New Trends and Discoveries in Big Data that will Help you Credit Union Compete. Bill Goedken CPA CMA CGMA President and CEO

Transcription

1 New Trends and Discoveries in Big Data that will Help you Credit Union Compete Bill Goedken CPA CMA CGMA President and CEO

2 What is Big Data? How can it help the Credit Union? Recent studies and data trends Best practices at credit unions - and even the banks! Outline Member and employee Generations - what do they want in a credit union? What does this mean for the next decade? How to prepare the credit union - now. Give you multiple growth, growing earnings and expense reduction ideas

3 Big Data Did you Know? 1. Facebook they know there are 3 million couples currently engaged to be married in the USA. What if you had a list of those people in your market? 2. Canadian Tire people who buy Mobil1 oil are significantly better credit risks than those that bought generic motor oil. Is Wal-Mart or Target gaining a potential advantage? 3. idea5 5 out of the top 10 power rated websites we studied are headquartered around major universities. Can we use our internal big data to gain younger members in our market?

4 Item Employees per Million of Assets Computers 1 Employee = $150,000 in assets Hand Ledger, Early Posting machines 1 Employee = $1 million in assets 5 Terabytes = all US financial institution records 1 Employee = $4 million in assets 560,000 Terabytes = all US financial institution records Branch Drive Thru s Experimental Yes used often Slowly declining ATM s? No Yes Yes Primary Delivery Channels Primary mode of Payment Main office, Mail, Some Telephone Branch, ATM s, Mail Website, Branch, ATM, Mobile, Call Center, Mail Cash, Check Check, Credit Card Debit Card, Credit Card, Electronic Transfers, Check Size of Call Report 2 pages - maybe 6 pages 25 pages (plus instructions)

5 What is Big Data? Noun: Big Data refers to extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. Examples: Weather Phone use (GPS location, length of time, age) Twitter feeds (subject matter, timing) Credit card purchases and patterns ATM statistics (time of use, type of transaction, customer profile, etc.) Surveys and studies are they Big Data? OCC Canary Project is using Big Data!

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7 What is Big Data (Continued) Big Data is similar to Transportation The amount of Big Data is increasing almost exponentially There were 5 Exabytes of information created between the dawn of civilization through 2003, but that much information is now created every 2 days. Eric Schmidt, of Google in (It is now nearly every day in 2015) The hype is that companies (including financial institutions) can use Big Data to gain better insight into relationships and patterns: 1. Buying patterns (by generation, location, income levels, etc.) 2. Use patterns 3. Relationships of data (e.g. weather to buying patterns) 4. Predictive modeling in the near future (if X happens, Y will happen with a degree of certainty

8 What is Big Data (Continued) Big Data can be numerical or text or documents, etc. Big Data can be public or internal, structured or unstructured Because of its size, Big Data is usually stored in the Cloud Cloud is term to describe servers usually at a third party location stored/computed usually by a third party But Your credit union has Big Data in your shop right now!

9 Public Big Data (usually in the Cloud ) Internal Big Data (usually on internal servers) Structured Semi-Structured Unstructured Bureau of Labor Statistics Economic Data Interest Rates Commodity Prices Tons of Government Data Census Data Competitive Data Industry Data Twitter Feeds Google reviews of your institution Facebook Comments/Likes Blog Posts Web Pages Budgets ProfitStar and other ALM programs Member Accounts and Activity Internal Statistics (Web hits, ATM transactions, etc.) Loan Applications Most internal spreadsheets Internal reports Memos Most Documents s Web pages Patterns of Members (applications, open/closed, etc.) Phone records Success of relationship pricing, etc.

10 Big Data for Financial Institutions Advantage: Financial Institutions have massive amounts of Internal Big Data. Getting to it easily may be an issue Finding Common examples of current use of Big Data at financial institutions: 1. Fraud detection (based on patterns, type, etc.) 2. Trends in delivery channels (ATM s, Call Center, Web/home banking, branches) 3. Marketing and CRM trends (customers, promotion campaigns, loan volume, etc.) 4. Comparing to Peer Groups 5. Predictive modeling in the near future (liquidity, ALM position, etc.) 6. Notice most of this deals with internal data. 7. Does it change your Strategic Thinking? The next step is to go beyond the common uses and combine with External Big Data Better internal to internal External to external Or external to internal Make it meaningful, cost effective, and give you a competitive advantage There is in the data!

11 Case 1: Internal Pattern Discovery Discoveries from Big Data. 1. Bank discovered there was a major blip in home banking account activity around 2 am 2. Target marketed specific web page and instant message ads about overdraft protection during the time in question 3. Over 400+ sign ups for overdraft protection in a 90 day span 4. Fee income increased $28,000 in the first year 5. Extra bonus discovered by a Gen X employee as a tangent to another project

12 18 Account Activity per Hour

13 Discoveries from Big Data. Case 2: Internal to Internal (with backup from External): 1. CU Board of Directors were pressuring management to open a brick and mortar branch in a certain community, and NOT close any branch 2. Management had few statistics to back up their claim that more resources should be devoted to electronic delivery channels AND close the one dead weight branch 3. Gathered internal stats (Big Data) on delivery channel changes. They followed national statistics 4. Taught B of D the external studies on delivery channel changes 5. Opening a much smaller branch and closing one in Annual savings projected to be $300,000+ in the first year alone. 6. Three year savings over $1,000, Part of savings going to electronic delivery channels 8. Internal stats are now part of their normal data gathering

14 Discoveries from Big Data. Case 3: Internal to External Discovery 1. Credit union wanted to know if there was a correlation between local unemployment or other economic data and various consumer loan applications and approvals. 2. Mortgage, HELOC, Auto (new and used), student, etc. 3. Discovery: Only a partial correlation to Mortgage or HELOC applications or approvals. 4. Autos had a medium/high correlation (inverse relationship). 5. RV and Boat Loans had a high correlation (inverse relationship). 6. They also found when unemployment went up, the effect was more immediate. When unemployment went down, the lag effect was more pronounced. 7. Local unemployment is watched carefully with promotion campaigns ready to go. 8. Discovery: loan sales can be effective when unemployment rises, but only in the beginning of the upslope. 9. Discovery: approval rates can be reasonably predicted by loan type as it relates to unemployment. 10. Discovery: Delinquency by loan type has a certain lag effect as it relates to local unemployment.

15 Tampa Bay-St. Petersburg-Clearwater: Delinquencies vs Unemployment Sources: idea5, NCUA.gov, FDIC.gov, BLS

16 X Financial Institution Consumer Loan Demand vs Tampa Bay Unemployment Sources: idea5, NCUA.gov, FDIC.gov, BLS

17 How do you know if Big Data can help 1. Remember 95% of External Big Data is useless to the credit union (does NOT change your Strategic Thinking.) 2. Another 3% may be useful, but is very difficult to get, is expensive, and make sense of. 3. Concentrate on the remaining 2%. 4. Ask yourself the top 10 questions you are trying to answer. Involve your management team. What keeps you up at night? First look at Bang for the Buck internal trends. Then internal to internal, and internal to external. Remember time and cost are considerations. 5. Later spreadsheet to help you formulate the questions. 6. It is the questions you don t ask which might get you into trouble or have a lost opportunity.

18 The Future of Employees, Members, Community Traditionalists Baby Boomers Generation X Generation Y Generation Z

19 Consumer Delivery Channel Trends Branches as a #1 preferred method of banking has declined in all age sectors However Branches are a solid #2 Most respondents (53%), indicate they will NOT doing banking with any institution without a physical branch presence. ATM as the #1 preferred method has also declined PC s/internet/home Banking have become #1 in preferred method in ALL age categories. This includes tablets. Mobile banking has been adopted more by the age group Mail and telephone use are rising for the 55+ age group Conclusion Remote Banking is taking over, but most consumers still want a physical branch network of some type

20 Future Direction of Financial Services + Website/Home Banking Branch Network Supporting Cast: ATM s, Mobile, Remote Deposit Capture, Call Center, Mail

21 Preferred Banking Method: Ages Branches Internet ATM Mobile Telephone Mail Unknown % of respondents who indicated their #1 preferred banking method Source: ABA

22 Preferred Banking Method: Ages Branches Internet ATM Mobile Telephone Mail Unknown % of respondents who indicated their #1 preferred banking method Source: ABA

23 Preferred Banking Method: Ages Branches Internet ATM Mobile Telephone Mail Unknown % of respondents who indicated their #1 preferred banking method Source: ABA

24 New Trends in Banking 2015 and on Delivery Channels a very large discussion/strategy rethink is happening around the country. The two that dominate Website (and Home Banking), and Branches Supplemented by: People, ATM s, Mobile, Mail, other services (like remote deposit capture.) THIS WILL CHANGE! Question is: Which is the best combination? Websites continue to improve but have a long way to go. And they will never stop improving. The future - Touch your customers lives more than money. You touch their life.

25 Website/Home Banking Trends At an average financial institution - Now has twice as many visits as a branch. Do you track it? (Big Data!) All age groups are using this delivery channel and it is increasing More than just informational, actually do most if not more than a physical branch Discovery Credit Unions are better at Websites than most Community Banks Community Banks are slowly catching up Many website best practices Efficiency, information, community involvement

26 Website Study #1 Looked at over 500+ websites over a 6 year period Observed Best Practices of Websites of Community Banks and Credit Unions Wanted to see content, flow, and basics. Does the website work? Attractive, works on multiple levels, etc. Is it too crowded? Is the information wrong or outdated? What does the website contain? (remember, different groups will look for different things)

27 Report Website Study #1 by Item Institutions Sampled No Web Site? 6% 4% 2% 3. No Bill Pay or Log In? 36% 31% 28% 4. % that contained information that was >= 2 years old and nothing newer (i.e Community Projects) 5. % that contained errors on flow (pages under construction, went to the wrong screen, etc.) 6. % that did NOT contain information about officers or lenders (Yet institutions brag about their people) 7. % that addresses listed for branches but no map program or directions to branch or indications what services the branch had 48% 37% 46% 63% 32% 35% 66% 64% 68% 74% 63% 61% 8. % that had no indication of community involvement 82% 62% 58% 9. % that had no indication of customer/member education 87% 71% 63%

28 Website Study #2 In 2014, looked at over 200+ websites and calculated their Use and Effectiveness. Both Community Banks and Credit Unions Used traffic, time in the Website, pages that are viewed, etc. Just like Target and Nordstrom s you want them in the store Shopping, information gathering, and actual banking

29 Website Study #2 9 out of the top 10 Power Rated Websites were Credit Unions 5 out of the top 10 were located around major universities Bottom 10 were all Community Banks Conclusion Credit Unions are more effective and their members use the Website/Home Banking more than Community Banks with their consumer customers Want to know your Power Rating?

30 Website Study #2 Rank Quartile Rating Range Average Power Rating Total # Banks # Credit Unions 1st Quartile Excellent > 38 to nd Quartile Good >19 to rd Quartile Fair 7 to th Quartile Really needs work Below Total

31 Power Rating idea5 Power Rating Total Bank Credit Union Excellent Good Rank Order (200 = WORST, 1 = BEST)

32 What do Employees Want? Depends on the Generation Traditionalists (born ) Baby Boomers (born ) Generation X (born ) Generation Y (born ) Generation Z (born 2000 now) Styles of management and managing are very different 32

33 Work Ethic and Values Traditionalist Hard Work Respect Authority Duty before Fun Adhere to Rules Baby Boomers Workaholics Work Efficiency Crusading Causes Desire Quality Question Authority Generation X Eliminate the Task Self Reliant Skeptical Want Structure and Discipline Generation Y What s next? Multitasking Goal Oriented Tolerant Entrepreneurial Work Environment Office Long hours in the office Office, Home, Desires flexible schedule. Time off is valued. Office, Home, Starbucks, Desires flexible schedule Work is A duty and obligation An exciting adventure A contract. A difficult challenge A means to an end. Fulfillment Customer and worker interaction One on one, personal contact Phone, Personal contact, meetings with team Phone, , IM, Text , IM, Text, Social media Main Motivator Self worth Salary Security Maintain personal life Technology Used Dictates documents, use of library. Limited web, phone use and e- mail. Documents prepared by associates, limited web use. Prefers phone, . Creates own documents, mobile PC s. Uses web to research. and text 24/7 Creates own documents and own databases, mobile PC s, devices. Uses web to research and network. , 33IM, text 24/7

34 Takeaways 1. Big Data is valuable if you know where to look and weed out the hype. 2. Ask yourself the top 10 questions you are trying to answer. If you had the answer will it change your Strategic Thinking? Keep a record of the questions, and move on to the next Hint Your Gen Y employees would love to do Big Data discoveries. (they will need some guidance) 4. Spend part of your week absorbing a new Discovery.

35 idea5 is a unique blend of great people, powerful technology, and innovative ideas, creating Aha! and Wow! moments for our employees and financial institution clients. We help them discover, decide, and then take action. Bill.Goedken@idea5inc.com