Business Intelligence & Big Data Analysis

Transcription

1 Business Intelligence & Big Data Analysis

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

3 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

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

5 How Much Data Exists We create over 2.5 quintillion bytes of data per day Billion = 10 9 Quintillion (Exabyte) = 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

6 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.

7 Analytics and Baseball

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

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

10 Results Teams Wins (Since 2000) Avg Wins Per Year Avg Payroll (millions) NYY $181 STL $92 ATL $91 BOS $131 ANA $102 OAK $56 LAD $117 SFG $94 PHI $109 CHW $88 Oakland A s: 8 Playoff Appearances 6 Division Titles ( 2 titles from )

11 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

12 Analytics and CU s

13

14 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

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

16 How is BI Going to Impact CU s? $16.9 Billion in 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

17 Managing Performance

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

19 Total Application Count Loan Applications by Underwriter 2, % 2, % 1, % 40.00% 1, % % 0 John Casey Mary Ann Glabella Total Application Count 1,863 1,951 Approved App Count Approved % 44.87% 37.88% 34.00%

20 Performance by Product Underwriter Total Application Count Approved App Count Approved % Credit Card % John Casey % Mary Ann Glabella % New Vehicle % John Casey % Mary Ann Glabella % Unsecured % John Casey % Mary Ann Glabella % Used Vehicle % John Casey % Mary Ann Glabella % Grand Total % 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

21 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

22 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

23 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

24 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

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

26 Loan Application Process Loan Serviced Loan Application Complete Loan Applied

27 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

28 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

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

30 Attrition Analysis

31 Membership Closures Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Membership Close #

32 Closure Reasons Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Loan Payoff Unknown No Longer Working at SEG Dormancy

33 Takeaways

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

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

36 Big Data and Member Closures Never Utilized Bill Pay Issues Locations Transactions Transactions Locations Bill Pay Issues Never Utilized Reasons

37 Setting Member Strategies

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

39 Discovery Member Drive Promoters promote Member Engagement

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

41 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

42 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.

43 Next Steps

44 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

45 Steps to Take

46 Utilize Your Data Pull Data Directly into Excel

47 Utilize Your Data Create Pivot Reports

48 Visualization Create Pivot Chart

49 Product Performance

50 Advanced Analytics Credit Card Delinquency and Unemployment Trend Regression analysis between Unemployment Rate and early Delinquency 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

51 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?

52 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

53 Thank You! BLESSON ABRAHAM (312)