Data Analytics across the Credit Cycle Case study

EFMA – Consumer Credit Conference

June 6th 2012

George Georgakopoulos

Executive Vice President – BancpostPresident of the BOD – EFG Retail ServicesGeorge.georgakopoulos@bancpost.ro

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.

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Romania

A Challenging environment in Consumer Credit

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Capital Inflows

Capital Inflows to Romani

-23-21-18-16-13-11-8-6-3-1257

10121517202225

Dec-06 Jun-07 Dec-07 Jun-08 Dec-08 Jun-09 Dec-09 Jun-10 Dec-10 Jun-11 Dec-11-23-21-18-16-13-11-8-6-3-125710121517202225

IMF loans Potfolio investment Foreign direct investment

Financial derivatives Financial loans and cash Current Account Deficit

A large current account deficit in the run-up to the crisis was financed by FDI and inflows to the financial sector. Since the crisis, the inflows would have collapsed, had it not been for the IMF

Euro BillionData Source: NBR

Sept 2008

4

Employment Outlook

50

55

60

65

70

75

80

85

90

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-112%

4%

6%

8%

10%

12%

14%

Unemployment Expectations Unemployment Rate (rhs.)

Financial Outlook

150

200

250

300

350

400

450

500

Sep-03 Jul-04 May-05 Mar-06 Jan-07 Nov-07 Sep-08 Jul-09 May-10 Mar-11 Jan-1269

70

71

72

73

74

Statement on financial situation of household (rhs)Euro Denominated Net Real Wage (lhs)

Factors Driving Borrowing have evolved negatively since 2008

In the period from 2003 to 2008, consumers’ income and employment expectations rose rapidly

This benign outlook encouraged the expansion of lending

Both the financial and employment outlook deteriorated sharply from 2008

Euros, Balance of positive answersData Source: INSSE, NBR, European Commission

Balance of positive answers, Percentage pointsData Source: European Commission, ANOFM

Ever higher inflows until end 2008 boosted the economy, creating higher employment and subsequently high optimism at households. Dramatic change of sentiment after the crisis, with some stabilization in the last 1 year

Sept 2008

Sept 2008

5

Depreciation of the Currency and Lower Expectations on Growth Led to Sharp

Increase of NPLs

Volume of Overdue

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Dec-05 Sep-06 Jun-07 Mar-08 Dec-08 Sep-09 Jun-10 Mar-11 Dec-11

Bill

ions

RO

N

0%50%100%150%200%250%300%350%400%450%500%

EUR Overdue Loans RON Overdue LoansRon Overdue Loans (y-o-y growth rate) Euro Overdue Loans (y-o-y growth rate)

percentage pointsData Source: NBR

Percentage pointsData Source: NBR, Bancpost Estimates

Asset quality deterioration in the banking system:

0

3

6

9

12

15

18

21

24

Jan-07 Oct-07 Jul-08 Apr-09 Jan-10 Oct-10 Jul-11

Credit Risk Ratio NPL Ratio*

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.

Sept 2008 Sept 2008

* 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

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Romania - A case study in consumer credit

How to identify value opportunities by using data analytics

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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 of Customers

Customer Development

Customer Service & Anti-attrition

Collections & RecoveriesUnderwriting

TOO

LS • Judgmental policies

• Judgmental policies

• N/A • Delinquency and outstanding balances

• Same pricing for all approved

AC

TIVI

TIES • Card acquisition

• X-sell to existing lending base

• Top-ups

• Add-ons

• Usage

• Anti-attrition offers

• Complaint management

• Collections & recoveries strategies

• Pricing of new production

BEFORE

AFTER

TOO

LS • Credit cards targeting model

• Behavioral score (FICO)

• Behavioral score, targeting good customers

• Yield matrix

• Behavioral score

• Behavioral score• Credit Bureau black & white• Employment info from the Pension House• Property info from Fiscal Authorities

• Focusing on net margin results, thus tailored approach per segment

The Romanian Credit Bureau Provides Valuable Info & Scores

In 2009, the Credit Bureau introduced an integrated behavioral scoring developed by Fair Isaac Corporation, called FICO Score. Bancpost was one of the early adopters and implemented it as an analytical tool to be used across the credit cycle.

Romania has a single Central Credit Bureau that contains data of ~98% of the banking system, both negative and positive data. Since 2009, a behavioral scorecard has been developed by Fair Isaac Corporation (FICO), adding a ranking tool in the existing available data (exposure of the customers, payment behavior, demographic data)

The components on which the FICO score is calculated:

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2. Outstanding debt 30%

3. Credit history length 15%

4. Pursuit of new credit 10%

5. Credit mix 10%

1. Payment History 35%

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

Targeting

Bancpost has replaced common sense (judgmental) targeting with an approach based on developed analytical tools. We studied the existing populations with the respective product based on the mix of other products and their behavior, based on which the drivers that make an individual to be less risky and more profitable have been identified.

Results

Apply the logic on the existing population non holder of a Credit Card

Create and validate the logic: segmentation or data modeling

Observe predicting variables for revenue and risk

Suppress high risk customers

Suppress low revenue bringers

First phase: development of the model for targeted approach

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

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11

BP var.Seg A

BT Alpha Var. BRD CEC B Rom RZB var. BCR Garanti UCR Sp Alpha fix Bravo fix

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.

Data as of December 2011

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.

RBP Implementation(using Credit Bureau’s

FICO as key discriminator)No. of Low risk customers in the portfolio

Spreads, albeit discounts

Before RBP

~  Non

 Secured

 RON  ~

DAE was estimated for a 5Y loan, 30 days between the simulation and the 1st due date, 12,000 RON as loan amountAvg. Market DAE

BP var. BP var.Seg B

BP var. Seg. C

Underwriting – Risk Based Pricing (II)

The risk-based pricing was implemented as an extensive marketing campaign (A LOAN IN YOUR MEASURES), with very good results and good press coverage.

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Introduction of RBP Product

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.

The model provides the client’s likelihood (%) to attrite and also the customer lifetime value (CLTV).

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Based on:

• Customer Life Time Value

• Probability of attrition

• Spending pattern

• Utilization

Clients are addressed differently with and not only:

• annual fee waiver

• cash back

• lower interest

Categories of Variables for Propensity to Attrition Modeling

Retention Strategy

Transaction Data

Payments Data w/bankCustomer Service

Account Performance Marketing Data

Application DataOther relationships w/bank

Credit Bureau Data

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.

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

Low Risk

High Risk

Intensity of early collection actions

Intensity of early collection actions

delinquent days

Risk based collection strategy led to decrease in vertical 1-5 roll rates

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.

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We interrogate the Fiscal Authorities and the Pension House

Per account strategy is defined by the relevant information

if no information is identified, sources are re-interrogated at regular intervals

Starting point for defining recovery

strategy using customer risk

Information based recovery strategy and

intensification of actions

180+ dpd recoveries

Bancpost internal data

With the help of data analytics across the credit cycle the effects of the financial crisis are not “visible” in the net spread of the consumer lending business.

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Risk-based targeting

Risk-based pricing & limit allocation for cards

Old programmes

Risk-based collection strategies

Information-based recoveries

Financial Results & Data Analytics

Consumer lending net spreads (after impairment)

0

50

100

150

200

250

FY 08Act

FY 09Act

FY 10Act

FY 11Act

FY 12Prop

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.

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