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G Georgakopoulos Efma Consumer Credit Conference


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identify and create value through data analytics across the credit cycle in consumer credit. Presentation at EFMA consumer credit conference by george georgakopoulos

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G Georgakopoulos Efma Consumer Credit Conference

  1. 1. Data Analytics across the Credit CycleCase study EFMA – Consumer Credit ConferenceGeorge GeorgakopoulosExecutive Vice President – BancpostPresident of the BOD – EFG Retail June 6th 2012
  2. 2. Introduction and Summary The financial environment is challenging across Eastern Europe. In Romania, we have seen lower capital inflows, lower consumer confidence and higher delinquency over the last 3 years In such an environment, the consumer credit providers can use data analytics, to identify value creation strategies EFG Group in Romania has been using data analytics across the entire cycle of consumer lending, from targeting to underwriting, in customer service till collections & recoveries Credit providers can develop their your own models/strategy; there is though great opportunity to use external tools and data, mapped on their strategies Key issue for success is top management buy-in; the key task of leadership in a consumer credit provider is to create a culture where data analytics are embedded into the process of the firm Extensive usage at EFG Group Romania has given our consumer credit operation a commercial advantage, doubled net spreads since 2008, reduced roll rates and increased recoveries. 2
  3. 3. RomaniaA Challenging environment in Consumer Credit 3
  4. 4. Capital Inflows A large current account deficit in the run-up to the crisis was financed by FDI and inflows to thefinancial sector. Since the crisis, the inflows would have Romani Capital Inflows to collapsed, had it not been for the IMF Sept 2008 25 25 22 22 20 20 17 17 15 15 12 12 10 10 7 7 5 5 2 2 -1 -1 -3 -3 -6 -6 -8 -8 -11 -11 -13 -13 -16 -16 -18 -18 -21 -21 -23 -23 Dec-06 Jun-07 Dec-07 Jun-08 Dec-08 Jun-09 Dec-09 Jun-10 Dec-10 Jun-11 Dec-11 IMF loans Potfolio investment Foreign direct investment Financial derivatives Financial loans and cash Current Account Deficit Euro Billion Data Source: NBR 4
  5. 5. Factors Driving Borrowing have evolved negatively since 2008Ever higher inflows until end 2008 boosted the economy, creating higher employment and subsequentlyhigh optimism at households. Dramatic change of sentiment after the crisis, with some stabilization inthe last 1 year Employment Outlook Sept 2008 90 14% 85 12% 80 10% 75 70 8% 65 6% In the period from 2003 to 2008, 60 consumers’ income and employment 4% 55 expectations rose rapidly 50 2% Mar-02 Dec-02 Sep-03 Jun-04 Mar-05 Dec-05 Sep-06 Jun-07 Mar-08 Dec-08 Sep-09 Jun-10 Mar-11 Dec-11 This benign outlook encouraged the Unemployment Expectations Unemployment Rate (rhs.) expansion of lending Balance of positive answers, Percentage points Data Source: European Commission, ANOFM Both the financial and employment Financial Outlook outlook deteriorated sharply from 2008 Sept 2008 500 74 450 73 400 350 72 300 71 250 70 200 150 69 Sep-03 Jul-04 May-05 Mar-06 Jan-07 Nov-07 Sep-08 Jul-09 May-10 Mar-11 Jan-12 Statement on financial situation of household (rhs) Euro Denominated Net Real Wage (lhs) Euros, Balance of positive answers 5 Data Source: INSSE, NBR, European Commission
  6. 6. Depreciation of the Currency and Lower Expectations on Growth Led to Sharp Increase of NPLs Volume of overdue loans increased very quickly from 2008, but the growth rate is receding. Both the credit risk ratio and the NPL ratio deteriorated rapidly once overdue loans started to accumulate. Asset quality deterioration in the banking system: Volume of Overdue Sept 2008 Sept 2008 3.5 500% 24B illions R ON 450% 21 3.0 400% 18 2.5 350% 15 300% 2.0 250% 12 1.5 200% 9 1.0 150% 6 100% 0.5 3 50% 0 0.0 0% Jan-07 Oct-07 Jul-08 Apr-09 Jan-10 Oct-10 Jul-11 Dec-05 Sep-06 Jun-07 Mar-08 Dec-08 Sep-09 Jun-10 Mar-11 Dec-11 EUR Overdue Loans RON Overdue Loans Credit Risk Ratio NPL Ratio* Ron Overdue Loans (y-o-y growth rate) Euro Overdue Loans (y-o-y growth rate) Percentage points percentage points Data Source: NBR, Bancpost Estimates Data Source: NBR * Backwards from November 2009, the NPL ratio is re-constructed as an interpolation of the Credit Risk Ratio. Credit Risk Ratio is defined as gross exposure to non-banking loans and interest classified as “doubtful” and “loss” to total non-banking loans and interest, excluding off-balance sheet elements 6
  7. 7. Romania - A case study in consumer creditHow to identify value opportunities by using data analytics 7
  8. 8. Data Analytics across the Credit Cycle have Defined a New Business Model for EFG Romania The benefits of using data analytics shifts the “blind mass approach” to “segmented approach” across the credit cycle, from customer acquisition to collections. Targeting Customer Customer Service & Collections & Underwriting Recoveries of Customers Development Anti-attritionACTIVITIES • Card acquisition • Top-ups • Anti-attrition • Pricing of new • Collections & recoveries • X-sell to existing production • Add-ons offers strategies lending base • Complaint • Usage management BEFORETOOLS • Judgmental • Same pricing • Judgmental • Delinquency and outstanding • N/A policies for all approved policies balances AFTER • Focusing on netTOOLS • Credit cards • Behavioral score targeting model margin results, • Yield matrix • Behavioral score, • Credit Bureau black & white thus tailored • Behavioral targeting good • Behavioral score • Employment info from the Pension House approach per score (FICO) customers • Property info from Fiscal Authorities segment 8
  9. 9. The Romanian Credit Bureau Provides Valuable Info & ScoresRomania has a single Central Credit Bureau that contains data of ~98% of the banking system, bothnegative and positive data. Since 2009, a behavioral scorecard has been developed by Fair IsaacCorporation (FICO), adding a ranking tool in the existing available data (exposure of the customers,payment behavior, demographic data)In 2009, the Credit Bureau introduced an integrated behavioral scoring developed by Fair IsaacCorporation, called FICO Score. Bancpost was one of the early adopters and implemented it as ananalytical tool to be used across the credit cycle. The components on which the FICO score is calculated: 5. Credit mix 10% 1. Payment 4. Pursuit of new History 35% credit 10% 3. Credit history length 15% 2. Outstanding debt 30% 9
  10. 10. TargetingBancpost has replaced common sense (judgmental) targeting with an approach based on developedanalytical tools. We studied the existing populations with the respective product based on the mix ofother products and their behavior, based on which the drivers that make an individual to be less risky andmore profitable have been identified. First phase: development of the model for targeted approach Observe Create and Apply the logic on predicting validate the logic: the existing Suppress Suppress variables for segmentation or population non low high risk revenue and data modeling holder of a Credit revenue customers risk Card bringers Second phase: Review current line assignment process and criteria as the size of the line is the trigger for both revenue and risk. In case of Amex and Visa portfolio the lines were not differentiated by risk of default (similar lines no matter the risk) and current equation were reviewed Results More targeted approach towards both risk and revenue to provide rank-order of customers by profitability. Logic was transferred and implemented into our systems, the prospects list is generated automatically and can be refreshed on a continuous basis Optimized line assignment, in order to maximize revenues and reduce risk 10
  11. 11. Underwriting – Risk Based Pricing (I) As opposed to a standard approach used previously on all qualifying customers, a segmented approach has been developed, aiming to reward the good behavior, and as well as to keep the net margin at the same or higher levels. Spreads, albeit discounts RBP Implementation (using Credit Bureau’s FICO as key discriminator) No. of Low risk customers in the portfolio Consumers’ market perception of interest for consumer loans. Bancpost’s strategy is to reward existing good behavior, attract more low risk customers and maintain or increase its net revenues. DAE was estimated for a 5Y loan, 30 days between the simulation and the 1st due date, 12,000 RON as loan amount Avg. Market DAE~  Non Secured RON  ~ BT Alpha Var. BP var. BRD CEC B Rom RZB var. BP var. BP var. BCR Garanti UCR Sp Alpha fix BP var. Bravo fix Seg A Seg B Seg. C Data as of December 2011 Before RBP 11
  12. 12. Underwriting – Risk Based Pricing (II)The risk-based pricing was implemented as an extensive marketing campaign (A LOAN IN YOURMEASURES), with very good results and good press coverage. Introduction of RBP Product 12
  13. 13. Customer Service – Anti attrition Bancpost developed an anti-attrition model for Amex Cards to replace the “common sense” approach of proactively (through retention campaigns) or reactively addressing customers. Categories of Variables for Propensity to Attrition Modeling Based on: • Customer Life Time Transaction Data Value • Probability of attrition Customer Service Payments Data w/bank • Spending pattern • UtilizationAccount Performance Retention Strategy Marketing Data Clients are addressed differently with and not only: Application Data Other relationships w/bank • annual fee waiver • cash back Credit Bureau Data • lower interest The model provides the client’s likelihood (%) to attrite and also the customer lifetime value (CLTV). 13
  14. 14. Collections - Early The strategy for early collection shifted from time-based approach to a risk-based approach of the delinquent customers; risk-rating per customer was derived from the Credit Bureau’s FICO score and own Basel models. tions llection acLow Risk Intensity of early co delinquent daysHigh Risk Intensity of early collection a ctions Risk based collection strategy led to decrease in vertical 1-5 roll rates Per each risk segment and bucket, different collection tools & actions are applied: for each bucket, different letter layouts & text were implemented; intensity of calls varies according to risk & bucket: lower buckets, higher intensity is applied for medium & high risk accounts, while higher buckets low risk is treated with higher intensity; different timeline is used in sending letters and text messages. 14
  15. 15. Late Collections & Recoveries The Legal process uses an information based strategy for recoveries. Considering answers received from interrogation performed to state authorities, the case is assigned to either legal or amicable process. 180+ dpd recoveries Information based recovery strategy and We interrogate the Fiscal intensification of actions Authorities and the Pension House Per account strategy is Starting point for defined by the relevant defining recovery information strategy using customer risk if no information is identified, sources are re-interrogated at regular intervals 15Bancpost internal data
  16. 16. Financial Results & Data AnalyticsWith the help of data analytics across the credit cycle the effects of the financial crisis are not “visible” inthe net spread of the consumer lending business. Consumer lending net spreads (after impairment) 250 Risk-based targeting 200 Risk-based pricing & limit allocation for cards 150 Old programmes 100 Risk-based collection strategies 50 Information-based recoveries 0 FY 08 FY 09 FY 10 FY 11 FY 12 Act Act Act Act Prop 16
  17. 17. Conclusions The financial environment is unfavorable to consumer finance across Eastern Europe driven by lower capital inflows, lower consumer confidence and higher delinquency since the crisis started in 2008 EFG Group in Romania has been using data analytics, and extensively data and scores from the credit bureau, across the entire cycle of consumer lending, to identify value creation opportunities Extensive usage at EFG Group Romania has given our consumer credit operation a commercial advantage, doubled net spreads since 2008, reduced roll rates and increased recoveries. 17