hdfc bank ppt

© Experian Limited 2007. All rights reserved. Experian and the marks used herein are service marks or registered trademarks of Experian Limited. Other product and company names mentioned herein may be the trademarks of their respective owners. No part of this copyrighted work may be reproduced, modified, or distributed in any form or manner without the prior written permission of Experian Limited.Confidential and proprietary.

Best practice in data & scoring

Dr Paul RussellDirector Analytical Solutions

© Experian Limited 2007. All rights reserved.Confidential and proprietary. 2

Agenda

Some themes

Analytics and the customer life cycle

The role of scoring

Building a scorecard

Using scoring systems

Risk management infrastructure


Best practice is often discussed but almost never seen

Do the simple things well

Risk management is more than just a scorecard

The same principles apply across the credit lifecycle

Themes


New customers

1. Identifying potential customers;

2. Selling credit products to new customers;

3. Identifying the credit risk of the customer and the proposed transaction;

4. Identifying the risk of fraudulent application

5. Deciding whether to accept or decline the transaction;

6. Deciding, for accepted transactions, on the terms, e.g., credit amount, pricing.

Existing, non-delinquent customers

7. Reviewing the customers facilities (e.g., credit limits, price, etc.);

8. Cross-selling new products to the customers;

9. Ensuring good customers are retained;

10. Identify fraudulent transactions.

Existing, delinquent customers

11. Identifying self-cure customers;

12. Rehabilitation of potentially good customers;

13. Work-out customers where relationship is broken.

Target population Description

Credit process step

Customer acquisitionCustomer acquisition

Customer managementCustomer management

CollectionsCollections

13 ways to grow bad debt


Why is credit risk management important?

Get it right and it can support phenomenal value creation

European consumer finance business, Profit Before Tax and Impairment Charges ($m)

Source: Annual Reports

Impairment charges

Profits

676802 836

1,094

1,230

1,382

764

2,196

2,986

1,374

1,520

1,522

924804

740

478340288

1998 1999 2000 2001 2002 2003 2004 2005 2006


Data

Statistical Models

Credit strategies

Implementation tools

Evaluation tools

Component Description

Application data (for new customers)Account behaviour data (for existing customers)External data (e.g., credit bureaux)

Risk models (PD, LGD), fraud models (application and transaction fraud) and revenue models

Business rules that translate the outcome of statistical models in credit decisions (accept/decline, price, credit limits, etc.) that maximise profit

Software tools to automate the calculation of the above scores and credit strategies on-line on high volumes, with a high degree of flexibility to change credit strategies “on the fly”

Software tools to evaluate the performance of statistical models and credit strategies, and accuracy of implementation

5 core components


Agenda

Some basic themes


The role of scoring






Solicitation Debtrecovery

Application Customermanagement

Collections

Population

Information

Analytics touches every part of the customer lifecycle

Analytics touches every part of the customer life cycle

Amount of information about the customer grows as the relationship advances through the customer life cycle



• Channel preference

• Contact history

• Demographics

• Location

• Bureau data

• Action outcomes

• Costs

• Channel

• Product holdings

• Demographics

• Bureau data

• Previous relationships

• Account performance

• Costs

• Product holdings

• Usage

• Delinquency

• Customer contacts

• Preferences

• Bureau data

• Actions taken

• Action outcomes

• Costs

• Action history

• Promises to pay

• Promises fulfilled

• Action outcomes

• Bureau data

• Costs

• Action history

• Promises to pay

• Bureau data

• Agents used

• Promises fulfilled

• Litigation outcomes

• Costs

Solicitation Debtrecovery

Application Customermanagement

Collections



Strategy Review

Plan

Define GoalsAgree objectives

Review

Assess current challenger

Design

Build new strategy

Implement

Ensure operational deployment

Monitor

Track progress against expectations

Assess

Understand results


Agenda

Some basic themes


The role of scoring





The role of scoring

Credit scoring is a technique for predicting the future

This prediction can be anything of importance to the business Arrears Fraud Profit Response Account closure Company failure Etc.

All scoring is based on one key assumption: The past predicts the future


The role of scoring

How does scoring work?

• Scorecards add and subtract points to a baseline constant according to individual’s or account’s data

• Scorecards are easy to apply and simple to understand

• The resulting score gives a prediction of future behaviour

• Scores are used to rank individuals to assign the best actions

Baseline Constant 800

Applicant Age in Years

< 22 -50

22 - 25 -20

26 – 40 0

41 – 55 +30

> 55 0

Worst Status L6M (on all Accounts)

0 0

1 - 2 -45

3+ -100

Joint Applicant Present

Y +20

N 0

Etc. Etc.

… …

Example Scorecard


The role of scoring – application scorecard

• Consider a scorecard built to predict whether a new applicant for a credit product will default in the next 12 months

• This scorecard is used when a new customer applies…

Application Form Data

External Data(Bureau etc.)

Score-card Score

Take most appropriate action for each individual


The role of scoring - scores can drive actions

Application Score

Pro

po

rtio

n o

f A

pp

lican

ts

Low Score / High Risk

High Score / Low Risk

Extremely High Risk

Reject

High Risk

Reject or price to cover the high expected loss

Standard Risk

Accept on standard terms

Extremely Low Risk

Consider for cross-sell of other

products


The role of scoring - benefits

Best use of data

Objective

Consistent

Automation

Control

Reduced losses


Agenda

Some basic themes


The role of scoring





Building a scorecard – 3 requirements

Development sample – the historical data on which the scorecard will be built

Outcome – what we are trying to predict

Modelling methodology – the statistical tool that will help us form our scoring model

Some time laterThe recent past

TH

EN

NO

W

Development Sample

Outcome

Score-cardStatistical Model


Is my sample any good?

Representative Products Business cycle The future

Robust Volumes

Mature

Is the outcome reliable?

The recent past

TH

EN

Development Sample


Building a scorecard – the development sample

The recent past

TH

EN

Development Sample

• This data can come from a number of sources• All relevant data should go into the development sample

ApplicationForm

Information collected from the applicant at the application

point

CreditBureau

Data

Information on the individual’s

other credit commitments

HistoricalAccount

Behaviour

Information on the historical behaviour on the account

OtherAccount

Information

Information on the historical behaviour on

other accounts with the same

lender


Building a scorecard – the outcome

This is the behaviour that we are trying to predict

NO

W

Outcome

• Can be a continuous variable (profit, revenue, loss given default, etc.)

• More commonly it is dichotomous - yes/no Will this applicant default? Is this transaction fraudulent? Will this company fail? Etc.

THE FUTURE

Observation - Now

Good

Outcome - Prediction

Bad


What are we trying to predict?

Bad Good

Consumer 3 payments in arrears

Not 3 payments in arrears

Limited business Failed Still going

Non-limited business Bankruptcy, court judgements or

defaults

No bankruptcy, court judgements

or defaults

NO

W

Outcome


Building a scorecard – the statistical model

Many statistical tools available

Data is the most important factorStatistical Model

Observation Data Outcome Statistical ModelScore-card

Statistical tool needs to be:

Powerful – to get the best prediction from the data

Flexible – can handle varying data types and outcomes

Interpretable – easy to understand and to overlay business intelligence

Transparent – should be non-’black box’ for regulatory reasons and to ensure understanding


Building a scorecard - the statistical model

Linear regression

Logistic regression

Artificial neural networks

Etc

Other things being equal the choice of algorithm has relatively little impact on the ultimate power of the model

Statistical Model

xx

xx

xx

x

x

xxx

xx

x

x

x x

xx

x

x

Prediction

Rea

lity


Building a scorecard – assessing the model

Does the model solve the business problem?

Discrimination – the power to polarise individuals between good and bad - Gini statistic & Kolmogorov-Smirnov statistic

Accuracy – how much of the variability of the outcome is explained by the model

Validation – ensures that over-modelling has not occurred or that an anomalous sample has not been used

Improvement – the new model should outperforms the existing model

Statistical Model


Agenda

Some basic themes


The role of scoring






The data feeds the scoring system, which is used to aid the decisioning

The decisions a company makes determine its strategy

It is the aims and strategy of the business that must be considered when deciding how to use a scoring system, e.g.

Growing the market share Reducing bad debt Increasing automation Maximising response for given marketing cost Combating fraud

DATASCORECARD

STRATEGYDECISIONS


The role of scoring - scores can drive actions

Application Score

Pro

po

rtio

n o

f A

pp

lican

ts

Low Score / High Risk

High Score / Low Risk

Extremely High Risk

Reject

High Risk

Reject or price to cover the high expected loss

Standard Risk

Accept on standard terms

Extremely Low Risk

Consider for cross-sell of other

products


Using scoring systems - the score distribution

Score Band # Goods # Bads GB Odds % Applicants

≤ 400 500 500 1 9.8

401 – 550 700 350 2 10.3

551 – 650 815 163 5 9.6

651 – 700 1008 84 12 10.7

701 – 750 976 61 16 10.1

751 - 800 950 38 25 9.7

801 – 850 1000 25 40 10.0

851 – 900 1050 21 50 10.5

901 – 950 960 16 60 9.5

≥ 951 1000 10 100 9.9

TOTAL 8959 1268 7.1 100

• Score distribution is obtained by applying the score to the development sample

• Gives us a prediction for new applicants falling into a given score range


Building a scorecard - the score distribution

Score Band # Goods # Bads GB Odds % Applicants

≤ 400 500 500 1 9.8

401 – 550 700 350 2 10.3

551 – 650 815 163 5 9.6

651 – 700 1008 84 12 10.7

701 – 750 976 61 16 10.1

751 - 800 950 38 25 9.7

801 – 850 1000 25 40 10.0

851 – 900 1050 21 50 10.5

901 – 950 960 16 60 9.5

≥ 951 1000 10 100 9.9

TOTAL 8959 1268 7.1 100

REJECT

REFER

ACCEPT

ACCEPT WITH X-

SELL

Score + Policy Rules + Terms of Business = Strategy


Agenda

Some basic themes


The role of scoring





Implementation – the Business Rules Engine

Deployed in:- Origination Application processing Portfolio Management Customer level

decisioning Collections Authorisations Intelligent Messaging Event Management Basel II Stress testing …..

Data

Rules execution(Decision Agent)

Rules execution(Decision Agent)

Rules Definition(Strategy Design Studio)Rules Definition(Strategy Design Studio)

Results


Implement final decision

Get policy decision & enrichment

strategy

The unsecured lending origination process

Gather & validate application data

Gather existing customer

information

Invoke enrichment strategy

Get decision & terms of business

Handle referrals and manual procedures

Detect application fraud

A full range of client options and interfaces for channel independence and data accuracy

Online links to gather data about existing relationships and customer behaviour

Business-driven scoring & decision-making

Application screening and data matching

Credit bureau links

Business-driven scoring and decision-making

Comprehensive workflow capabilities and provision of relevant data for users

Automated account set-up. Provision of hand-off files. Letter and e-mail production


Operational environmentOperational environment

Data ManagerStrategic businessStrategic businessenvironmentenvironment

DecisionDecisionEngineEngine

H O S H O S TT

e.g. Account e.g. Account Management Management

System,System,Authorisation Authorisation

SystemSystemetcetc

Defines Defines Business logic, Business logic, Segmentation, Scorecards, Segmentation, Scorecards, Strategies and Champion Strategies and Champion ChallengerChallenger

RuleDefinition

RuleDefinition

ActiveActiveHistoryHistory

VariablesVariables

ExtractExtract

FeedbackFeedback

ResultsResultsAnalyticalAnalyticalData MartData Mart

ImplementsImplementsBusiness logic, Business logic, Segmentation, Scorecards, Segmentation, Scorecards, Strategies and Champion Strategies and Champion ChallengerChallenger

EvaluationEvaluationOptimisationOptimisationReportingReporting

Strategy ImplementationStrategy Implementation


Using performance data enables better decisions, but is also more complex to combine all the decision influences to maximise value

Influence due to:• Macro-economics?• Use of intuition?• Misunderstanding?

#35

Beyond scoring - strategy optimisation

There are disadvantages to traditional champion/challenger testing…• The time frame for observing results can be long• It can be hard to design the next step• The result can become a “semi-random walk”...

Time

Value

Champion

Challenger 1

Challenger 2

Challenger 3

Challenger n

Decision strategy “deploy-learn-

deploy” process

Challenger 4

The challenger strategy proven in one time period, may no longer be appropriate for another time period – things change

We want to get there with the first challenger !


#36

Developments in analytics - strategy optimisation

• Experience and intuition

• Trial and error

Incremental benefitROI

Underlying decision complexity

Manual

XXX

XXX

X XXXX X

XX

XXX

XX X

X

X

Scoring

Elaborate Strategies

• single predictive model e.g. credit risk score

• “Heuristic” cut-offs assigned using good:bad odds

• Segmentation based on predictive model dimensions: e.g. risk and revenue

• “Subjective” judgment used to manage trade-offs

XXXX XX

X XXXXXXX

X XX

XX

XX

X

Optimised Strategies

• Allocates optimal action for each customer within constraints

• Objective, mathematical goal maximisation

The next step…

Most are here

Some organisations are still here


Stage 1: build the infrastructure

Centralisation of credit decisioning

Set-up of IT tools required to automate credit risk and market management processes and the interaction between front line and back office

Development of decision support tools

Development of credit / marketing databasesAutomate the processes


Stage 2: fine-tune for performance

Fine customer segmentation based on customer profile, product holding and behavior data

Advanced credit and marketing databases drive increased sophistication in statistical models development

Customer interactions for risk and marketing are proactively initiated at all key points

Strategies are designed at customer-level

Automate the decisions


Stage 3: optimize for excellence

Infrastructure enables total proactive control of the business – decision analytics becomes a way of life

Risk and marketing strategies are centrally designed based on advanced statistical techniques and drive customer profitability

Decision analytics is well structured and integrated across business functions including risk, marketing, sales, operations, finance

Optimize the decisions


The Road Map

“a world-class consumer finance company”

How well defined and the processes,

and what is the degree of

automation?

How are credit risk, marketing and finance working together? How

are operational and strategic decisions taken?

How well do staff understand all profit drivers? What is the

degree of expertise in credit scoring and decision science?

How are credit policies and strategies defined,

reviewed and improved?

What credit management tools are used? How

flexible are they? How easy is it for business user to change processes and

strategies?

Processes fully support profit-driven strategy,

and are integrated across functions

Profit-driven organisation

across functions

Ongoing knowledge

improvement

Monthly review of credit strategies.

Champion/challenger a way of life

Full suite of scorecards,

ability to optimize credit

strategies

Processes regularly reviewed and refined.

Little manual intervention

Create strategy review cross-functional team

Education on strategy review process, fully

understanding the use of MIS

Profit driven credit strategy in place and

reviewed regularly

Ability to review and modify

credit strategies ‘on the fly’

Ensure clear assignment of responsibilities for risk management functions

Include all available data into the process. Focus underwriter on

“key” review, not second scorecard

Processes well defined and automated

Credit policy in place

Scorecards in place for all critical

segments, decision engine used to

control terms of business. Generate

key KPI’s

Fine-tune for performance

Optimize for excellence

Tools

Strategy

OrganisationKnowledge

Processes

Build the infrastructure


Conclusions

It all starts with data

Scorecards are important

Strategy is more important

Implementing the strategy properly is vital

If you don’t monitor you’re wasting you time

Risk management is a never-ending journey

© Experian Limited 2007. All rights reserved. Experian and the marks used herein are service marks or registered trademarks of Experian Limited. Other product and company names mentioned herein may be the trademarks of their respective owners. No part of this copyrighted work may be reproduced, modified, or distributed in any form or manner without the prior written permission of Experian Limited.Confidential and proprietary.

Best practice in data & scoring

Dr Paul RussellDirector Analytical Solutions

hdfc bank ppt

Documents

customer lifecycle analytics

credit limits

credit products

good customers

customers facilities

potential customers

credit bureaux risk

bureau data agents