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I NFORMATION P OLICY I NSTITUTE

I NFORMATION P OLICY I NSTITUTE. Predictive Attributes of Alternative Data: A Million Little Pieces. By Michael Turner, Ph.D. CDIA Annual Conference Scottsdale, AZ January 26, 2006. Introduction. What is “Alternative” or “Non-Traditional” Data? Energy and Water Utility Payments

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I NFORMATION P OLICY I NSTITUTE

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  1. INFORMATION POLICY INSTITUTE Predictive Attributes of Alternative Data:A Million Little Pieces By Michael Turner, Ph.D. CDIA Annual Conference Scottsdale, AZ January 26, 2006

  2. Introduction What is “Alternative” or “Non-Traditional” Data? • Energy and Water Utility Payments • Landline and wireless phone bills • Auto liability insurance payments • Rental payments (especially apartments) • Remittance payments and stored value cards • And, certain types of retail payments

  3. Introduction What is alternative data being used to predict? • The probability of delinquency and default on a loan • Creditworthiness, credit capacity, and credit risk

  4. Introduction: Caveats We know that some alternative data is predictive: • Mortgage lenders using it for underwriting loans for past 15 years (Fannie and Freddie) • Mortgage insurers collecting non-traditional data for insuring affordable housing loans for past 15 years (Genworth) • Because credit bureaus and modelers have told us so

  5. Introduction: Disclaimer We are neutral with respect to scoring models and data collection techniques: • Standard model—reporting alternative data to major credit bureaus as tradelines • Aggregation model—using data from niche aggregators in scoring model (FICO) • Self-reporting model—data subjects select which trades to report, credit bureau verifies (PRBC)

  6. Introduction Why care about alternative data? • 35 to 54 million Americans “unscorable” • Primarily low income, immigrants, elderly, and ethnic minorities • Legislators interested • FACTA §308-309 mandated examination • 22 members at May 2005 hearing • Huge payoff for players with viable product in market

  7. Methodology Our qualitative study was designed to: • Define the parameters of alternative data • Provide analytical framework for understanding which data sets are most useful in bringing no-file and thin-file Americans into credit mainstream • Assess barriers to the sharing of alternative data • Economic • Technological • Regulatory

  8. Methodology Our primary concern is with data that may help those outside the U.S. credit system access affordable mainstream sources of credit • Other data sets may be more promising in different contexts (e.g. emerging markets with no prior credit reporting history) • Some data sets may not be assessed as promising today, but may have potential • Ultimately, value of alternative data set as a predictor of default is an empirical question

  9. Methodology Assessed the promise (useful and practical) of each data type along three key dimensions • “Cash-like” vs. “Credit-like” • Coverage • Concentration

  10. Key Finding 1 Energy utility and telecom data is likely to be the most useful and practicalalternative data for reaching Americans with little or no information in their credit files.

  11. Key Finding 1 Useful: • Virtually all Americans purchase utility services • Transactions are more “credit-like” than “cash-like”

  12. Key Finding 1 Practical: • Sectors are concentrated— relatively few data furnishers (billing systems) need to be reached

  13. Benefits of Reporting Borrowers likely to receive increased access to credit, and potentially higher limits and better terms • Thin-file population expected to receive greatest lift to credit score • Younger Americans can build credit history earlier • Impact on credit score for Americans with thicker files should be relatively minor

  14. Benefits of Reporting Furnishers may benefit along two fronts: • Reporting as a “stick” may result in reduction in delinquencies and defaults • Reporting as a “carrot” could provide competitive advantage and help build brand

  15. Benefits of Reporting Reporting alone is insufficient, must communicate with customers to capture benefit • Nicor Gas reported 20% reduction in delinquencies and defaults after reporting (notified customers) • Verizon’s cash flow improved significantly; ramped up from 3, to 7, to 20 million tradelines reported in less than 1 year. • WE Energies of Wisconsin reported substantial reduction in arrearages

  16. Benefits of Reporting Lenders will receive two primary benefits: • Improved ability to distinguish “goods” from “bads”, especially in thinner file consumers • Increased ability to enter into new markets (underserved)

  17. Barriers to Reporting Technological barriers to reporting: • Complex billing cycles (footprint dependent) • Legacy IT systems • Metro 2 (reporting) and eOscar (verification) have reduced barriers

  18. Barriers to Reporting Economic barriers: • Competition (fear of poaching) • Compliance costs—FCRA data furnisher obligations • Customer service costs from lenders scaring customers substantial • Exposure to litigation (impact on market cap, private right of action in milieu of hyper-sensitivity to data privacy and security)

  19. Barriers to Reporting Economic barriers are real: • PAID/EEI survey—many stopped reporting because perceived costs greater than perceived benefits (2005) • AGA—informal survey, most members reported no benefit (2005) • Findings suggestive, not conclusive • Didn’t ask about customer communication • Small sample size

  20. Barriers to Reporting Two regulatory barriers (2005 Information Policy Institute/NARUC survey): • Three states have statutory prohibitions (CA, NJ, OH) • Regulatory uncertainty at the state level

  21. Quantitative Research Sample credit files with 1 or more non-traditional tradelines Drawn from two points in time to measure performance Use commercial grade generic scoring model(s), mortgage scoring model(s), credit card model(s)

  22. Quantitative Research Will measure impact of non-traditional data on: • Model fit (KS statistic) • Score distribution • Error rates • Access to credit • Loan performance

  23. Quantitative Research Supporters include: • CDIA • TransUnion, Experian, Equifax • Bank of America, GE Consumer Finance, HSBC • SAS • Hispanic National Mortgage Association • Brookings Institution

  24. Related Research Use of non-traditional data in commercial credit scoring models for small businesses: • Evidence shows decisions made based upon both business plan data and proprietor’s credit report superior to business plan alone • Info Pol Inst & Brookings co-sponsoring symposium on topic on March 9th

  25. The Road Ahead Study will be released Spring 2006 Massive data furnisher outreach needed • EEI/AGA joint conference March 2006 • Info Pol/Brookings/EEI/AGA/NARUC focus on the states to remove barriers New telecom law may preclude sharing of customer data—must act Better mousetrap will be built if data is brought online—challenge is to persuade furnishers

  26. Conclusion Some alternative data is promising now, BUT potential won’t be realized overnight • Vast majority of potential data furnishers currently don’t report • Real barriers exist Pervasive reporting of alternative data creates win-win-win scenario • Harmony of interest for coalition building

  27. INFORMATION POLICY INSTITUTE 100 Europa Drive, Suite 431 Chapel Hill, NC 27517 www.infopolicy.org Phone: (212) 629 -4557

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