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

Transcription

1 Combining all relevant and new data sources to create one complete risk management offering Kelly Love Experian Christopher Briggs Experian Experian and the marks used herein are service marks or registered trademarks of Experian Information Solutions, Inc. Other product and company names mentioned herein are 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.

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? 2

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 3

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 4

5 What types of data sources do our North American clients typically use within decision management? Consumer Canada (10%) Commercial Canada (10%) Other (1%) Authentication & Fraud U.S. (14%) Consumer U.S. (42%) Commercial U.S. (23%) 5

6 What are the typical types of data sources supporting decision management today? Verification Increased operational efficiency by having verification data available via ATB Fraud Allows for applicant authentication Vertical Additional insight into a process or asset Thin file Ability to determine consumer risks in several credit industries, such as auto, bankcard, retail card, etc. Transactional Consumer transaction data at the lowest level, that provides additional intelligence on consumer risks 6

7 What are the typical types of data sources supporting decision management today? Consumer Highest data integrity for consumer information 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 7

8 Combining all relevant and new data sources Polling question #2 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 8

9 Integration challenges promote specific behaviors when working with new types of data sources Data is utilized in only one stage of customer life cycle Many pieces of software used to manage many decision management processes High initial cost to gather learnings on new data types Slow cycles in adding and utilizing data sources across the enterprise Go with the least common denominator typically consumer bureau data Requires sophisticated calculations and crossdata capabilities to build insight out of data Difficult to effectively leverage the data across the customer life cycle Low tool flexibility with testing and validation capabilities throughout the implementation process Data not fully utilized High dependency on external resources and third party vendors, including the actual data source providers 9

10 Combining all relevant and new data sources Polling question #3 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 10

11 Other data types that are most commonly being integrated in decision management processes Other data types that have been considered in analysis and strategies for quite some time, are now being integrated into systems Mortgage & collateral Property value, property type, location factors, association type, Fixed, ARM, other, specific types Vertical Vertical lists, lifestyle / life event, and channel preferences Capacity & capital Many different measures used, starting with verified income, verified assets, type of income Relationship data Performance data from internal customer warehouse, balance transfer data 11

12 Combining all relevant and new data sources Polling question #4 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 12

13 Data access and the ability to bring data together allows our clients to... Data Credit bureau Demographic Lifestyle Transactional Etc. Review process across the organization and across the life cycle Manage account and customerlevel 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 13

14 Software makes the process easier by allowing our clients to... Data Credit bureau Demographic Lifestyle Transactional Etc. Software Data integrator Decision engine Rules engine 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 14

15 Our analytics and model development offerings provide best in class decision management capabilities... Data Credit bureau Demographic Lifestyle Transactional Etc. Software Data integrator Decision engine Rules engine Analytics / models Origination models Behavioral models Line increase strategies Pricing models 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 15

16 Our consultants have deep subject matter expertise and have helped our clients to... Data Software Analytics / models 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 16

17 Having the proper tools in place supports optimal decision management practices across the customer lifecycle Data Software Analytics / models + + Collections Improve collections efficiency Identify the right treatment for each customer Customer management Acquisitions Prospecting Optimize customer lifetime value More effectively target and penetrate key markets Maximize customer relationships Manage each customer for their value and potential Increase efficiency Manage credit quality Improve bottom line 17

18 Using additional data sources to support specific consumer lending implementations Integrating into operational decision systems Multiple data sources Attribute calculations Policy rules (risk evaluation) Decision management Consumer credit Total loan payment History of payment Overall indebtedness Premier Attributes SM Custom models Application rules: Age = 21 + Employment = Self employed, full-time Data aggregation Attribute calculations Decision processing Data verification Fraud Application data Validation Fraud detection Fraud detection Risk assessment Ability to pay evaluation Loan policy: Total balance > $100k DTI < 40% NDI $750/month Decision rules: Pre-approved General payment behavior = good, bad Consumer lending strategy outcome Approved or declined Continuous improvement on risk evaluation process 18

19 Case study for consumer lending supporting faster implementation and more efficient processing Objectives Needs 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) 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 singleplatform solution 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 Management SM Attribute Toolbox TM 19

20 Creating one complete decision management process for auto effectively powered by data and analytics Integrating into operational decision systems Multiple data sources Attribute calculations Policy rules (risk evaluation) Decision management Experian credit bureau data Experian Auto Check data Auto data Experian income model / data Credit score Total secured loan $ Total unsecured loan $ Other credit specific attributes for auto VIN number for vehicle history detail Vehicle value models to create LTV Income model for PTI and DTI Application rules: Age = 21+ Employment = Yes Ever delinquent = No Total outstanding balance = $150,000 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 Data aggregation Attribute calculations Model implementation Decision processing Credit decisions Approval, referral, decline Credit terms (APR, monthly payment, etc) Down payment Closing costs 20

21 Increase auto lending competitiveness through more effective models deployed quickly Objectives Needs Solution 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 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 Attributes SM (US & Canada) Strategy Management SM Attribute Toolbox TM 21

22 Combining all relevant and new data sources Polling question #5 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 22

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 23

24 For additional information, please contact: 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 24

25 May 6 9 The Phoenician Scottsdale, Ariz.