Roadmap For a Journey to Success Using Big Data & Analytics. Sanjiv Anand, Managing Director, Cedar Management Consulting International LLC

Transcription

1 Roadmap For a Journey to Success Using Big Data & Analytics Sanjiv Anand, Managing Director, j, g g, Cedar Management Consulting International LLC

2 The past is the best predictor of the future Chinese Fortune Cookie 2

3 Content BI & DA Overview Applications of BI & DA US Banking Overview BI & DA in Banking BI Vendor Overview Conclusions & Recommendations 3

4 Banking Architecture Analytical layernow a critical component of a bank s IT application architecture Channel Layer Internet Banking Mobile Banking IVR Call Centers POS/ ATM E- Trading Social Media 6 MIDDLEWAR RE LAYER Support Layer Service Layer CRM Middle-Office Layer Enterprise Risk Management Back-office Layer Core Banking Analytical Layer Predictive Analytics Lending Mgmt Treasury Front Office Scoring & LOS Cards Mgmt Enterprise Performance Mgmt Bankwide Loyalty Corporate Rating Systems Cash Mgmt BI Tools Trade Finance DW ERP BPM ECM, DMS Recon Audit Suite tools &OCR Tools Apps MIDDLEWAR RE LAYER External Layer Cheque Clearing EBPP SWIFT/ RTGS SMS, , FAX Gateway Visa/ Mastercard Bloomberg/ Reuters New specialized applications 4 Applications in early 2000 s

5 BI & Analytics Evolution (2/2) Evolution of BI from static reporting to predictive analytics 1990s s s Why did it happen? Analysis (Slicing & Dicing) What is happening? Monitoring/ Exploratory Analysis S How to make the most of it? Optimisation What will happen? Prediction E P O Predictive Analytics Offering 1980s What happened? Reporting R Static Reports Query, OLAP Dashboards, Scorecards Business Intelligence Offering Statistics, data mining, optimization Low Business Value High 5

6 BI & Analytics Evolution (1/2) Significant evolution in BI & Analytics over the years > onwards Manual Systems Computer Based Systems MIS Based Systems MIS coupled predictive Modelling Source: Infosys, BI in Banking Thought Paper Manual recording of branch systems BI limited to simple reporting of banking transactions only Manual ledgers were primary form of reports Rapid account/ txn growth; shift to automated systems Did not support informed decision making Reporting in the form of Excel Spreadsheets Sophisticated reporting tools with decisioning capabilities Emergence of dashboards at department & enterprise level Systems unable to process big Data Emergence of advanced analytics tools to process Big Data 360 view with slice/dice options Predictive modelling for key customer trends/insights 6

7 From Data to Insight Analytical tools used to leverage data & provide real insights Data Analysis Insight Customer Master Cross Sell Account / Folio Transaction Static Data Extract Transform Load Aggregate Manage Up Sell Attrition Sales Analysis Customer Book Employee Segmentation 7

8 Illustrative BI Dashboard 8

9 Typical Customer Insights from BI Key Business Insights How many customers and accounts do I have; how many have I added or lost in a period? How are my number of accounts per customer and size of customer relationships trending? What is my relationship size per customer? What are the clusters of my customer profiles and how can I group customers that behave similarly? What are my core & secondary customer segments? Who are candidates for segment upgrade / downgrade? What are the preferred channels for my customer segments? Which are my top & bottom performing customers, sales channels, products, branches, segments, employees etc.? What is the size of my customer book and how is it trending? How is my business doing vis à vis competitors and industry? 9

10 Predictive Modeling Primer What is Predictive Modeling Predicts likelihood of future outcome based on past behavior. Utilizes historical data trends to determine behavior responsible for outcome. Behavior translated into model which uses current data to predict future Outcome Examples Buying a product predicts likelihood of future purchase. Likelihood of attrition, delinquency or default Likelihood of customer responding to an offer via a certain ti channel. Expected change in account balance or relationship Benefits Holistic view of data to predict and outcome. Uses advanced dstatistical i algorithms. Improved prediction i accuracy. Builds a culture of data based decision making. Provides ability to visualize and solve more complex problems. Focused campaign reduces spend by 30%+, Increased customer penetration and revenue High ROI. 10

11 How is Predictive Modeling done? month end data base variables. Exploded to 2K 5K derived variables. Reduction process to ~50 Data Model Selection Data applied to 3 models Logistic Regression, Neural Network & Decision Tree Accuracy measurement KS, AUC, Gini, R2. Model iterations (3 5 times) & Selection. Register and Deploy. Use lead and lag indicators to monitor accuracy. Monitor and update every ~ 6 months. Model Deployment & Monitoring

12 Predictive Cross Sell Analytics Key Business Insights What is my Cross/Up sell penetration? What is the sequence of product adoption for each customer segment? What is the next best product to offer to a customer segment? Which customer is most likely to buy a newly launched product? Which are the best offer types for Cross/Up sell? sell? Which are the best channels for making Cross/Up sell Offers? What is the most popular product basket for each segment? What will be the effectiveness of my current Cross sell programs? Are my balances cannibalized due to cross sell? What is the likelihood of Cross/Up sell? 12

13 Predictive Retention Analytics Key Business Insights Which customers / accounts are most likely to attrite and when? Whatis my customer / account attrition rate? Which relationships am I likely to lose due to maturity? How many accounts are close to maturity? Which accounts are likely to close due to prepayment? p How much balance am I likely to lose due to prepayment? What is the effectiveness of my retention / loyalty schemes? How can I retain my valuable customers? What is the impact of Cross/Up sell on attrition? 13

14 Typical BI Data Requirements Typical Data requirements (30 40 parameters) available in clients data warehouse, data mart, or key systems (core, channel, GL) Customer Master Name, Gender, Date of birth, Nationality, Address, Income, Occupation, Customer core segment Account Id, Open date, Maturity date, Close date, Product, Sales Channel, Managing Channel, Acquisition Channel, Account Balance Transaction Code, Date, Type, Amount, Currency, Channel Static Data Hierarchies for products, Sales Channels, Acquisition Channels, Managing Channels & Employees Employees Employee codes, employees by sales, acquisition and managing channels 14

15 Content BI & DA Overview Applications of BI & DA US Banking Overview BI & DA in Banking BI Vendor Overview Conclusions & Recommendations 15

16 BI & Analytics Applications Financial Analysis, business monitoring & forecasting top uses of BI & DA % 100% 80% 60% 40% 20% 0% 10% 14% 14% 20% 22% 29% 70% 64% 57% Top 10 BI/ DA Uses 32% 25% 32% 25% 19% 42% 37% 21% 30% 30% 41% 48% 30% 37% 47% 45% 38% 34% 33% 28% 26% Current Use Planned Use No Plans Source: Information Week Additional uses include: Product Marketing Fraud Detection Sentiment Analysis 100% % 80% 60% 40% 20% 0% 3% 4% 6% 9% 10% 19% 22% 34% 66% 52% 41% Extent of Technology Use 15% 12% 16% 18% 23% 26% 20% 24% 20% 37% 37% 39% 34% 28% 27% 25% 27% 30% 39% 40% 35% 25% 15% 12% 10% Spreadsheets Reports Dashboards Queries Scorecards Alerts Embedded BI Predicitive Analysis Extensive Use Limited Use Planned Use No Use 16 Mobile

17 BI & Analytics Penetration Rapid growth expected for Financial Services ; large % plan to invest in 1 2 years Highest penetration in North America; not restricted to senior management & IT; diffused throughout organisation Source: Gartner, Cedar Research Banks among top users for BI; ~30% to provide BI to >81% of staff by 16 Industry Wise Penetration of Business Intelligence 9% 13% 3% 6% 5% 2% 10% 4% 100% 80% 27% 24% 35% 29% 29% 52% 37% 32% 20% 58% 42% 60% % 40% 20% 0% 26% 24% 37% 19% 41% 31% 39% 40% 50% 25% 38% 35% 34% 32% 30% 29% 28% 26% 26% 20% 17% 16% Media Banks Services Education Healthcare Mfg Retail Insurance Transport Utillities Gov ~30% of organisations provide BI solutions to ~80% of employees 100% 80% 60% 40% 20% 0% Have Invested Plans within 1-2 years No Plans at this Time Don't Know BI Penetration By Geography to % of Employees 15% 12% 30% 15% 15% 18% 15% 10% 17% 18% 15% 35% 45% 45% 60% North America Europe & MENA Asia Pacific Latin America <10% 11-20% 21-40% 41-60% 61-80% >81% 17

18 Content BI & DA Overview Applications of BI & DA US Banking Overview BI & DA in Banking BI Vendor Overview Conclusions & Recommendations 18

19 Banking Penetration Highest ih number of banks in US, market highly hl competitive; ii greatest need for BI & analytical tools to leverage data # Parameter US India UAE* Indonesia 1 Population (Mn) 314 1, Total Number of Banks 6, Population/Bank 51k 13,898k 119k 2,058k 4 Total Number of Branches 83,709 92, ,114 5 Population/Branch 37k 3.7k 13.4k 56k 5.6k 13.6k 6 Branches/Bank k Source: Cedar Research *Note: For UAE, averages calculated l based on data for 27 leading banks 7 Total Number of ATMs 425, ,080 3,967 90,080 8 Population/ATM 738 9k 1.4k 2.7k 9 Branches: ATMs Avg. Number of Employees ,325 6, Avg. Deposits ($ Bn) Avg. Assets ($ Bn)

20 Banks By Categories US smaller banks typically have smaller asset & deposit books # Parameter US India United Arab Emirates Indonesia S M L S M L S M L S M L 1 Total Nos. of Banks 5, Avg. No. of Branches k , Avg. No. of ATMs - 2.5k 5.8k ** k 4 Branches: ATMs Avg. Deposits ($ Bn) Avg. Assets ($ Bn) Criteria based on Assets S : < $1 Bn M : >$ 1 Bn & < $ 100 Bn L: >$ 100 Bn S: < $3 Bn M: >$ 3 Bn & < $ 20 Bn L: >$ 20 Bn S: < $3 Bn M: >$ 3 Bn & < $ 10 Bn L: >$ 10 Bn S:< $ 880 Mn M: >$ 880 Mn & <$4 Bn L: >$ 4 Bn Note: For India, 46 Public & Private Banks considered For UAE, averages calculated based on data for 27 leading banks ** typically smaller banks tie up with group networked ATMs to offer services S: Small Banks M: Medium Banks L: Large Banks 20

21 US Banking Structure ~91% of all banks in US have Assets < $ 1 Bn US Banks (6,096) Large Commercial Assets>300 Mn (1,719) Small Commercial Assets<300 Mn (4,377) Avg. Asset Size: $ 170 Mn Note: Some Community banks have assets > $ 1 Bn Community banks typically have assets <$ 1 Bn Large Assets>100 Bn (19) Mid Market Assets >1 Bn &<100 Bn (484) Small Assets >300 Mn & <1 Bn (1216) Avg. Asset Size: Avg. Asset Size: Avg. Asset Size: $465 Bn $ 6.4 Bn $ 520 Mn Source: Federal Reserve, Federal Deposit Insurance Corporation 21

22 US Banks Segment Wise Comparison (1/2) Small community banks have an avg asset book of ~ $216 Mn Segment Average Asset Book ($ Mn) Average Deposit Book ($ Mn) Revenue ($ Mn) Average Revenue Average ($ Mn) NPA ($ Mn) Average # of Branches Average # of ATMs Net Profit ($ Mn) Large Size Banks 687, ,000 27,000 4,782 2,552 5,819 4,000 Mid Size Banks 21,613 13, , Small Size/Community Banks Note: Large banks Asset book size >$100 Bn Mid-Size banks- Asset book size $1 Bn- $100 Bn Small Size banks Asset book size <$1 Bn Source: Cedar Analysis 22

23 US Banks Segment Wise Comparison (2/2) ROA and ROE typically lower than large and mid sized banks Asset/ Assets/ Employees/ Market Cap/ Segment Branch ROE (%) ROA(%) P/BV (x) Branch Revenue ($ ($ Mn) Mn) Large-Size Banks Mid-Size Banks Small Size/Community Banks % % - Note: Large banks Asset book size >$100 Bn Mid-Size banks- Asset book size $1 Bn- $100 Bn Small Size banks Asset book size <$ Source: Cedar Analysis 23

24 Content BI & DA Overview Applications of BI & DA US Banking Overview BI & DA in Banking BI Vendor Overview Conclusions & Recommendations 24

25 BI in Financial Services Overview (1/2) Highest US IT Investment between ; BI & Analytics top priority 50.0 Total IT Investment Spend $ Bn Between highest spend in Asia Europe Asia Americas # CIO Technologies Priorities 2013 Rank 2010 Rank Change 1 Analytics & Business Intelligence Mobile Technologies Cloud Computing (SaaS,IaaS,PaaS) Collaboration Technologies (Workflow) Legacy Modernisation IT Management CRM Virtualisation Security ERP Applications

26 BI in Financial Services Overview (2/2) Higher usage of analytics for Sales in EMEA & APAC compared to the US Region Top Influencers Top Objectives 1 Executive Management North America 2 Finance Department 3 IT Department EMEA 1 Executive Management Focus on operational 2 Finance Department efficiency in N. America 3 Sales Department Better Decision Making 1 Executive Management Growth in Revenues APAC 2 Sales Departments Operational Efficiency Enhanced Customer Service 3 Finance Department Competitive Advantage 1 Sales Department Latin America 2 Executive Management 3 Finance Department 26

27 Key Applications of BI 4% 15% 2% 23% Data Analytics Objectives 56% Many now starting to adopt tools for customer analytics BI/ DA & Decision Support System Customer Centric Financial & Risk Operation New Business model Employee Collaboration Customer Insights Customer Acquisition Customer profitability Customer Relationship Management Single Customer View Customer Lifetime Value Service/ Channel Preference Loss/ Default Prediction Transaction Analysis Txn Fraud Estimation Customer Attrition Forecast Cross-sell & Up-sell Analysis Customer Campaign Effectiveness Customer Loyalty Financial Performance Management Portfolio Analysis Asset & Liability Management Risk Management Fraud Detection Regulatory Compliance Operational Performance Management Complaints & Feedback Analysis Channel Management Staffing & Hiring Analysis Employee Performance Management Headcount Analysis 27

28 BI Advantages 70% 60% 50% % of banks Reaiised Competitive Adv. Through BI 58% 63% Improved Profitability 40% Increased Efficient Cross Sell, Up 30% 36% Operations Sell & Sales 20% 10% 0% Accurate Enhanced Risk Reporting Management Accurate Forecasting & Higher Returns Fraud Prevention & Detection Higher Customer Acquisition & Retention Improved Customer Service 28

29 Key Trends Social Media, Cloud Computing, Predictive i Modeling & Enterprise Mobility key trends for Big Data analytics Demand for Cloud Computing Significant reduction in infrastructure costs Enables data sets to be accessed on the go Growth of Predictive Modeling Emergence of user friendly PM tools Vendors investing heavily in improving usability High use of interactive visuals & automation Source: Technavio Insights Global Big Data Market in Financial Services Emergence of Social Media Emergence of powerful text analytic tools Simplified tracking of user behavior/sentiments t Augmentation of internal data warehouses with external data sources Key Trends Need for Enterprise Mobility Mobility to provide high ops & productivity benefits Reduced TAT on queries BI/DA to leverage high degree of enterprise mobile penetration

30 Case Study Indian Bank # Parameter Value 1 HQ Mumbai 2 Branches 15,297 3 Total Assets $380 Bn 4 Loans $246 Bn 5 Deposits $293 Bn Background & Problem Solution Background: One of India s largest govt owned bank Data Issues: Business data vast & spread over several domestic and international systems Requirement: To integrate the data into reliable actionable information Deployment of BI/DA Tools: Such as predictive analytics, CRM analytics, dashboards, POS/ATM analytics etc Capability: p y Enabled the bank to provide insights to individual departments and also dashboards for executives to quickly review performances Benefit Leads Increase: Was able to generate ~80k leads (10% increase) and convert ~ 9k of those (40% increase) Revenue: Was able to grow overall revenue by 27% 30

31 Case Study Asian Bank # Parameter Value 1 HQ Singapore 2 Branches Total Assets $267B Bn 4 Loans $ 134 Bn 5 Deposits $ 156 Bn Background & Problem Solution Background: One of the oldest bank in Singapore, and the second largest banking group by Assets Requirement: The bank was looking to build a customer centric business model through the use of customer analytics Analytics Team: Created a centralized team & invested $100+ Mn Analytics Platform: Built its own analytical platform and brought together all its data into a centralized data warehouse Deployment D l of BI/DA tools: Such as CRM analytics, internet banking analytics, sentiment analysis etc Usage: Analytics team became heavily used resource that serves all business units within the banking group Benefit Increase I in Cross & Up Selling: Cross-sell revenue accounts for 30% of Credit Card revenue, 25% of Wealth Management revenue and $85 Mn in shadow revenue 31

32 Case Study Middle East Bank Background & Problem Background: Third Largest Bank in the UAE Accounts: The bank had 430K accounts with 6+ month on books Requirement: Analyze and Reduce Attrition Solution Deployment of BI/DA Tools: Predictive Modelling Studio for automated analytics Capability: The solution would Predict probability of a customer to attrite in next 3 months Benefit Prediction: The solution predicted 64% attrition for CASA accounts (3 deciles); This was in line with what actually happened, Reduce Attrition: The solution enabled the bank to focus on retaining customers predicted to attrite 32

33 BI Deployment Asset light & service based offerings available for smaller banks Small/ Community Banks Mid- Market Banks Large Banks Avg. Assets $ 216 Mn $ 21.6 Bn $ 687 Bn # of Customers Typically local; within the Moderate; across a larger Large customer base same region geographical area across the nation Budget Available Limited Moderate Large Budgets BI Solutions Service Based; SaaS, Web based, hosted services Less Expensive Can be outsourced Combination of Service & Product based, Packaged Solutions, Limited expense on hardware & software Typically Product based, More expensive & complex solutions In-house BI Sophistication Basic Tools & Capabilities Moderately Sophisticated tools & capabilities Complex tools & capabilities Source: Cedar Research NEW YORK CHICAGO LONDON PARIS DUBAI MUMBAI SINGAPORE SHANGHAI 33

34 Content BI & DA Overview Applications of BI & DA US Banking Overview BI & DA in Banking BI Vendor Overview Conclusions & Recommendations 34

35 Leading BI Vendors Ability To Exec cute Gartner s Magic Quadrant Challengers Niche Players Leaders Visionaries Large number of BI solutions vendors for Financial Services # Name of Vendor Key Strength HQ Rev Employees Gartner Positioning 1 Actuate Corporation Ease of Use, performance CA, US $ 135 Mn 622 Niche player 2 Tableau Software Easy to use, easy to deploy, interactive tools WA, US $ 193 Mn 1,039 Leader 3 Information Builders Integration & quality NY, US $ 313 Mn 1600 Leader Completeness of Vision 4 Panorama Software Ltd Ease of use, performance & collaboration Toronto, Canada Niche player Source: Gartner, Cedar Research 5 QlikTech Low implementation costs PA, US $ 446 Mn 1,425 Leader 6 SAS Integration & supports large volumes of data NC, US $ 3 Bn 13,769 Leader 7 IBM Product quality, Integration NY, US $ 99.7 Bn 434,246 Leader 8 Microstrategy Product quality VA, US $ 576 Mn 3,179 Leader 9 Jaspersoft 10 Birst Cost effective, Highly embeddable & comprehensive Functionality, ease of use & lower costs ot CA, US $ 22 Mn 175 Niche Player CA, US $ 6Mn 50 Challenger 11 Cypress Analytica Product Quality, Rapid deployment NY, US $10 Mn 100 New Entrant 35

36 Vendor Landscape Vendors Vendors organized into 4 main segments Product Players Services Players Product Players built on Own Tools Productized offering built on own BI platform Pre-built data models, dashboards, reports customized for banks Built on Own Tool Built on 3 rd Party Tool Specialized Niche Generic Manpower based Product Players built on 3 rd Party Tools Productized offering built as OEM to 3 rd party tools Pre-built data models, dashboards, reports customized for banks Specialized Niche Service Providers Specialist packaged analytics service providers; use bank s existing BI infrastructure SaaS offerings available for smaller banks with no license requirement and pay-per-use model Large number of small players in this space Generic Manpower based Service Providers Consulting & analytics outsourcing services Provide resources with expertise on bank s existing BI platform to build analytics models NEW YORK CHICAGO LONDON PARIS DUBAI MUMBAI SINGAPORE SHANGHAI 36

37 Content BI & DA Overview Applications of BI & DA US Banking Overview BI & DA in Banking BI Vendor Overview Conclusions & Recommendations 37

38 Key Challenges in BI Adoption KEY AREAS DESCRIPTION 1 Developing a Compelling Business Case for BI/DA Supportive & committed business sponsors to overcome fiscal scrutiny Benefits of BI/DA should b recognized, despite the high investment required for such systems 2 Data Availability & Quality Data stored in different databases; ;p problems of inconsistency, inaccuracy & unstructured data Format of the data may not be compatible with the BI system 3 Staff Skills & Training i Required technical & business skills vary by application Team lacking the required experience, skills & training is more likely to fail in delivering results Different BI projects address different needs & are usually 4 Integration Across Systems implemented in different phases More tools create greater complexity & increased inter- operability issues 38

39 Conclusion My 2 cents worth 1. If you think your bank needs it, drive it from the top. Don t expect finance or IT to buy in easily partly because they are out of depth. 2. Keep it simple, and let it start with core functionality. 3. Don t turn it into a big data warehouse project. Let is start with whatever data is readily or easily available. 4. Use a solution that comes with pre built analytics if feasible. 5. Use the cloud don t ttry to deploy hardware and software internally if feasible. 6. Keep the initial user group to including the branch managers. 7. Let is start with a monthly analysis. No need for daily. 8. Focus on customer analytics to start with. 9. Try predictive analytics it really works! 39

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