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New Trends and Discoveries in ‘Big Data’ that will Help you Credit Union Compete

Bill Goedken CPA CMA CGMAPresident and CEO

Outline 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!

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

“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?

Item 1950 1982 2014Employees per Million of Assets

1 Employee = $150,000 in assets

1 Employee =$1 million in assets

1 Employee = $4 million in assets

Computers Hand Ledger, Early Posting machines

5 Terabytes = all US financial institution records

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

Main office, Mail, Some Telephone

Branch, ATM’s, Mail Website, Branch, ATM, Mobile, Call Center, Mail

Primary mode of Payment

Cash, Check Check, Credit Card Debit Card, Credit Card, ElectronicTransfers, Check

Size of Call Report 2 pages - maybe 6 pages 25 pages (plusinstructions)

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!

What is Big Data?

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

2010. (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

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!

Public Big Data (usually in the “Cloud”)

Bureau of Labor Statistics

Economic Data Interest Rates Commodity Prices Tons of Government

Data

∞ Twitter Feeds∞ Google reviews of

your institution∞ Facebook

Comments/Likes∞ Blog Posts∞ Web Pages

Census Data Competitive Data Industry Data

Internal Big Data (usually on internal servers)

Structured Semi-Structured Unstructured

Budgets Loan Applications

Memos

ProfitStar and other ALM programs

Most internal spreadsheets

Most Documents

MemberAccounts and Activity

Internal reports E-Mails

Internal Statistics (Web hits, ATM transactions, etc.)

Web pages

Patterns of Members (applications, open/closed, etc.)

Phone records

Success of relationship pricing, etc.

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!

Discoveries from Big Data.

Case 1: Internal Pattern Discovery1. 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

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1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12

Account Activity per Hour


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 2015. Annual savings projected to be $300,000+ in the first year alone.

6. Three year savings – over $1,000,000.

7. Part of savings going to electronic delivery channels

8. Internal stats are now part of their normal data gathering


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.

Tampa Bay-St. Petersburg-Clearwater: Delinquencies vs Unemployment

Sources: idea5, NCUA.gov, FDIC.gov, BLS

“X” Financial Institution Consumer Loan Demand vs Tampa Bay Unemployment

Sources: idea5, NCUA.gov, FDIC.gov, BLS

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.

The Future of Employees, Members, CommunityTraditionalists

Baby Boomers

Generation X

Generation Y

Generation Z

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 18-34 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

Future Direction of Financial Services

Supporting Cast: ATM’s, Mobile, Remote Deposit Capture, Call Center, Mail

Website/Home Banking Branch Network

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2008 2010 2012

Branches 42 32 25

Internet 15 20 27

ATM 19 14 12

Mobile 1 2 1

Telephone 4 9 10

Mail 9 13 18

Unknown 10 8 9

Preferred Banking Method: Ages 55+

Source: ABA% of respondents who indicated their #1 preferred banking method

2008 2010 2012

Branches 25 24 18

Internet 20 44 42

ATM 26 12 12

Mobile 2 2 3

Telephone 3 6 10

Mail 7 9 5

Unknown 7 3 9

Preferred Banking Method: Ages 35-54


2008 2010 2012

Branches 20 20 11

Internet 25 44 47

ATM 32 17 14

Mobile 0 4 15

Telephone 4 5 4

Mail 10 1 3

Unknown 9 9 6

Preferred Banking Method: Ages 18-34


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”.

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

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)

Item 2009 2011 2013

1. Institutions Sampled 550 523 514

2. 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. 2006 Community Projects)

48% 37% 46%

5. % that contained errors on “flow” (pages under construction, went to the wrong screen, etc.)

63% 32% 35%

6. % that did NOT contain information about officers or lenders (Yet institutions brag about their people)

66% 64% 68%

7. % that addresses listed for branches but no map program or directions to branch or indications what services the branch had

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%

Report – Website Study #1 by

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”

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?

Website Study #2

Rank Quartile Rating RangeAverage Power

Rating Total # Banks # Credit Unions

1st Quartile Excellent > 38 to 100 53.4 8 42

2nd Quartile Good >19 to 38 28.2 26 24

3rd Quartile Fair 7 to 19 10.6 26 24

4th Quartile

Really needs work Below 7 2.8 40 10

Total 23.8 100 100

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Rank Order (200 = WORST, 1 = BEST)

Power Rating

Bank Credit Union

Excellent

Go

od

What do Employees Want?

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Depends on the “Generation”

Traditionalists (born 1925-1945)

Baby Boomers (born 1946-1964)

Generation X (born 1965-1980)

Generation Y (born 1981 – 2000)

Generation Z (born 2000 – now)

Styles of management and managing are very

different

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Traditionalist1925-1945

Baby Boomers1946-1964

Generation X1965-1980

Generation Y1981-2000

Work Ethic and Values

Hard WorkRespect AuthorityDuty before FunAdhere to Rules

WorkaholicsWork EfficiencyCrusading CausesDesire QualityQuestion Authority

Eliminate the TaskSelf ReliantSkepticalWant Structure and Discipline

What’s next?MultitaskingGoal OrientedTolerantEntrepreneurial

Work Environment Office Long hours in the office

Office, Home, Desiresflexible schedule. Time off is valued.

Office, Home, “Starbucks”, Desiresflexible 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, e-mail, IM, Text

E-mail, 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, e-mail.

Creates own documents, mobile PC’s. Uses web to research. Email and text 24/7

Creates own documents and own databases, mobile PC’s, devices. Uses web to research and network. Email, IM, text 24/7

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 10.

3. 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.

www.idea5inc.com

[email protected]

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.