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© Deloitte Consulting, 2005 What To Do When You Cannot Use Credit? (Personal Lines) Cheng-Sheng Peter Wu, FCAS, ASA, MAAA CAS 2005 Special Interest Seminar Chicago September 19-20, 2005
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© Deloitte Consulting, 2005 2 Agenda The credit scoring revolution What to do when cannot use credit? Conclusions
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© Deloitte Consulting, 2005 The Credit Score Revolution
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© Deloitte Consulting, 2005 4 Personal Lines Pricing and Class Plans – History Few rating factors before World War II Explosion of class plan factors after the War Auto class plans: Territory, driver, vehicle, coverage, loss and violation, others, tiers/company… Homeowners class plans: Territory, construction class, protection class, coverage, prior loss, others, tiers/company... Credit scoring introduced in late 80s and early 90s
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© Deloitte Consulting, 2005 5 Personal Lines Credit Scoring – History First important factor identified over the past 2 decades Composite multivariate score vs. raw credit information Introduced in late 80s and early 90s Viewed at first as a “secret weapon” Quiet, confidential, controversial, black box, …etc “Early believers and users have gained significant competitive advantage!”
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© Deloitte Consulting, 2005 6 Early Believers’ Benefit from Credit Scores
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© Deloitte Consulting, 2005 7 The Current Environment Now everyone is using it: Marketing and direct solicitation New business and renewal business pricing and underwriting How to stay competitive if everyone is using it? Regulatory constraints: Many states have conducted studies on the true correlation with loss ratio and potential discrimination issues - WA study, TX study, MO study Many states have/are considering restricting the use of credit scores or certain types of credit information More states want the “black box” filed and opened
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© Deloitte Consulting, 2005 8 Some Facts About Credit Scores A composite score that usually contains 10 to 40 pieces of credit information Loss ratio lift is significant – a powerful class plan factor or rate tiering factor (2.0 ratio of worst 10 to best 10%) Benefits/ROI are measurable Lift curve can be translated into bottom-line benefit Blind test and independent validation can be done to verify the benefit
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© Deloitte Consulting, 2005 9 Loss Ratio Lift Curve 82 66 58 62 70 74 78 90 120 50 Credit Score Decile Loss Ratio
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© Deloitte Consulting, 2005 10 Credit Score Revolution - Segmentation Power
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© Deloitte Consulting, 2005 What to Do when Cannot Use Credit Scores?
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© Deloitte Consulting, 2005 12 What to Do when Cannot Use Credit One idea is to find “Credit Score Proxies” “Length of account” --- “Length of policies”, “Age of policyholders”? “Late payment” --- “Late payment in paying premium bill”, “Insurance lapse”? “Derogatory / Bankruptcy information etc” --- who has less chance to have derogatory or bankruptcy? etc…
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© Deloitte Consulting, 2005 13 What to Do when Cannot Use Credit Another idea is - why limited to “Credit Proxies” only, and go from credit scores to data mining and predictive modeling A credit score is just one example of an insurance predictive model The same methods used to build credit scores are used in data mining to build insurance predictive models – “Go Beyond Credit Models”. Broaden the usage of “predictive variables”
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© Deloitte Consulting, 2005 14 Go Beyond Credit Models The key is to use as much information as possible in a multivariate way Choice of statistical techniques is important, but the real key is the quality and breadth of predictive variables used. GIGO Actuarial/insurance knowledge is critical Untapped riches reside in many companies’ transactional records.
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© Deloitte Consulting, 2005 15 Data Sources We classify possible data sources into two groups Internal data sources: predictive information gleaned from the company’s own systems Regardless of how or whether it is currently used External data sources: predictive information available from 3 rd parties. Both credit and non-credit
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© Deloitte Consulting, 2005 16 Internal Data Sources Policy information Limits, Deductibles, Measure of exposure (# cars, #houses, #employees, $sales, premium size… Line-Specific information Driver, Vehicle, Business Class … Policyholder information Age, gender, marital status …
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© Deloitte Consulting, 2005 17 Internal Data Sources Customer-level information Transactional data Coverage, premium and loss transactions Billing information Correlation with credit Agent information A little creativity in using these data sources will go a long way!
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© Deloitte Consulting, 2005 18 An Example of a “Creative” Variable “Distance between Agent and Insured”: close by agents know you better! Insured’s address available in policy data system Agent’s address available in agency database Map two addresses into longitude and latitude using “geo-coding” tools Calculate the distance using “longitude-latitude distance formula”
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© Deloitte Consulting, 2005 19 External Data Sources Credit Predictive both for commercial and personal lines MVR – CLUE Zipcode/geographic information Rating territory Many different sources available The sky is the limit but Consider cost, hit rate, implementation, …etc
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© Deloitte Consulting, 2005 20 Types of Variables Generated Territory-level Demographic, weather, crime,...etc Policy / policyholder-specific Many traditional rating variables fall into this category Behavioral Less traditional – fits more neatly into data mining paradigm than classification ratemaking Credit, billing, prior claims, cancel-reinstatements…
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© Deloitte Consulting, 2005 21 How Many Variables? It is possible to generate literally hundreds of predictive variables Some will be redundant Some will not be very predictive Some will be somewhat predictive Some will be “killer” A good model can contain as few as 15-20 or as many as 60-70 variables Usually no single “ideal” model
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© Deloitte Consulting, 2005 22 Which Variables to Use? Choosing is a major part of the data mining process Use variety of exploratory statistical techniques Use prior modeling experience / actuarial knowledge Several considerations Actuarial / underwriting knowledge Client’s business needs Legal / regulatory considerations Data availability / cost Systems implementation considerations
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© Deloitte Consulting, 2005 23 In Our Experience…. Do “Go-Beyond Credit” PMs work? YES: non-credit predictive models are Valuable alternative to credit scores Flexible Tailored to individual companies Leverage company’s untapped internal data Comparable predictive power to credit scores And mixed credit / non-credit PMs can be even stronger
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© Deloitte Consulting, 2005 24 …But It’s Not a Walk Through the Park Challenges for PMs: IT resources constraints Project management Business process buy-in Success of system and business implementation Training and organizational change
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© Deloitte Consulting, 2005 Conclusions
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© Deloitte Consulting, 2005 26 Industry Trends How do companies try to stay competitive regarding the use of credit? How do companies prepare for increasing regulatory constraints? Industry trends Companies are developing modeling capabilities and pursuing various applications Companies are developing proprietary credit scoring models rather than buying “off-the-shelf” credit scores. Companies are also going beyond credit, to build scoring models that don’t rely solely on credit
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© Deloitte Consulting, 2005 27 Keys to Building Credit Alternative Models Fully utilize all sources of information Leverage company’s internal data sources Enriched with other external data sources Use large amount of data Employ systematic analytical process Use state-of-the-art modeling tools Apply multivariate methodology Disciplined project management
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