Our Members! Members are the Catalyst Members dictate everything we do Their needs and desires directly influence our balance sheet composition Which

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1 Member Demographics and Planning for the Future Mark DeBree, CFA VP ALM Services What drives our success? 1

2 Our Members! Members are the Catalyst Members dictate everything we do Their needs and desires directly influence our balance sheet composition Which in turn drive: Earnings Credit Risk Exposure Interest Rate Risk Exposure Liquidity Risk/Needs 2

3 Since members drive our success, how can we understand them better? Increase our Knowledge 3

4 Concept of Big Data Analytics Analyzing large data sets to identify hidden patterns, trends, customer preferences, and any other information that can be used to improve decision making Common sources include: Social media content, customer surveys, spending patterns, life cycle stage, geography, demographics, etc. Data analytics enable Predictive Modeling Using relationships uncovered through data analytics to forecast probable outcomes or actions Offering customized products or services based on what members are likely to need based on their unique profile Fraud detection is a form of predictive modeling 4

5 Amazon Use of Big Data Amazons product offering is overwhelmingly massive Amazon uses Big Data to fine tune recommendations for each user Able to predict what you want to buy! Once they know what you want, they begin persuading you to buy it Analytics enable Amazon to build a picture of you and then offer products that people with similar profiles have searched for or purchased Building you includes Types of products you browse and/or purchase Shipping address Enables identify local trends, potential income level Census data for demographics Amazons Insight Amazon found that tailor choices improved sales Too many choices and too little guidance decreased purchasing decisions 5

6 The Credit Union Application We have enormous amounts of data We know our members What they buy Where they live Annual Income Where they travel Age We can use data to create pictures of our members to estimate future behaviors The Credit Union Application Predicting behavior can be extremely valuable with a variety of use cases Common use cases for data analytics Fraud Detection Customer Segmentation/Personalized Marketing Risk Management Source: Forbes Magazine 6

7 Fraud Detection Outlines normal trends in member behavior to identify or flag transactions that occur beyond normal Flagging suspicious or unusual behavior in real time by monitoring Types of transactions Size of transactions Preferred stores Purchase locations To be able to identify transitions outside the norm, you must have sufficient data to identify what the norm is Customer Segmentation Customer Segmentation Grouping members based upon multiple factors such as age, income, zip code, preferred stores, purchase sizes, debt levels, credit card reliance, etc. Improves the understanding and picture of each member to allow for: More targeted marketing Improved service 7

8 Customer Segmentation Example Analysis of Member Deposits What is our balance distribution? How are our accounts distributed? What is the age of new members? When do our members close their accounts? Taking a Life Cycle Approach 8

9 Deposit Analytics 80,000 70,000 60,000 Balances by Age $ (in thousands) 50,000 40,000 30,000 20,000 10, Age Bracket Checking Savings Money Market IRA US Median Net Worth 250,000 Median Net Worth by Age 200, , ,000 50,000 - < >75 Source: US Census Bureau 9

10 Deposit Analytics 6,000 # of Accounts by Age 5,000 4,000 3,000 2,000 1, Checking Savings Money Market IRA Deposit Analytics Age at Account Open Number of Accounts 20,000 18,000 16,000 14,000 12,000 10,000 8,000 6,000 4,000 2, Age Bracket Checking Savings Money Market IRA 10

11 Deposit Analytics 30% Age at Account Closure 25% Percentage of Accounts 20% 15% 10% 5% 0% Depositor Age Checking Savings Money Market IRA Member Deposit Observations Takeaways: The overall balance distribution of deposits is healthy As expected, concentrated with members years of age, but healthy allocation to years Consistent with national trends Large number of accounts with younger accounts Average age of depositors is 50 years of age Successful in generating new accounts with younger members Overall industry concern in this area Rate of account closures is mostly consistent across age criteria 11

12 Personalized Marketing Consumer Debt Patterns 3,500 Aggregate Debt by Age (in billions) 3,000 2,500 2,000 1,500 1, Source: Federal Reserve Bank of New York age age age age age age

13 Consumer Debt Patterns 3,500 Aggregate Debt by Age by Type (in billions) 3,000 2,500 2,000 1,500 1, age age age age age age 70+ Other Student Mortgage 356 1,775 2,398 2,255 1, HELOC Credit Card Auto Source: Federal Reserve Bank of New York Consumer Debt Patterns 4,000 Aggregate Debt by Age (in billions) 3,500 3,000 2,500 2,000 1,500 1, Sum of age Sum of age Sum of age Sum of age Sum of age Sum of age 70+ Source: Federal Reserve Bank of New York Total 13

14 Overall Observations Debt allocations are largely consistent over time Recent shift towards increased debt being assumed in the age group Mortgage is clearly the largest portion of consumer debt Auto debt peaks in the age group but remains far less than mortgage debt Credit cards show a similar trend Home Equity debt peals in the age group Potential Opportunities (Combining Data Sets) Strong new membership growth in younger members may allow for opportunities and deeper ties Potential product offerings could include Targeted loan pre-approvals Financial planning/investment guidance Large balances in older membership Less need for loans Estate planning desirable Family relationship benefits to attract and retain heirs 14

15 Big Data and Customer Segmentation Data analytics can provide valuable information to credit unions as we seek to understand our members What happens on our members life journey What kind of loans will our members want based on their life stage Effectiveness of marketing campaigns Risk Management Applications 15

16 Risk Management Applications Data analytics can enhance the overall risk management process at credit unions Improving accuracy of underlying modeling assumptions: Maturity estimates of deposits Rate of decay (runoff) of deposits Cash flow forecasting Risk Management Applications Beginning with the same Customer Segmentation data analytics Incorporate additional factors Rate of account closures Retention of accounts through the life cycle Can be simple or complex including estimates for relocation, job losses, etc. Mortality 16

17 Deposit Analytics 80,000 70,000 60,000 Balances by Age $ (in thousands) 50,000 40,000 30,000 20,000 10, Age Bracket Checking Savings Money Market IRA Deposit Analytics Age at Account Closure 30% 25% Percentage of Accounts 20% 15% 10% 5% 0% Depositor Age Checking Savings Money Market IRA 17

18 Deposit Analytics Months Estimated Remaining Life Depositor Age Checking Savings Money Market IRA Impact of Deposit Analytics on Deposit Lives Historical Look Share Type Historical Final Maturity Monthly Decay Rate Annual Decay WAL (years) Checking % 26.51% 3.22 Savings % 22.62% 3.90 Money Market % 14.93% 5.80 IRA % 23.52% 3.72 Weighted Average % 23.09% 3.80 Share Type Forward Maturity Estimate Monthly Decay Rate Annual Decay WAL (years) Checking % 26.51% Savings % 22.62% 2.15 Money Market % 14.93% 2.13 IRA % 23.52% 1.75 Forward Look Weighted Average % 23.09%

19 Deposit Weighted Average Life 7.0 Deposit WAL Estimates Years Checking Savings Money Market IRA Weighted Average Historical Forward NERA Risk Management Applications Strong value from a risk management perspective Better data improves overall risk management Asset/Liability Matching Improved clarity of liquidity position Clearer picture of potential deposit withdrawals/account closers for the future 19

20 Enhanced Balance Sheet Matching WAL Asset - Liability Matching Years Money Market vs. Auto IRA vs. Unsec. Loans Loans Savings vs. HELOC Checking vs. 1st Mtg Fxd Deposit WALs Asset WALs Deposit Analytics Estimated Remaining Life Months Depositor Age Checking Savings Money Market IRA 20

21 Knowledge is Power Questions Mark DeBree VP ALM Services 21

22 Auto Loan Debt Trends 300 Aggregate Auto Loan Debt by Age (in billions) Sum of age Sum of age Sum of age Sum of age Sum of age Sum of age 70+ Source: Federal Reserve Bank of New York Auto Mortgage Loan Debt Trends 3,500 Aggregate Mortgage Loan Debt by Age (in billions) 3,000 2,500 2,000 1,500 1, Mortgage Sum of age Sum of age Sum of age Sum of age Sum of age Sum of age 70+ Source: Federal Reserve Bank of New York 22

23 Student Loan Debt Trends 500 Aggregate Student Loan Debt by Age (in billions) Student Sum of age Sum of age Sum of age Sum of age Sum of age Sum of age 70+ Source: Federal Reserve Bank of New York HELOC Debt Trends 250 Aggregate HELOC Debt by Age (in billions) Sum of age Sum of age Sum of age Sum of age Sum of age Sum of age 70+ Source: Federal Reserve Bank of New York HELOC 23

24 Credit Card Debt Trends 250 Aggregate Credit Card Debt by Age (in billions) Credit Card Sum of age Sum of age Sum of age Sum of age Sum of age Sum of age 70+ Source: Federal Reserve Bank of New York 24