Strategy Optimization for Credit€¦ · • Optimization applied dynamically at the individual customer level • Improve personal loan customer profitability • Consider many alternative

© 2005, Experian-Scorex Proprietary and Confidential Release v1.0 // September 2005 // Page 1

Strategy Optimization for Credit

Maximise profit while managing

risk

New Business PricingNew Business Pricing


The credit decisioning dilemma

How to determine the right action based on the multiple dimensions of customer value within operational and financial constraints?

Customers Decision

Action “A”

Action “B”

Action “C”

Action “D”

Action “E”

Action “F”

Profit

RiskRisk

Take UpTake Up

XX--SellSell

FeesFees

Early Early

SettleSettle

Results

A function of the customer profile, and the action taken.

Typical constraints

•Best offer

•Best Advice

•Competition

•Credit Losses

•Rates allocated

•Business volume

•ROI hurdles

•Resource capacity

•Targets

•Budgets


Agenda

�The solution

�Loan pricing business problem

�Development methodology

�Some results


Making a decision on each customer

�Calculate the profit made for each customer based on historical

decisions

� Infer the financial effect on each customer as if we had we taken different decisions

�Select the strategy action which would have maximised the

profit for each customer

Right?



�Maximising profit for each customer won’t necessarily meet the

business needs

• Bad debt levels could increase beyond agreed budgets

• The volume of business written may fall – impacting the credibility of the brand in the market

• Referral volumes could exceed manageable levels

Not quite….


How to resolve the issues

�We need to create a decision process which enables the

business to maximise profit but ……..subject to constrained

portfolio rates of Bad debt, volumes, exposure etc.

Resolution

�It is an optimization problem


Optimization defined

Mathematical decisioning process to maximise a business objective or goal (such as profit) subject to constrained resources


£90 £100 £120

£4 £6 £8

£70 £90 £60

£3 £6 £8

£20 £200 £300

£3 £12 £15

Profit

Bad Debt

Profit

Bad Debt

Profit

Bad Debt

Loan APR

Customer 1

Customer 2

Customer 3

10% 12%8%

Simplified new business optimization example

Option/ 1 2 3

1

2

3

Profit

Bad Debt

Customer

10% 10% 8%

- 8% 8%

12% 10% 10%

£400 £370 £360

£21 £21 £19

Option 1: Max bad debt = £21

Option 2: Max bad debt = £21, and same accept rate

Option 3: Max bad debt = £21, and same accept rate, and lower bad debt


Agenda

�The solution



�Some results


New Business : Loan Pricing

• Typical results: up to 20% increase in profit contribution for the same lending amount and bad debt value

• Optimised Decision Engine used to determine the optimal price to offer to new customers

• Optimal decision based on: Deal profitability

• incorporates propensity to take up the offer and credit risk losses

• Optimization applied dynamically at the individual customer level

• Improve personal loan customer profitability

• Consider many alternative interest rates

Challenge

Solution


Risk Based Pricing – building models for profit

�The organisation has operated risk based pricing for a period of

time

�There are are variety of rates assigned to different customer groups

� Interest rates have varied over time

�Competition has offered different rates influencing the market

place and customer behaviour

Scenario


What factors influence the decision?

� Increasing price improves margins but

• Reduces take up volumes

• Increases bad debt rates

�Potential impact on market

share

� Legislation – only 1/3 of

customers may be priced

Business Issues Financial Factors

�Credit Risk

�Propensity to take up loan

�Existing margin on loan

�Term and value of loan

�Early settlement

�Existing relationship value

Risk is a key component of the decision – but not the only dimension


How does optimization help in pricing?

�Enables organisations to manage multiple constraints and consider many alternative strategy actions

�Allows organisations to apply quantitative methods to the art of pricing decisioning, maximising returns

Optimization

Volume

constraints

Bad debt

Constraint

Product

targets

Pricing

constraints

Etc.

Constraints

Right Offer

Customer-level recommendation

Customer

Profile

Business

Objective

Max profitetc


How does optimization help in pricing?

Variable Terms setting

Test and Control

Accept Decline

decisions using risk score

Decision complexity

Inc

rem

en

tal

be

ne

fit

Strategy management

Champion Challenger

+5 to +20%

+5% to +20%

Offer Modelling

Optimized

Selections

+15 to +30% or more

Constrained, mathematical optimization significantly

outperforms current best practice approaches by

evaluating the entire set of actions / offers

simultaneously rather than one at a time


Agenda

�The solution



�Some results


Development Methodology

Predictive analytics rank

customers based on risk,

propensity to take up the

loan

Analytics

Models are combined

using optimization to

recommend customer

decisions which maximise

profit within constraints

Optimization

Decisions are executed

within an application

processing system

Deployment

Decisions are evaluated

and fed back and

evaluated

Evaluation


Analytical steps

Decision

Definition

Decision

Modelling &

Evaluation

Decision

Formulation

Decision

Simulation


Analytical steps

Identify the key components of the utility function (e.g. profit)

Identify the range of potential behavioural states (e.g bad)

Decision

Definition

Decision

Modelling &

Evaluation

Decision

Formulation

Decision

Simulation

Identify Key Outcome States

Utility Function Definition



Assume that max profit is the business objective and that profit will vary with any given strategy. The requirement is to forecast profit for various strategy actions for each customer. First Step is to define ‘What is Profit’

Other costs (which do not vary by loan rate) can be

included in the utility function to reflect overall

business profitability

Other costs (which do not vary by loan rate) can be

included in the utility function to reflect overall

business profitability

An example Profit definition for Loan

portfolio:

+ Insurance income

+ Principal interest

– Cost of funds

– Cost of Capital

– Expected loss

– set-up costs



Outcome states identify groups of customers which have behaved in a specific way - gone to default, closed, early settle (say for a loan). The likelihood of customers entering this state can be modelled from the observation data available at the decision making point.

Outcome states are the key drivers affecting the profit components calculations (and

therefore the profit models)

Loss

Default

Revenue

Close

Revenue is accounted for only if the applicant accept the loan offer

Revenue is accounted for only if the applicant accept the loan offer

Observation point dataThe take up outcome

state is modelled using observation point data

The take up outcome state is modelled using observation point data

Take UpEarly Settle

Revenue Revenue

Default outcome state is modelled using

observation point data

Default outcome state is modelled using

observation point data

The loss value is modelled for any

customers likely to default

The loss value is modelled for any

customers likely to default


Analytical steps


Comparative analysis of the predictive models against observed behaviour

Define the modelling data set and grouping customers with similar behaviours


Predict each profit component/potential outcome state and each potential action based on historical decisions

Decision

Definition

Decision

Modelling &

Evaluation

Decision

Formulation

Decision

Simulation



Observation Segment Definition

Profit Component Modelling

Model Validation and

results


Modelling Profit Components

� In order to develop models, the organisation needs experience of different actions/ rates

� Ideally this experience is on similar customers

�Actions need to be designed carefully to provide a

range of different experiences of action.

�Client involvement is critical

Experience is essential



Take up of loan varies by application score

- the higher the score the lower the probability of take up

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Factoring in the effect of pricing

Increasing rates reduce the take up rate - often the effect is not linear

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Risk Scores will need adjusting…

•One of the most predictive pieces of information about a customer’s

risk is whether the customer is prepared to take up the loan.

•The doubling of default (bad) rates at a given score for customers who

are risk priced is not unusual

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Analytical steps


Comparative analysis of the predictive models against observed behaviour

Combine the predictive models into the function to be maximised

Define the modelling data set and grouping customers with similar behaviours


Predict each profit component/potential outcome state and each potential action based on historical decisions

Simulate how the components of the utility function vary by different potential actions

Decision

Definition

Decision

Modelling &

Evaluation

Decision

Formulation

Decision

Simulation



Observation Segment Definition

Profit Component Modelling

Simulation

Model Validation and

results

Formulation


Typical inputs

Strategic imperatives

Risk Scores

Insurance Take up

Early settlement

Loan Profit

Referral costs

Initial Rate

Channel

Constraints

Example optimization process

Results feedback loop continuously

improves the process

Scenario 1

Scenario 2

Scenario 3

Scenario n

Assess different

scenarios &

choose the best one

to deploy

Deploy

chosen

scenario

Optimization

Create scenarios

that maximise

objective(s) subject

to constraints


Marketswitch Optimization Software

Configurable

Business &

Operational

Constraints

�

Configurable Predictive

Economics

�

‘What-if’

Scenario Optimizations

� Configurable Business

Goals

�

Offer & Channel

Hierarchies

�


Strategy Optimization Implementation

• Application scorecard applied and based on score (and other rules) policy decline rules applied

• Some customers may not be eligible for Pricing

• Champion Challenger Test – based on rule base or Optimization

• Multiple Models are applied and profit components derived for each price option

• Ability to apply different Optimized scenarios based on different constraint setting

• Consolidation of decision

Strategy Management

system defines the parameters for each customer which define propensities and other profit components


Agenda

�The solution



�Some results


Results: Trade off Between Profit and Volume

Historical

Maintain Take Up% Price Constrained

Unconstrained ��!��

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Trade Off Between Profit and Bad Debt

Current Accept

Unconstrained

Bad Debt Constrained

Maximum Volume


Results: Profit Improvement

Previous Accepts –few accounts declined historically

Profit Opportunity 12% -15%

Where unconstrained, most customers are priced

Scenario

% Change

in

Accepts

% Change in

Profit

%

Change

in Take

Up

% Not

Priced (all)

Historical Strategy 100000 90

Non constrained -2% 15% -10% 14

% Priced Constrained -2.5% 12% -15% 54


Retail BankLoan pricing

13% profit improvement

Diversified ServicesCustomer cross sell campaigns

25% profit improvement

International Wireless Provider�500,000 per day improvementin retained revenues

Finance CompanyInitial collections actions

18% reduction in losses

Card IssuerCredit line increases

£7 incremental profit per account per annum

Card IssuerCustomer acquisition campaigns

20% improvement in customer lifetime value

Enterprise-wide customer optimization


Strategy Optimization for Credit

Maximise profit while managing

risk

New Business PricingNew Business Pricing

Documents

Strategy Optimization for Credit€¦ · • Optimization applied dynamically at the individual customer level • Improve personal loan customer profitability • Consider many alternative