Business Intelligence & Big Data Analysis

Business Intelligence & Big Data Analysis

Agenda Business Intelligence and Big Data Overview Impact in Other Industries Credit Union Industry Steps We Can Take

Business Intelligence & Big Data Analysis Business Intelligence (BI): Umbrella term that refers to a variety of software applications used to analyze an organization's raw data BI as a discipline is made up of several related activities, including data mining, online analytical processing, querying and reporting Big Data: The practice of examining large sets of data from: o Variety o Velocity o Veracity

Data Mining Analyzing this data allows you to: Find specific patterns Prediction of likely outcomes Cluster data into groups Creation of actionable information

How Much Data Exists We create over 2.5 quintillion bytes of data per day Billion = 10 9 Quintillion (Exabyte) = 10 30 We ve generated more data in the last few years than the decades before Dawn of civilization 2003: 5 quintillion bytes of data Today: 5 quintillion bytes of data per every two days 90% of the available data today was generated in the last 2 years

Working Definition Simply put, it s pulling large data points both conventionally (i.e. transactional data from a core processor) and unconventionally (i.e. text data, picture) to learn business insights to make better business decisions. It is timely, accurate, high-value, and actionable business insights that enable us to understand and analyze our performance and opportunities on a deeper level.

Analytics and Baseball

Traditional Stats Batting Average Earned Run Average Runs Batted In Player Performance Wins Strikeouts Home Runs

Advanced Analytics On base percentage One Base Plus Slugging Slugging Percentage Sabermetrics Fielding Independent Pitching Win Share Wins above replacement

Results Teams Wins (Since 2000) Avg Wins Per Year Avg Payroll (millions) NYY 1421 95 $181 STL 1364 91 $92 ATL 1341 89 $91 BOS 1336 89 $131 ANA 1331 89 $102 OAK 1323 88 $56 LAD 1296 86 $117 SFG 1291 86 $94 PHI 1276 85 $109 CHW 1245 83 $88 Oakland A s: 8 Playoff Appearances 6 Division Titles ( 2 titles from 1990-2000)

BI s Impact in Other Industries Healthcare 20% decrease in patient mortality by analyzing streaming patient data Telecom 92% decrease in processing time by analyzing networking and call data Utilities 99% improved accuracy in placing power generation resources by analyzing 2.8 petabytes of untapped data

Analytics and CU s

How CU s Utilize Data Today? Core Processor Mortgages Servicer Credit Card Processor DQ and Risk CRM Loyalty Surveys Consumer Loans Report Query Spreadsheet Spreadsheet Spreadsheet Report Query Report Query Report Query

Impact 30%: Analyzing 70%: Gathering Data 2 Hrs: Searching for the right info Time Spent

How is BI Going to Impact CU s? $16.9 Billion in 2015 7 times the spending for BI VS Communications Technologies 240% increase in BI spending in next 5 years 73% invested or plan to invest in BI & Big Data in the next 2 years 5%-6% higher production and output

Managing Performance

Traditional Loan Metrics Loan Application Volume Approval Rate Performance by Product Delinquency

Total Application Count Loan Applications by Underwriter 2,500 46.00% 2,000 44.00% 1,500 42.00% 40.00% 1,000 38.00% 500 36.00% 0 John Casey Mary Ann Glabella Total Application Count 1,863 1,951 Approved App Count 836 739 Approved % 44.87% 37.88% 34.00%

Performance by Product Underwriter Total Application Count Approved App Count Approved % Credit Card 1362 536 39.35% John Casey 675 289 42.81% Mary Ann Glabella 687 247 35.95% New Vehicle 1216 710 58.39% John Casey 590 384 65.08% Mary Ann Glabella 626 326 52.08% Unsecured 586 136 23.21% John Casey 283 66 23.32% Mary Ann Glabella 303 70 23.10% Used Vehicle 650 193 29.69% John Casey 315 97 30.79% Mary Ann Glabella 335 96 28.66% Grand Total 3814 1575 41.30% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% Credit Card New Vehicle Unsecured Used Vehicle John Casey Mary Ann Glabella

Approval % Underwriter Performance by Month 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec John Casey 42.68% 41.57% 47.62% 46.84% 46.04% 44.30% 45.06% 42.28% 45.16% 47.83% 42.25% 46.67% 44.87% Mary Ann Glabella 39.88% 39.26% 39.13% 35.10% 37.66% 36.72% 38.93% 33.79% 39.39% 37.27% 38.55% 38.15% 37.88% Grand Total 41.28% 40.43% 43.18% 41.10% 41.64% 40.18% 42.12% 38.10% 42.19% 42.55% 40.26% 42.11% 41.30% Grand Total

Product Level DQ and CO Rate Report Month 60+ Delinquency Rate Product Level DQ Rate New Vehicle 0.26%.26% Used Vehicle 0.76% Unsecured Loan 1.50% Credit Card 2.09% Grand Total 0.89% 2.09%.76% Report Month Annual Charge Off % New Vehicle 0.10% Used Vehicle 0.45% Unsecured Loan 1.20% Credit Card 1.56% charge off Rate 0.42% 1.50% New Vehicle Used Vehicle Unsecured Loan Credit Card

John s Financial Impact Underwriter Loan Type Loan Booked In One Year Average Balance Annual Interest Margin Total Loan Balance Annual Interest Earned John Casey Credit card 150 $1,000 7% $150,000 $10,500 John Casey Auto Loans 300 $10,000 3% $3,000,000 $90,000 John Casey Unsecured 100 $500 8% $50,000 $4,000

Mary s Financial Impact Underwriter Loan Type Loan Booked In One Year Average Balance Annual Interest Margin Total Loan Balance Annual Interest Earned Mary Ann GlabellaCredit card 120 $1,000 7% $120,000 $8,400 Mary Ann GlabellaAuto Loans 275 $10,000 3% $2,750,000 $82,500 Mary Ann GlabellaUnsecured 90 $500 8% $45,000 $3,600

Side by Side Underwriter Annual Interest Earned on new Booked Loans John Casey $104,500 Mary Ann Glabella $94,500

Loan Application Process Loan Serviced Loan Application Complete Loan Applied

Solutions Option # 1: Change loan system Time consuming Costly Option # 2: Use BI to do a deeper dive Create a link between data from Core and LOS Link between Loan and Loan App to find the Underwriter associated with Loan

Deeper Dive Report Month 60+ Delinquency Rate Charge Off Rate John Casey 1.19% 0.51% Mary Ann Glabella 0.73% 0.31% Grand Total 0.89% 0.42% Report Month 60+ Delinquency Rate Charge Off Rate Credit Card 2.09% 1.56% John Casey 2.46% 1.90% Mary Ann Glabella 1.71% 1.26% New Vehicle 0.26% 0.10% John Casey 0.30% 0.15% Mary Ann Glabella 0.20% 0.08% Unsecured Loan 1.50% 1.20% John Casey 2.00% 1.49% Mary Ann Glabella 1.38% 1.12% Used Vehicle 0.76% 0.45% John Casey 0.89% 0.78% Mary Ann Glabella 0.65% 0.41% Grand Total 0.89% 0.42% 60+ Delinquency Rate Charge Off Rate John Casey Mary Ann Glabella

Financial Impact Underwriter Total Balance Charge off Rate Charged off $ John Casey $120,000,000 0.51% $684,000 Mary Ann Glabella $108,000,000 0.31% $334,800 Normalized Normalized to John s Balance $120,000,000 $312,000 Normalized to Mary's Balance $108,000,000 $216,000

Attrition Analysis

Membership Closures 200 180 160 140 120 100 80 60 40 20 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Membership Close # 89 72 81 100 86 96 82 89 183 167 175 101

Closure Reasons 200 180 160 140 120 100 80 60 40 20 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Loan Payoff 16 10 11 13 13 17 16 18 19 13 14 16 Unknown 38 32 38 52 36 46 30 34 127 120 129 51 No Longer Working at SEG 14 10 12 13 13 11 12 12 13 11 10 10 Dormancy 21 20 20 22 24 22 24 25 24 23 22 24

Takeaways

Big Data With Small Tools Twitter Facebook Comments Closure Comments in Web Banking Comments from the call center

Comments and Analysis Results Word usage Usage ranking Analysis of word usage ranking Take action

Big Data and Member Closures Never Utilized Bill Pay Issues Locations Transactions 0 20 40 60 80 100 120 140 Transactions Locations Bill Pay Issues Never Utilized Reasons 50 2 30 120

Setting Member Strategies

Dormancy Analyze Dormant Open Dates When members stop utilizing Member Loyalty feedback

Discovery Member Drive Promoters promote Member Engagement

Areas Affected by BI Shares Loans BD Marketing Cards Call Center Branches HR Member Loyalty Board ALCO/ALM Audit IT NCUA Investment Advisors

Impacting the Business Improve Efficiency oby identifying the non performing and overlapping products, we could reduce our product mix. That helped to reduce the operating/it cost. It increased the member loyalty score with clear product offering for members. Increase Loans oapplication pipeline management dashboard helped underwriting department to improve the loan booking ratio from 55% to 62%. ovintage Analysis: Understand when members would payoff a loan and better target market them

Impacting the Business Manage delinquency oby providing early DQ analysis reports for the Collection department by product, we reduced DQ by 25BP Increase Products/Services per member oby identifying the next targeted product we increased product per member ratio from 4.2 to 4.5. oby analyzing new member product/service initial usage we were able to change our new member onboarding strategy and increased services per new member from 2.5 to 2.92.

Next Steps

Creating an Effective BI Program IT End-Users Service Level Agreements Databases Applications Tools Right Stakeholders? Right Technology? Scalability? Skillset? Data Volume Growth Training Costs Expertise Industry Knowledge Support

Steps to Take

Utilize Your Data Pull Data Directly into Excel

Utilize Your Data Create Pivot Reports

Visualization Create Pivot Chart

Product Performance

1991-01-01 1992-03-01 1993-05-01 1994-07-01 1995-09-01 1996-11-01 1998-01-01 1999-03-01 2000-05-01 2001-07-01 2002-09-01 2003-11-01 2005-01-01 2006-03-01 2007-05-01 2008-07-01 2009-09-01 2010-11-01 2012-01-01 2013-03-01 Advanced Analytics 12.00 10.00 Credit Card Delinquency and Unemployment Trend Regression analysis between Unemployment Rate and early Delinquency 8.00 6.00 4.00 2.00 Use economic forecast of Unemployment rate to predict early Delinquency 0.00 Delinquency Rate Unemployment Rate Use relationship between early DQ and Charge off rate to predict loan loss provision needs

Questions to Ask Are you reporting or analyzing your data? Are you making information easily accessible to your team, so they can do their jobs better? Do you receive information that is of high-value, timely, accurate and actionable business insights? Are you a data driven decision CU?

Opportunity CUs are faced with: Increased regulatory scrutiny Reduced operational efficiency Increased risk and fraud Increased competition Impact on the members: Higher loan rates Lower dividends on share balances Reduced member payouts Reduced wallet share THIS IMPACTS MEMBER LOYALTY AND THE CU S BOTTOM LINE

Thank You! BLESSON ABRAHAM BLESSON@SAVVYINTEL.COM (312) 899-6891