combining all relevant and new data sources to create one ... · three distinct types of data...

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Experian Public.

Combining all relevant and new data sources to create one complete risk management offering

Kelly Love | Experian

Christopher Briggs | Experian

© 2012 Experian Information Solutions, Inc. All rights reserved.

Experian Public. 2

Contrary to popular belief …

… many clients don’t really use significant data sources as part of decision management

Over 75% of clients surveyed use less than three distinct types of data within originations

Most, however, use data to create models and scorecards that are used in a decision process

Many sources are readily available, yet few organizations take advantage of the wealth of information that can relatively easily accessed in real-time

Why don’t organizations use more different types of data?


Experian Public. 3

Combining all relevant and new data sources Polling question #1

As part of a standard acquisition processes, do you use more than four data sources (including both internal and external sources)?

A. Yes

B. No

C. Don’t know


Experian Public. 4

Objectives

Gather different data types available to complete a holistic view of the customer

Focus on how specific data sources provide value for customers

Discuss challenges when realistically trying to integrate new data sources

Understand how Experian products offer significant advantages for those organizations expanding into new data

Review case study examples on how to deploy those new data sources given current businesses processes


Experian Public. 5

What types of data sources do our North American clients typically use within decision management?

Consumer U.S. (42%)

Commercial U.S. (23%)

Authentication & Fraud U.S. (14%)

Consumer Canada (10%)

Commercial Canada (10%)

Other (1%)


Experian Public. 6

Transactional Consumer

transaction data at the

lowest level, that provides

additional intelligence on

consumer risks

Verification Increased operational efficiency by having verification data available via ATB

Thin file Ability to determine consumer risks in several credit industries, such as auto, bankcard, retail card, etc.

What are the typical types of data sources supporting decision management today?

Vertical Additional

insight into a process

or asset

Fraud Allows for applicant

authentication


Experian Public. 7

What are the typical types of data sources supporting decision management today?

Commercial World class

choice of

commercial data

Collection Increased operational efficiency via collection data

Custom Direct access

versus toggling back and forth with another data

source

Consumer

Highest data

integrity for

consumer

information


Experian Public. 8


What are the primary types of data are you using today within your acquisition processes?

A. Credit (consumer or commercial)

B. Asset (e.g., mortgage, auto, etc)

C. Fraud, authentication, and / or verification

D. Other

E. Asset and fraud, authentication, and / or verification, but not credit

F. All of the above


Experian Public. 9

Integration challenges promote specific behaviors when working with new types of data sources

High initial cost to

gather learnings on

new data types

Requires sophisticated

calculations and cross-

data capabilities to build

insight out of data

Difficult to effectively

leverage the data across

the customer life cycle

High dependency on external

resources and third party

vendors, including the actual

data source providers

Low tool flexibility with

testing and validation

capabilities throughout the

implementation process

Go with the least common

denominator – typically

consumer bureau data

Data is utilized in only

one stage of customer

life cycle

Slow cycles in adding and

utilizing data sources

across the enterprise

Many pieces of software used

to manage many decision

management processes

Data not

fully utilized


Experian Public. 10


What is the most significant challenge you have you faced integrating new types of data sources?

A. Difficult technically to integrate

B. Low return on investment

C. Don’t know exactly what to use or where to start

D. Not interested in using new and different types of data

E. None of the above


Experian Public. 11

Other data types that are most commonly being integrated in decision management processes

Capacity & capital Many different

measures used,

starting with verified

income, verified assets,

type of income

Vertical Vertical lists, lifestyle / life event, and channel preferences

Relationship data Performance data

from internal customer

warehouse, balance

transfer data

Mortgage &

collateral Property value ,

property type,

location factors,

association type,

Fixed, ARM, other,

specific types

Other data types that

have been considered in

analysis and strategies

for quite some time, are

now being integrated

into systems


Experian Public. 12


What are the primary types of data are you using today within your customer management processes?

A. Credit (consumer or commercial)

B. Asset (mortgage, auto, etc.)

C. Fraud (authentication, verification, and fraud)

D. Customer management (CRM or internal customer databases)

E. Other

F. All of the above


Experian Public. 13

Data access and the ability to bring data together allows our clients to...

Review process across the organization and across the life cycle

Manage account and customer-level relationships across business units

Allow the client to access aggregated data through software – build once and use many times

Use extensible scripting language to create and deploy characteristics supporting many uses

Personalized, comparative view across all data sources with raw data, calculated attributes

Data

Credit bureau

Demographic

Lifestyle

Transactional

Etc.


Experian Public. 14

Software makes the process easier by allowing our clients to...

Deploy attributes, models and strategies against specific data sources

Manage common content leveled across data sources

Normalize analytics across different data creating greater consistency

Leverage applicant and input data across the life cycle

Quickly identify how data changes can impact strategies

Apply changes in a test and learn environment through test database

Data

Credit bureau

Demographic

Lifestyle

Transactional

Etc.

Software

Data integrator

Decision engine

Rules engine


Experian Public. 15

Data

Credit bureau

Demographic

Lifestyle

Transactional

Etc.

Software

Data integrator

Decision engine

Rules engine

Our analytics and model development offerings provide best in class decision management capabilities...

Acquisitions Customer management Collections

Exposure management Accept / refer / decline Pre-collections

Increase usage Limit Over-limit collections

Retain valuable customers Price Early stage collections

Cross-sell / up-sell Up-sell / down-sell Late stage collections

Analytics / models

Origination models

Behavioral models

Line increase strategies

Pricing models


Experian Public. 16

Our consultants have deep subject matter expertise and have helped our clients to...

Improve overall processes and workflows

Determine data needs and how to best incorporate and utilize data across the life cycle

Develop policy rules and strategies to support decision management practices

Build business cases in support of new initiatives

Data Software Analytics /

models

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Experian Public. 17

Having the proper tools in place supports optimal decision management practices across the customer lifecycle

Increase efficiency

Manage credit quality

Improve bottom line

Optimize customer lifetime value

More effectively target and penetrate key markets

Maximize customer relationships

Manage each customer for their value and potential

Improve collections efficiency

Identify the right treatment for each customer

Data Software Analytics / models

+ +

Collections Prospecting

Acquisitions Customer management

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Experian Public. 18

Application data Validation Fraud detection

Total loan payment History of payment Overall indebtedness Premier AttributesSM

Custom models

Attribute calculations

Using additional data sources to support specific consumer lending implementations

Fraud

Data verification

Consumer credit

Multiple data sources

Integrating into operational decision systems

Decision management

Data aggregation


Decision processing

Decision rules:

Pre-approved

General payment behavior = good, bad

Application rules:

Age = 21 +

Employment = Self employed, full-time

Policy rules (risk evaluation)

Continuous improvement on risk evaluation process

Fraud detection

Risk assessment

“Ability to pay” evaluation

Loan policy:

Total balance > $100k

DTI < 40%

NDI ≥ $750/month Consumer lending strategy outcome

“Approved” or “declined”


Experian Public. 19

Case study for consumer lending supporting faster implementation and more efficient processing

Build custom attributes constructed using consumer credit and asset data

Enhance access to more types of data, such as non-standard consumer credit

Installed on site or hosted transaction models

Strategy ManagementSM

Attribute ToolboxTM

Significantly increase income over short time

Champion challenger calculations across one or more channels

Aggregate data across multiple sources, including internal data

Automate manual processing and lower IT dependency

Integrate with existing infrastructure in a single-platform solution

Faster turnaround to strategy updates

Optimize originations process across multiple systems

Facilitate improved migration paths for decisioning processes

Replace other solution (in house or third party)

Objectives Needs Solution


Experian Public. 20

Vehicle value models to create LTV

VIN number for vehicle history detail

Credit score Total secured loan $ Total unsecured loan $ Other credit specific

attributes for auto


Creating one complete decision management process for auto effectively powered by data and analytics

Auto data

Experian Auto Check® data

Experian credit bureau data

Multiple data sources

Integrating into operational decision systems

Loan policy:

PTI < 40%

Total DTI < 50%

NDI > $500 / month

LTV < 95%

Vehicle age, mileage

Max loan = $35,000

Min loan = $7,500

Prime, near-prime, sub-prime

Decision management

Data aggregation


Model implementation

Decision processing

Credit decisions

Approval, referral, decline

Credit terms (APR, monthly payment, etc)

Down payment

Closing costs

Application rules:

Age = 21+

Employment = Yes

Ever delinquent = No

Total outstanding balance = $150,000

Policy rules (risk evaluation)

Experian income model / data

Income model for PTI and DTI


Experian Public. 21

Increase auto lending competitiveness through more effective models deployed quickly

Implement new models and scores across many systems

Construct and deploy scorecards models based on strong analytics

Increase predictive nature of scorecards by aggregating data across multiple sources

Define and implement stronger models and scorecards

Build custom attributes constructed on standard analytics using consumer credit and asset data

Consumer Credit data (US & Canada)

Premier AttributesSM (US & Canada)

Strategy ManagementSM

Attribute ToolboxTM

Better qualify lending through easier and faster integration into new, different vertical market data

Offer more, different types of products and services with associated risk measurements

Increase North America market share across US & Canada

Objectives Needs Solution


Experian Public. 22


What is your next step in trying to determine what different types of data sources should be used in the decision management process?

A. Talk to the speakers – Kelly and Chris

B. Review product documentation and collateral

C. Speak with internal and / or external analytical resources

D. None of the above


Experian Public. 23

Summary

Many challenges integrating data into decision management

Challenges can be easily overcome

Enable holistic insight into the customer

Facilitate greater profitability

Advice is to start small, start now


Experian Public. 24

For additional information, please contact:

[email protected]

[email protected]

Vision Expert Annex Open every morning, afternoon and during session breaks

Meet session speakers

Gather information on Experian products and services

Request research and complimentary materials

Schedule one-on-one meetings with Experian experts

May 6–9 • The Phoenician • Scottsdale, Ariz.


Experian Public.

combining all relevant and new data sources to create one ... · three distinct types of data...

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