Smarter Data Insights. Emmanuel Osanga

Transcription

1 Smarter Data Insights Emmanuel Osanga 1

2 Global Challenges Increasing Stakeholder Pressure Global Megatrends Factors reshaping our world Global Economic & Socio-Political Challenges 2

3 Disruptive Innovation Transforms or Destroys Current Market % of Market Share Disruptive Technology Years 3

4 Digitization Data is the new Oil for Business and it has the potential to disrupt existing structures! 4

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6 Digital Landscape in Africa population 1, 135 billion Active internet users 298 million Active social media accounts 103 million Mobile connections 900 million Active mobile social accounts 85 million population 1, 216 billion Active internet users 340 million Active social media accounts 280 million Unique Subscribers >557 million Active mobile social accounts 124 million Sources: Wikipedia, InternetLiveStats,Vkontakte,InternetWorldStats, Facebook, Tencent, LiveInternet, GSMA Intelligence

7 Digital Activities in Key Markets of Africa Digital hotspots weekly digital activities s Instant messaging Social networking Visit blogs / forums Write blog Upload content Read news/sport Console gaming Device gaming Streaming music Internet banking Nigeria Ghana Kenya South Africa Source: TNS Connected Life

8 Focus Shift Required Historically, service providers have been product centric But the demand on them is to be more customer centric Customer In Checking Mortgage Credit Card Channels Channels Channels Digital Experience Eligibility Master Eligibility Master Eligibility Master Digital Engagement Product Definition Product Definition Product Definition Accounting Accounting Accounting Product Out 8

9 Semi- Structured Smarter Data Insights How do you transform Data into Smarter Data Insight? Structured Unstructured Data Process/Models Smarter Data Insights Credit Card Behavior Customer Financials Customer Behavior Credit Bureau Smart Application Location System Logs Channel Logs Click Stream Data Customer Complaints Social Media Data Speech-To-Text 1. Next Best Action 2. Credit Risk Management 3. Fraud Detection 4. Flexible Product Pricing 5. Customer Acquisition Unbanked 6. Geo Spatial Analytics 7. Opportunities for new services 8. Content Recommendation 9. Risk Data Aggregation 10.Eco System Analytics 9

10 Next Best Action 10

11 Credit Risk Management Decision Process Enhanced Loan Approval Scoring Early Warning System Ability to repay, based on income and current debt load Tenure Social Profile Willingness to repay, usually based on past credit performance Depth Contacts Value Transactions Credit Score Title / Position Employer Location Mass Segment Credit Micro Cluster Financial Indicators Behavioral Indicators Missed or delayed payroll, probablity to over draft, spending pattern, KBB # missed/unanswered calls, # of agreed pay Internal Data External Data Real Time Analysis Inflow & Outflow Patterns External Indicators Telco & utility bill payment performance DWH BDA Saving Patterns Re-paynment performance Risk Flags External Scores Industry Indicators Perception Indicators Economic & industry specific signals for SME Brand and media coverage, SNA Score 11

12 Fraud Management Ingest and store data Application and DB log data Network and System log data ATM Logs GEO-Location Transaction data Online & mobile banking log data Analyze and extract insights Flagging anomalous activities in real time, based on analyzing an incoming transaction against a usages model of normal customer behavior. Smart Insights Flagging anomalous activities in real time Separate a typical but legitimate behavior from suspicious activity 12

13 Flexible Product Pricing Flexible Deposit Pricing Ingest and store data Customer Profile Historical pricing data Product Pricing Data Analyze and extract insights Clustering customers according to their personalized features. Compare the model output with historical data. Limiting all prices within a maxima and minima in function of the firm goals, and achieving price optimization Smart Insights Understanding the customer behavior, predict the customer s sensitivity to price reductions Target the right price for each customer Align with the business objective at any time 13

14 Unreached Customer Acquisition Un banked Customer Acquisition Ingest and store data Analyze and extract insights Smart Insights Supermarket purchase data Mobile phone usage data Utility data (e.g. Electricity ) SME customer data SME supplier data (e.g. ebay)... Building models for initial credit scoring, income validation and customer segmentation in order to qualify customer potential and target customers for specific campaigns New customer acquisition Get as complete as possible view on a potential customer in terms of income, risk profile and needs Minimize risk 14

15 Geo-Spatial Smart Insights Objectives Increase revenue through real-time, location based offers Solution Capture credit card POS and merchant data with event processor Determine geo location of POS and nearby wholesale customers Leverage real-time decision engine to generate offer to mobile device 15

16 Sell Analytic svcs to Merchants Opportunities for new Services Sell Insights To Third Parties Ingest and store data Analyze and extract insights Smart Insights Banking Transactions Credit Card Transactions External Sources, e.g. demographic data Analyze bank / credit card transactions, match these with other sources, provide aggregated views and bundle these with existing POS services Complement traditional services with analytical services for stores / merchants Provide retails with detailed customer demographics, spending patterns, etc. Banking Transactions Credit Card Transactions Social Media Data Analyze bank / credit card transactions, anonymize the data set, provide aggregated views and sell the data to third parties and institutions Make money out of data, e.g. to tourist agency s: identify and resell insights on where tourists are and where, how and when they are spending money 16

17 Smarter Insight Strategy Data & Info. Scope Initial Smarter Insight Strategy External Data Other Internal Data First fix the basics before worrying about Big Data Customer/Product Reference & Transaction Data Insights Type Most companies will struggle to realize benefits from a Big Data initiative, as long as they don t have their small data under control yet, many vendors still try 17

18 Recommendations Support Legislation Prioritize (Start Small) Digital Strategy Assess Maturity Governance Framework Data Strategy Skills Development Industry Collaboration Most Value Expert Assistance 18