Analytics Solutions for Retail Banking_Marketelligent

8/8/2019 Analytics Solutions for Retail Banking_Marketelligent

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Analytics Solutions for

Retail Banking

Confidential & proprietary information. Property of Ashley Marketelligent Pvt Ltd.

www.marketelligent.com

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Marketelligent: Managing Risk & Reward across Retail Banking

Customer



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CardCredit Card

Charge Card

Revolving

Installment

InvestmentsUnit Trust

S. Notes

Bonds

Equities

Insured

InsuranceCredit

General

Life

DepositTerm deposit

Unfixed

LoanRevolving

Installment

Secured

Portfolio Finance

CASACurrent Acct

Savings

MortgageRevolving

Installment

Lend InvestSpend Transact Protect

Customer Acquisitions Customer Segmentation Marketing Investment Optimization Cross-sell/One-sell

Branch location placement Growing profitable balances MIS

Risk Management Collections & Recoveries Flow of Funds Executive Dashboards


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Delinquency Scorecards

First pay Default Scorecard

4th or 5th c cle Risk Scorecard

Decide Loan pricing

and amount

Identify Customers most

likely to default so as to

take corrective action

Identify Customers most likely

to attrite so as to take

proactive actions for retention

And across the Customer Lifecycle



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Approval Scorecards

Conversion Scorecards

Application Fraud

Revenue Scorecards

Profitability Scorecards

Pricing and

Loan Amount

Collection Scorecards

Self-cures

Re-Activation

Winback

Retention

Decision on who to approve

based on expected profitability

Maximize Collections

Efficiencies

Target Inactive Customers for

repeat loans


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Our Expertise in Risk & Marketing Analytics

Marketing Analytics

1. Profit-based Customer

Acquisition Strategy

1. Credit Delinquency Models

2. Other Delinquency Models; eg.

Credit Risk AnalyticsCredit Risk

Management &

Training1. To understand existing data /

reports and present a top-line



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2. Revenue Models; eg. Total 180

days revenue

3. Campaign Management

4. Cross-sell

5. Retention & Activation

6. Loyalty and Winback

7. Pricing Analytics

5+ cycles bad

3. Customer Approval and

Conversion Models

4. Optimal Loan Amount, Pricing

and loan duration

5. Forecasting

6. Collections Analytics

7. Mortgage Portfolio Optimization8. Fraud Analytics

9. Basel II Analytics

"what additional analytics to

read"

2. Prepare and deliver the

additional analytics and

highlight key concerns on

policies and processes

3. Present credit policy

changes, collections strategies

and product program changesto deliver required

management deliverables

4. Credit Policy Training


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New Accounts Acquired

Accounts Closed

Account Activation rate

Payment Rate

Total Ending Receivables

Interest

Bank P&L

Acquire New Customers

- Segments X Products X Channel- Mailbase Expansion- Pricing

Reduce Customer Attrition

- Voluntary / Involuntary- Retention Strategies

- Winback

Increasing activation rates- Deepening Engagement- Inactive Customer Treatment

Improve profitability ofAssets

- Balance Transfer- Credit Line Strategies

-

Marketelligent: A strong P&L discipline to all analyticsEg. Credit Cards



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Net Interest Margin

Risk-based Fees

Interchange

Affinity Rebates

Cross-Sell

Annual Fees

Net Credit Losses

Net Credit Margin

Operating Expenses

Loan Loss reserve

Net Income

REVENUES

EXPEN

SES

Maximizing Interest Revenue- Product Pricing- Customer Behavior Revolvers, transactors, etc

Maximizing Fee Revenue- Over Credit limit

- Delinquency- Bad Check

Reduce Net CreditLosses

- Credit Line strategies- Pricing strategies

- Collections

Increasing Cross-sell Revenues- Revenue Enhancing Products- Breadth of relationships

Top-down approach

Analytics that impact all line items of the P&L


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Marketing Analytics

Credit Risk Analytics



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Profit-based AcquisitionAcquisition strategies that balance Risk & Reward

Approval Model

Acquisition

Objective

Implement a Customer-level profit-based Acquisition Strategy based on

segmentation, predictive models and joint scores



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Conversion Model

First Pay Default Model

5+ cycle Default Model

180 day Revenues

Reactivation Model

Risk

Revenue

Flexible

Acquisition

Strategy

Individual Scores Strategy Matrix

Joint Scores

Illustrative


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Customer Segmentation

Segment Customers to better understand their needs & wants

Key Bureau Attributes

OverModerate High Moderate

HighAccess

ModerateAccess All

Very High utilizationof revolving credit

Lower access to

credit as a groupLowest FICO

Very high access tolimits

Very high balances

Above averageutilization and risk

Very high access tolimits

High balances, but

low utilizationLower risk

Defined as balance 50% utilized 78.0% 32.1% 23.5% 5.7% 5.1% 2.6% 0.5% 12.1%

FICO Score 684 712 725 702 764 782 772 751

Moderate limits

High balances andutilization

Increased risk

Moderate limits

Below averagebalances

Higher risk

Moderate limits

Low balances andutilization

Lowest risk segmentSegmentation using SAS PROC FASTCLUS


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Universe ManagementEligibility, Risk

Performance TrackingTesting discipline, MISUniverse

Building Profitable Assets

Balance transfer strategies to build profitable assets



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Segmentation

OfferStrategy

Tracking

Offer StrategyPricing / Duration / Fees

Universe SegmentationCustomized Marketing


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Pricing Analytics

Customer-level pricing to build Deposits

TD Elasticity Curve

50000

100000

150000

200000

Change

Rate hunter

Moderate Mover

Term Deposits Pricing Sensitivity Curve

Retail Banking



10

-200000

-150000

-100000

-5000050403020100-10-20-30-40-50

deviation From market(bps)

Balan

ce Loyal depositor

Lazy Depositor

Pricing is one of the most sensitive lever to improve profitability. We can build tools to establish

the price sensitivity of various customer segments. Based on this, pricing strategies can be

developed for different segments to maximise profitability through better margins and/or better

volumes.


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Cross-selling

Deepen engagement with existing Customers

Installment

Loans

Mortgages- Which Customer to target



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Existing

Retail Banking

Customer

HELOC/FRHEL

Credit Cards

Wealth

Management

- What Product to Offer

- Impact of new product on

existing Product Profitability

- Overall Profitability


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alue

Retention

Activation

Retention & Activation

Manage Customers across their Lifecycle



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Acquisition Usage & LoyaltyTime

RETENTION Identify Customers at Risk of Disengagement via

predictive modeling or activity-based segmentation

Take proactive actions via targeted offers

RETENTION Identify Customers at Risk of Disengagement via

predictive modeling or activity-based segmentation

Take proactive actions via targeted offers

ACTIVATION Segment Inactive Customers across various

dimensions

Implement targeted activation campaigns

ACTIVATION Segment Inactive Customers across various

dimensions

Implement targeted activation campaigns


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Marketing Analytics




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Predictive Scorecards to optimize Decisions

1. Approval ScorecardsCustomer-level score to decision on which

Customer to approve and which to decline for

New Products (loans, cards, etc) based on

information provided application data, bureau

data, etc.

70%

80%

90%

100%

Eg. Delinquency Scorecard

Rate



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2. Delinquency ScorecardsCustomer-level score to decision on which

Customer is going to default on their loans so as

to enable the business to take proactive actions

to minimize losses

3. Collections ScorecardsCustomer-level score to decision on whichdelinquent Customer has a higher likelihood of

paying back balances; and which Customer is

likely to self-cure; so as to enable business to

optimize Collections activities

Predictive Models using SAS PROC LOGISTIC

0%

10%

20%

30%

40%

50%

0 1 2 3 4 5 6 7 8 9 10

Random

New Model

Existing ModelCumulativeDe

faul

Score deciles


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Model captures63% of First Pay

Defaulters in

40% of

Accounts

#

AccountsCuml % FPD

Marginal

rateCuml % Non-FPD

Marginal

rateCuml % KS

789 887 512 10% 368 72% 23% 144 28% 4% 28.03

756 788 518 20% 271 52% 39% 247 48% 11% 39.36

731 755 506 30% 209 41% 52% 297 59% 20% 41.32

710 730 493 40% 177 36% 63% 316 64% 29% 41.88

693 709 583 51% 170 29% 74% 413 71% 40% 39.74

680 692 480 60% 111 23% 81% 369 77% 51% 34.65

659 679 493 70% 103 21% 87% 390 79% 62% 28.33

615 658 512 80% 86 17% 92% 426 83% 74% 21.12

Score Range

High

Risk


Eg. First Pay Default Scorecard



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.

180 412 511 100% 59 12% 100% 452 88% 100% 0

. . 5129 . 1619 32% . 3510 68% . 41.88

Score # Customers # Bad Loans# Bad Loans in

past 30 days

# Inquiries in

past 60 days# Loans given

0 512 1.30 1.15 8.85 1.76

1 518 0.55 0.41 10.61 1.41

2 506 0.10 0.03 7.43 0.543 493 0.03 0.01 5.01 0.34

4 583 0.02 0.00 2.55 0.18

5 480 0.04 0.01 3.61 0.59

6 493 0.02 0.01 3.45 0.75

7 512 0.03 0.01 3.45 1.08

8 521 0.04 0.00 3.10 1.45

9 511 0.05 0.01 3.00 4.26

Grand Total 5129 0.22 0.17 5.09 1.23

High Risk

Customers havea significantly

higher # of loan

inquiries in the

past 60 days

High

Risk

Low

Risk

Low

Risk


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Behavioral ModelsRevenue and Cost

Drivers Optimal Line Determination Optimal Line Drivers

Balance

Model

Revenue


Control Exposure with right appropriate Lines of Credit



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RevolveModel

Risk Model

LOCOptimal LOC

LOC

Cost

LOC

Predicted V/s Actual Inactivity

0.00

50.00

100.00

150.00

200.00

250.00

300.00

0 50 100 150 200 250 300 350 400

Predicted Inactivity

Ideal

c ua

Predicted

Illustrative process for assigning

Optimal Line of Credit (LOC)

Other

Models


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Collections Analytics

Prioritize Customers to action on to optimize Collections efforts

OBJECTIVE

Collect more $ efficiently thereby reducing cost/dollar collected

OBJECTIVE

Collect more $ efficiently thereby reducing cost/dollar collected

Unique strategies across various stages: early-stage; late-stage; charge-off/recovery



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Mine Customer &Operational data

Create broad profiles and

segments

Profiling & Segmentation

Rank order accountson a dimension ofinterest

Event probabilities :self sure; charge-off,etc

Expected value ofCollections

Behavior Scorecards

Assess & create smallersegments acrossmultiple scores

Assess trade-offsbetween strategies

Multi-dimensionalAnalysis

Test and Evaluateactions underbusiness constraints

Typically black-box,heuristically drivenmodels

Selecting OptimalStrategies


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Mortgage Portfolio Optimization

PREDICT

PREDICT

OPTIMIZE

OPTIMIZE

AUTOMATE

AUTOMATE

OBJECTIVE

Optimal treatment for each Customer so as to maximize NPV givenbusiness constraints

OBJECTIVE

Optimal treatment for each Customer so as to maximize NPV givenbusiness constraints



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Optimization analytics using

action-effect models to

select the best action foreach customer, given

business objectives and

constraints

NPV calculationsfor all possible

Outcomes

NPV calculationsfor all possible

Outcomes

Optimal Treatmentfor each CustomerOptimal Treatmentfor each Customer Business Rules forDecisioningBusiness Rules forDecisioning

identify likely outcomes of

different actions for

different customerprofiles, and the overall

effect on the NPV of

portfolio

Build rules that can be

deployed through your

business rules managementsystem


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Fraud Analytics

Manage for Fraud while ensuring a positive Customer experience

More InformationResponsible use of data is a

powerful weapon

against fraud

Break ConventionsTraditional credit scoring

and underwriting

procedures do not identify

fraudulent applications

Dig DeeperOnline verification of

information beyond a

Social Security number is

needed

Look for InconsistenciesVerification processes

should check for

consistencies in address and

credit bureau information

OBJECTIVEMinimize Fraud-related Losses and Fraud-related expenses while ensuring a positive Customer experience



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SSN

FRAUDFIRST

NAME

LASTNAME

DOB

ADDRESS

WORK

PHONE

HOME

PHONE

E-MAIL

ID

NO MATCH

SAME

Rules-based

Neural Networks

Illustrative for Insurance Claims


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A model was built to predict the charge-off rate in the US economy. It performs well in both

observation and validation windows except for two peaks that cannot be attributed to

macroeconomic factors

OVERALL USA CHARGE-OFF RATE (%)

5.00

Due to Post

9/11Due to change in

Bankru tc law

Independent

Variables

Sign

Total Consumer +

Forecasting

Eg. Portfolio-level Charge-off forecasting



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Actual Model built Validation Forecast

0.00

1.00

2.00

3.00

.

1986Q4 1989Q4 1992Q4 1995Q4 1998Q4 2001Q4 2004Q4 2007Q4

Federal Rate +

Houses for Sale -

DisposablePersonalIncome

-

Average WeeklyEarnings-

FinancialObligations

+


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Pillar I

Minimum CapitalCredit Risk Market Risk Operations Risk

Our experience in Basel II

Basel II Analytics

Pillar 1 Credit Risk & Operations Risk



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Standardised

IRB*- Foundation

IRB*- Advanced

Same approach as Basel I

Local, Small Banks

Internal-ratings based

PD inputs provided by bank, rest by Regulator

Multi-line National Banks

Internal-ratings based

PD, EAD, LGD based on inputs provided by bank

Large Global Banks

* IRB - Internal Ratings Based Approach


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Marketelligent: A strong P&L discipline to all analytics

Eg. Credit Cards

New Accounts Acquired

Accounts Closed

Account Activation rate

Payment Rate

Total Ending Receivables

Interest

Bank P&L

Acquire New Customers- Segments X Products X Channel

- Mailbase Expansion- Pricing

Reduce Customer Attrition- Voluntary / Involuntary

- Retention Strategies- Winback

Increasing activation rates- Deepening Engagement- Inactive Customer Treatment

Improve profitability ofAssets

- Balance Transfer- Credit Line Strategies

-



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Net Interest Margin

Risk-based Fees

Interchange

Affinity Rebates

Cross-Sell

Annual Fees

Net Credit Losses

Net Credit Margin

Operating Expenses

Loan Loss reserve

Net Income

REVENUE

S

EXPE

NSES

Maximizing Interest Revenue- Product Pricing- Customer Behavior Revolvers, transactors, etc

Maximizing Fee Revenue- Over Credit limit

- Delinquency- Bad Check

Reduce Net Credit

Losses- Credit Line strategies- Pricing strategies

- Collections

Increasing Cross-sell Revenues

- Revenue Enhancing Products- Breadth of relationships

Top-down approach

Analytics that impact all line items of the P&L


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Thank You

ASHLEY MARKETELLIGENT PVT LTD

+91-80-26642802 (India)

1-408-834-8822 (USA)

[email protected]



Analytics Solutions for Retail Banking_Marketelligent

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