would store data for multiple countries. Saksoft built the Segmentation and Behavioural Scoring data mart for Singapore, Malaysia and Thailand.

Requirement As per the IRB approach of the Basel II accord, banks are required to identify separately three sub-classes of exposures within the retail asset class category:

 Exposures secured by residential property  Qualifying revolving retail exposures  All other retail exposures

After segmentation of the portfolio into the three distinct asset classes, banks must further segment them into homogeneous risk pools and estimate the corresponding loss characteristics (Probability of Default – PD, Exposure at Default- EAD and Loss Given Default – LGD) of each risk pool. The loss characteristic of each retail

pool will then be used as inputs for the Risk Weighted Assets (RWA) calculations for the retail portfolio and ultimately for the bank.

Saksoft analysed the Country specific Business Requirements and identified the data elements that were to be extracted from the Global Data Warehouse for Scoring and Segmentation. All business exception conditions were identified at the product category level to ensure that all accounts were segmented into homogeneous risk pools.

Behaviour scores predict the risk or profit of existing customers by employing statistical techniques to assess their transactions patterns to portray objective evaluation. Thus behaviour scores serve as a useful attribute for predicting risk. For the Singapore business of the customer, more than 500 variables for unsecured products were derived in the data mart (primarily delinquency, spend and payment related variables), which served as the source criteria for determining the behavioural scores. An interface was built with an external credit-scoring engine that would calculate the behaviour scores, which would be sent back to the data mart. Behaviour scores is an important driver for segmentation of products for all countries.

Segmentation was done at various levels starting from the product level. For example: the credit cards portfolio was segmented by Vintage, Race, Behaviour Scores, Delinquency attributes and Utilization rates. Secured Overdrafts were segmented by vintage and collateral type. A set of financial products associated with a facility was grouped together as a “Financial Package” and segmented on multiple drivers like account conduct, account vintage and financial packages secured by rented property, commercial property etc.

Risk drivers for PD, EAD and LGD were identified. PD is defined as a bank’s own internally generated estimate of the probability (expressed in decimals) that a particular obligor will default on its obligations to the bank over a given time period (usually one year). This was calculated for each segment (risk pool) as Number of defaults observed within the year/ Number of accounts in the reference pool. Data measures required for determining the probability of default included Max number of days OD in Excess over last 12 months; Number of outward cheques returned over last 12 months; Average utilization rate on credit cards over last 12 months; Average Sales coverage (funded working capital) over last 12 months; debt serviceability etc.

LGD was calculated for each risk pool as Final Loss/ Loss outstanding at default, this is the estimated amount of loss that a bank would suffer if the obligor of a particular asset were to default on its obligations to the bank as a proportion of the overall amount of the bank’s exposure to the obligor for that asset.

The Exposure at Default (EAD) is the estimated amount of facility outstanding (including potential additional draw down for committed facilities) at the point of default. This is the estimated amount of a bank’s gross exposure to an obligor in respect of a particular asset, should he default. EAD _EFF was calculated at the risk pool level as Current Outstanding + CCF (Credit Conversion Factor)* Undrawn Amount.

Expected loss % was calculated as EL / EAD_EFF; where Expected Loss (EL) represents the cost of doing business, and is the amount of credit loss that the Bank expects to experience over a chosen time horizon.

The SBS data mart, while having a common design structure, also included country specific business requirements based on country processes and local legal and regulatory requirements.

For example, For Malaysia, for credit cards, the retail segmentation process was executed on a daily basis, with outstanding balances and utilization being computed on a daily basis. This is different from Singapore and Thailand, where utilization was determined on a cyclical and on a month end frequency.

For Thailand, specific changes included calculation of the NPL status (Non-Performing Loan) status at the customer level, as opposed to NPL status at the account level. Also Parent Child limits were defined at the customer level and individual exposures were calculated based on the Parent-child hierarchy.

The Solution Saksoft built reports for Credit Scoring and Basel 2 segmentation metrics for Singapore, Malaysia and Thailand. Credit Scoring reports compared the statistics of development sample for credit scoring with the actual population, Population Stability Index and KS statistic related reports, and delinquency analysis vis-a–vis credit scoring etc. Another report displayed the Scorecard of various applications based on the Final Decision.

Reports on population segmentation include Retail IRB Analysis for NPL and Non NPL accounts for multiple products, Population Distribution by Segmentation for multiple products, and tracking key metrics like Expected Loss, Expected Loss %, Risk Weighted Assets for multiple products over a period of time. Other reports include a Pool Migration Report that tracked product performance from one delinquency pool to the next, to NPL categorisation for multiple products, based on account vintages. There was also Back Testing Report, which tracked the last 12-month performance of the Ever-NPL accounts.

A common universe was built for a single set of reports. To achieve better maintenance and improved performance of the universe, it was designed in such-a-way that all the common objects required for various reports were placed in a single class.

The Technology The technology environment is as follows:

 GDW: DB400 V5R2 – Development is on client running in Windows platform, connectivity through DB2 Connect.  Informatica Powercenter v6.1 on RS6000 (AIX) – Development on Windows platform clients.  Oracle v9.2 on AIX Unix Server as temporary working area  Data mart modeling using ER-Win 4.0  Connect: Direct for file exchange between different servers  Control-M scheduler  Reports on Business Objects 6.5