Using Business Intelligence to. Joe Greenwald SVP, CU Direct Corporation and Michael Cochrum Product Director, Analytics Products

Transcription

1 Using Business Intelligence to Improve CU Loan Performance Joe Greenwald SVP, CU Direct Corporation and Michael Cochrum Product Director, Analytics Products

2 Who is CU Direct? Established in 1994 as a CUSO Parent to CUDL, Lending Insights, Lending , CUDL Retail andvero Helps credit unions build strong and profitable lending portfolios Provides lending solutions through a national network and innovative technology

3 A Family of Brands CU Direct Brands and Solutions Lending Conferences Webcasts Online courses Roundtables Vehicle Lending & Sales Analytics Loan Originations i Retail Lending Vehicle Aftermarket CUDL3 LPMS LOS Lending Platform AutoSMART LI Consulting MAPS

4 Lending Insights Users Data from over 100 CU s Over 80 clients in past 2 years Clients in all asset categories Pricing starts at $195 per month

5 Lending Insights Vision As a CUSO CUSO, we care about the credit union system.

6 Lending Insights Vision Lending Solutions are only half of the equation. ti Supporting profitable lendingg p strategies is the other half. half

7 Lending Insights Vision Information is often lost in data. Lending Insights hl helps credit unions find the information they need to know.

8 14 yr. Changes in CU Portfolio Distribution & Loan Types Federally Insured State Chartered Credit Unions Other Member Loans 6.96% YE 1997 YE 2011 All Other Loans 0.48% Credit Cards 7.70% Other Unsecured 9.28% Leases Receivable 0.13% All Other 5.24% Credit Cards 5.21% Other Unsecured 3.60% NFG Student Loans 0.26% Mortgage Loans 35.82% Auto Loans 39.77% Real Estate 56.65% Auto Loans 28.92% Source: NCUA Aggregate Financial Reports

9 The Details Under the Surface oftoday s CU Portfolio More MBL More Participations More Indirect Auto Lending Increase in Lifestyle Lending Increase in Non Federally Guaranteed Student Loans What risks do these loans types bring that are not fully known?

10 What Credit Unions Should BeConcerned AboutToday Monitoring Risk Concentrations Recognizing and monitoring new products Determining effectiveness addressing risks Monitoring Interest Rate Risk Considerable long term Mortgage balances Growing balances of deposits subject to short term repricing Loan Portfolio Growth

11 Analytics that CU s are Required Didn tt Exist in 1997 to do That Didn Static Pool Reports Scorecard Validation Origination Dashboards Dealer and Branch Management Performance Dashboards Portfolio Distribution and Concentration Industry Benchmarks Credit Score Migration MDPA Risk Based Pricing Validation

12 Requirements & Benefits of Ongoing Multi Dimensional Portfolio Analysis Data warehousing of historic performance data in secure environment. Consistent reporting across all lending functions. Documentation of ongoing portfolio management. Identification of positive and negative trends and highest risk pools over time.

13 Multi Dimensional Portfolio Analysis Saves Time Multi Dimensional Portfolio Analysis reports are generated in minutes rather than hours or days. Money By early detection of issues, money saving strategies t can be employed earlier in lifecycle of the loan. Losses By providing an accessible solution, credit unions have access to information that will protect Net Worth.

14 12 Month Loan & Share Growth Share Growth continues to outpace Loan Growth. Credit unions continue to struggle to increase loans to share ratio. 8.00% 7.00% 6.00% 5.00% 4.00% 3.00% 2.00% Share & Loan Growth Loan Growth Share Growth 1.00% 0.00% Uncertainty continues to impact CU lending 1.00% strategies. 2Q Q 2012

15 Common Executive Challenges Today s credit union executive is faced withthese these commonchallenges: challenges: Increased Regulatory Scrutiny More analysis & monitoring Documentation Maintaining Profitability Aggressive competition IRR Strategic Planning Member loan demand Increasing loan growth New opportunities

16 Key Examination Points Internal Risk Assessment by product Documented Risk Management guidelines, processes and controls Comprehensive, effective and ongoing due diligence program Risk Based Pricing and Scorecard Validation Timely access to accurate data to support Strategic Planning Accurately projecting Loan Losses MDPA

17 Key Reporting Analytics Strategies Board and Exam Ready Reports Meet demands for information quickly. Multi Dimensional Analysis Credit unions can use over a dozen dimensions to slice portfolio data. Specific Dealer Performance Reports Delinquency and Loss by dealer and origination pool. Static Pool Identify highest risk pools in the y g p portfolio and early performance trends.

18 Key Reporting Analytics Strategies Capture Everything LTV at origination Origination channel Branch/Dealer, etc. Standardize Reporting / Analytics Use common data Standardize measurements Report t/ Analyze Regularly l Monthly (originations, delinquency, profitability) Quarterly (credit migration, losses, static pools)

19 Key Reporting Analytics Strategies Isolate Use Static Pools Segment by risk characteristic Analyze Don t ignore emerging issues Be open to being wrong React Use analytics to make timely changes Maintain profitability Lookfor new opportunities

20 Case Study 1 Hidden Delinquency $200 million credit union Loan delinquency under 1% Two year, high growth indirect lending program Examiners concerned that management was not aware of hidden delinquency in the indirect Portfolio Solution Delinquency by Product

21 Case Study 1 Hidden Delinquency

22 Case Study 2 Rising Revolving Losses $80 million SEG Based credit union High member credit card penetration MajoritySEG layoffs with sizable severance packages Bankruptcies rise within 12 months High losses in Credit Card Portfolio Solution Credit Score Migration Analytics

23 Case Study 2 Rising Revolving Losses

24 Case Study 3 Internal Risk Assessment $1+ billion credit union Large profitable Real Estate Portfolio Examiners concerned about exposure to risk based upon credit quality and LTV Request CU cease mortgage lending Credit union in bad position due to limited lending channels Solution Internal Risk Assessment by LTV and Credit Tier

25 Case Study 3 Internal Risk Assessment

26 Static Pool Analysis by Year of Origination

27 Static Pool Analysis by Year of Origination

28 Static Pool Analysis by Month of Origination

29 Cumulative Losses by Credit Tier (All)

30 Cumulative Losses by Credit Tier (2007)

31 Cumulative Losses by Dealer and Origination Year

32 Yield and Losses by Dealer or Branch

33 Credit Score Migration Analysis

34 Annualized Losses by Risk Tier and Loan Type Grouping of Loans by Risk Tier and Loan Type will help the CU determine what is being done well and what is not. Positive and negative trends can often be diluted in a larger pool.

35 Cumulative Losses by Origination Year and Loan Type Grouping of Loans by Loan Type and Origination Year helps the CU to easily see the effect that changes in strategy have on loan performance. Positive and negative trends can often be diluted by portfolio growth.

36 Delinquency by Branch and Origination Quarter Grouping of Loans by Branch and Origination Quarter helps the CU to adjust collections strategies early in a loan pool s lifecycle. Pinpointing specific loan pools allows the credit union to provide precision to collections strategies.

37 Monitoring Risk Concentration

38 Projecting Losses Using Cumulative Loss Ratios Step1 Establish cumulative loss to origination trends from existing loans. Primary Risk Tier Years 0 Years 1 Year 2 Years 3 Years 4 Years 5 Years All Charge Off Amount Ratio Cumulative Charge Off Amount Ratio Cumulative Charge Off Amount Ratio Cumulative Charge Off Amount Ratio Cumulative Charge Off Amount Ratio Cumulative Charge Off Amount Ratio Cumulative Charge Off Amount Ratio Cumulative A 0.17 % 0.74 % 1.12 % 1.32 % 1.38 % 1.38 % 1.38 % B 0.03 % 1.12 % 1.70 % 1.82 % 1.82 % 1.82 % 1.82 % C 0.49 % 2.26 % 2.26 % 2.26 % 2.26 % 2.26 % 2.26 % D 1.50 % 2.91 % 2.91 % 2.91 % 2.91 % 2.91 % 2.91 % All 0.18 % 0.92 % 1.32 % 1.50 % 1.54 % 1.54 % 1.54 %

39 Projecting Losses Using Cumulative Loss Ratios Step 2 Calculate projected losses on originated loans Originations Charge Off Amount Ratio Cumulative Years 0 Years 1 Year 2 Years 3 Years 4 Years Projected Total Loss Charge Off Amount Ratio Cumulative Projected Total Loss Charge Off Amount Ratio Cumulative Projected Total Loss Charge Off Amount Ratio Cumulative Projected Total Loss Charge Off Amount Ratio Cumulative Projected Total Loss A 8,649, % 14, % 64, % 96, % 114, % 119, r Primary Risk Tier B 3,411, % 1, % 38, % 57, % 62, % 62, C 4,004, % 19, % 90, % 90, % 90, % 90, D 2,108, % 31, % 61, % 61, % 61, % 61, All 18,172, % 66, % 254, % 306, % 328, % 333,279.83

40 Questions and Feedback