Improving Customer Retention by Identifying Characteristics of Auto Loan Prepayers using MARS. John Trimble Wells Fargo

Transcription

1 Improving Customer Retention by Identifying Characteristics of Auto Loan Prepayers using MARS John Trimble Wells Fargo

2 An Appeal of Data Mining Efficient identification of: Most important predictors Local relationships Offers ability to target different strategies: Within and Without local segments MARS and other DM techniques have these capabilities

3 Topic of This Presentation Prepayments in 1 st 18 Months Loans paid off prior to their maturity date Imposes costs on the lender Increases administrative burden More dealer discounts, marketing costs, general admin burden Imposes reinvestment risks: May be at lower rate if rates falling May take longer to match with borrowers if rates rising May have to accept lower credit quality if occurs in midst of market share battle If can predict prepayments, can devise mitigating strategies

4 Rate of Voluntary Prepayment Approximately 2-3% of Loans Per Month 30% Prepay Over 18 Months 60% Prepay Over Life of Loan Includes insignificant prepayments Payoff on Next to Last Payment Payoff With Small Balance

5 About Wells Fargo Auto Finance

6 Auto Loan Originations Automated Approval Car Dealer Wells Fargo Lender #2 Lender #3

7 Wells Fargo Portfolio $14+ billion in loans and leases (retail and wholesale) 840,000+ Customers Approve new loans with credit scoring models to large degree Model assesses risk of approx. 1.8 million individuals per year (800,000 applications)

8 Characteristics of Prepayers Using MARS to Understand Who Prepays Auto Loans

9 Prepayment Potential Causes Natural Hazards Road hazards Fire, Flood, Theft Voluntary Causes Advantageous refinancing opportunities Vehicle replacement Interest rate levels Manufacturer incentives Retention Could be lack of attractive financing alternatives

10 Insurance/Hazard Prepayment Likely Difficult to Predict By Nature Due to Random Events Likely Difficult to Predict Predicting Payoff More Difficult The Greater the % of Payoffs Due to Natural Hazards Ins Payoffs May Mask Payoffs Motivated by Personal Interests Following three slides seem to bear this out

11 Predicting Prepayments Available Attributes Over 900 Credit Bureau and Application Attributes on 100,000+ Records Available Examples include: Credit Inquiries Loan Maturity Recent Delinquencies Credit Utilization # Accounts Debt Burden Down Payment

12 MARS Prepayer Profile - Loan Term + Credit Union Accts + Recent Account Openings - Carries Revolving Balances - Low Revolving Debt + ACH payment + Poor or Thin Delinquency History + High Percent Down Basis Functions=30, Interactions=2 +(-) Indicates Positive (Negative) Impact on Prob Payoff

13 Prepayer Logistic Model MARS vs Descending FICO Score Lorenz Curves MARS % of PAYOFFS FICO Barely Exceeds Random Ranking % of OBS

14 Insurance Prepayers Using MARS to Help Identify Them in the Sample

15 Identifying Ins Prepayers Information Contained in Customer Service Comments Files Need to Write Script to Search These Files No Standardization Difficult to Write Scripts C/S Reps Do Tend To Follow Personal Habits Exploit This To Identify Ins Prepays Involves Visual Inspection of Comment Files Basis Functions=30, Interactions=2 +(-) Indicates Positive (Negative) Impact on Prob Payoff

16 Customer Service Comment Need to Classify Prepays from Info Like This cust c/i to say she got coupon book in mail today and will mail pmt today, will call back if she decides to do ach. CCI WE REFINANCED VEH CUST STATES THERE IS STILL A BAL REMAINING ASKED CUST IF SHE CANCELLED HER WARR W/ THEM CUST REALIZE WHY SHE WILL CANCEL WARR. W/ GEICO. DAGWOOD says this is a loan that she recieved by mail. She was w/ Wachovia Bank before and says we haven't paid off her loan in full, transfd to on-line services # since this is considered internet loan for further assistance. poe sttd no DAGWOOD Bumstead wrks there deleting (xxx) xxx-xxxx tt DAGWOOD, sd she is changing jobs, starts new job 2/18, does not have new bp#, cust auth aps 2/15 for jan iao fee, included lc, check #176, con # 111win010101, ver demos, sd next payment will probably be late but will get it in asap 40L to 30i all late fees paid eff 3/28/02, then 36L all late fees outstanding, late fee code should be '0' per ARSI late fee project CS ACTIVITY ONML REVIEW DATE BALANCE HP CS ACTIVITY OBNC REVIEW DATE BALANCE TT DAGWOOD SD CAN T MAKE PYMT TIL NEXT TWO WEEKS RFUSED POST DATED CK ADVS FUR COLL CS ACTIVITY OBWP REVIEW DATE BALANCE BP (xxx) xxx-xxxx TT FEMALE DAGWOOD NO LONG EMP AT cust CI INQ ON ach DAGWOOD ci, wanted to make pymnt via phone, adv her that slc is handling gave #(xxx)-xxx-xxxx... CS ACTIVITY IBPA PROMISE REVIEW DATE BALANCE **SPEED PAY/chk#193 / $ FEE / *** IB TT DAGWOOD VI CS ACTIVITY OBPA PROMISE REVIEW DATE BALANCE ***APS/325.29/6/12/22*** HP TT DAGWOOD VI CS ACTIVITY OBNC REVIEW DATE BALANCE HP T DAGWOOD SAYS SHE IS RUNNING BEHIND HASNT DONE BILLS WILL DO IT AND THAN SHE CAN GET US OUR MONEY. V/I CS ACTIVITY IBPA PROMISE REVIEW DATE BALANCE ***APS/325.29/071602/CK#111/*** TT DAGWOOD VI CS ACTIVITY OBPA PROMISE REVIEW DATE BALANCE ***APS/325.29/071622/CHK#111/*** HP TT DAGWOOD V/I Good Till 7/20/22 franklin from ford dealership IL Data as of 07/20/22 Daily Dollar Rate: Payoff Quoted: $14,281.07, Balance: $14, CS ACTIVITY ONML REVIEW DATE BALANCE P/O RECVD FROM: DAFFY DUCK AUTO PK ADDRESS: 1111 DRYLAKE BLVD, USELESS PARK FL zzzzz MAKER: SUNTRUST #zzzzz $ \P/O $ O/P $ N CS ACTIVITY ONML REVIEW DATE BALANCE CS ACTIVITY IBPO REVIEW DATE BALANCE IB TT DAGWOOD SD THAT DEALERSHIP IS SENDING IN THE PAYOFF VI WAAS TRADED ON TRANSFER From CLT001 to TPPP01 PAID FILE PROCESSED Names, dates, locations and identifying numbers have been altered to preserve account holder privacy

17 Strategy to Identify Ins Cases Visually Inspect a Sample of Comments Classify into Ins Payoff and Non-Ins Payoff Build MARS Model of Non-Ins Payoff Classify Records Search for Ins Company Names Helpful Strategy But Still Need Substantial Visual Inspection to Build Viable Script to Accurately Classify Records

18 Insurance Figures in Most Prepayments 92% of prepays involve insurance payment Ranges from total loss to Partial loss buyer completes the payoff A very high % likely involve vehicle replacement Remaining 8% most likely related to: Vehicle replacement and New financing Cheaper rate (lower financing cost) Extension of term (lower monthly payment)

19 Insurance Prepayers Using MARS to Predict Insurance Prepayers

20 MARS Ins Prepayer Profile - Term of Loan Longer Term - Carries Revolving Balances - Credit Utilization - Low Percent Down Pmt + High Percent Down Pmt + Co-applicant s delinquency record - Co-applicant has thin credit file + Credit Seeking Activity - Carries Revolving Balances - Tenure of trades Basis Functions=30, Interactions=2 +(-) Indicates Positive (Negative) Impact on Prob Ins Payoff

21 Ins Prepay Logistic Model MARS vs Descending FICO % of INS PAYOFFS Lorenz Curves FICO MARS Models Barely Exceed Random Ranking % of OBS

22 Non-Insurance Prepayers Using MARS to Predict Non- Insurance Prepayers

23 MARS Non-Ins Prepayer Profile Upper 25% Credit Quality + Credit Quality Bottom 75% Credit Quality + Credit Utilization + Credit Seeking Activity + Monthly Pmt for Shelter Co-applicant has weak credit record + Credit Seeking Activity - Credit Utilization + Applicant s Age - Down Pmt Basis Functions=30, Interactions=2 +(-) Indicates Positive (Negative) Impact on Prob Non-Ins Payoff

24 Non-Ins Prepay Logistic Model MARS vs Ascending FICO % of NON-INS PAYOFFS Lorenz Curves MARS % of OBS FICO MARS Predicts Best FICO Rank Orders, But Overpredicts

25 Insurance vs Non-Insurance Prepayers Using MARS to Predict Which

26 MARS Prepayer Profile Non-Ins vs. Ins Upper 35% credit quality + Credit Quality Bottom 65% Credit Quality + Credit Quality + Early in Life of Loan - Later in Life of Loan + Loan-to-value ratio > 130% Co-applicant Has Weak Credit Record - Credit Utilization - High Down Pmt + Low Down Pmt Basis Functions=30, Interactions=2 +(-) Indicates Positive (Negative) Impact on Prob that a Payoff will be Non-Ins vs Ins

27 MARS Prepayer Profile Non-Ins vs. Ins (continued) Co-applicant Has Weak Credit Record + Early in Life of Loan + Age of Applicant + Credit Seeking Activity + Carries Revolving Balances + Longer Term of Loan Basis Functions=30, Interactions=2 +(-) Indicates Positive (Negative) Impact on Prob that a Payoff will be Non-Ins vs Ins

28 Non-Ins vs Ins Logistic Model PAYOFFS: MARS vs Ascend. FICO % of NON-INS PAYOFFS Lorenz Curves MARS % of PAYOFF OBS FICO MARS Predicts Best, incl. Timing FICO Rank Orders, But Overpredicts

29 What Have We Learned? About the Motivations for Prepayment

30 Motivations for Prepayment Vehicle Replacement is #1 Reason Vast Majority Involve Substantial Ins Payment Remainder of replacements are likely vehicle upgrades Avoid rising maintenance costs or Upgrade vehicle status Other Motivations Not Easily Determined Reduce Size of Payment Switch to Alternative Financing (e.g. Home Equity) Desire and capability to own outright

31 What Have We Learned? About Who Prepays

32 Who Prepays Virtually Anyone if Insurance Payment Involved For Non-Insurance Prepayment Bottom 75% of Credit Quality Distribution most likely Otherwise Ascending NOT Descending FICO Would Have Rank-Ordered Non-Ins Prepays Top 25% Credit Quality Distribution is small segment When Co-Applicant Has Weak Credit Older owners more likely to pay off Those in weaker financial condition more likely to pay off Higher debt utilizers Those who sought easier terms (e.g. longer term, lower down pmt)

33 What Have We Learned About When Prepayment Occurs

34 When Prepayment Occurs Early in Life of Loan More Likely If Borrower(s) in Lower 75% of Credity Quality If Co-Applicant Has Weak Credit Later in Life of Loan Becomes Less Likely If Borrower(s) in Lower 75% of Credity Quality and If Co-Applicant Has Weak Credit And if Life of Loan Has Passed Early Payoff Peak Don t Know About Top 25% Credit Quality Would Expect A Pattern Either Early or Late

35 Conclusions About Possible Mitigation Strategies

36 Conclusions About Possible Mitigation Strategies Predicting Payoff (All Reasons) at Origination Predictive Capability Too Weak Strategy Based on This Likely Unsuccessful Predicting Non-Ins Payoff at Origination Significant Capability To Predict Strategy Likely to Be Beneficial Further Identifying Reason For Payoff in Sample Likely Enhance Ability To Build More Predictive Model