Download BIG DATA AND THE FINANCIAL SECTOR April, 2015

BIG DATA AND THE FINANCIAL SECTOR
April, 2015
WHAT IS BIG DATA?
Of data today was
generated in the last
Years
* Based on IBM statistics.
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WHY BIG DATA?
Understand the past.
What happened?
Predict the future.
What should we do?
Immediate action.
Execute like a hero.
Competitive Advantage
Direction – navigate through complexity.
Big Data
Analytics
Execution – the right person, the right time.
Predictive
Analytics
Results – accurate and efficient.
Traditional BI
Ad Hoc
Reports
Direct mail and telemarketing
Reports and Dashboards
The Evolution of Data and Analytics
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KEY COMPONENTS FOR BIG DATA ANALYTICS
 Identification and aggregation of relevant data
points
 Technical infrastructure to host large data sets
 Machine learning algorithms to predict behavior
 Accurate interpretation of results
 Data privacy and customer consent
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CHALLENGES FOR BANKS
CHALLENGE
ISSUE
HOW BIG DATA CAN HELP
Acquisition of
Customers
Little to No
History
Use New Sources of
Expand Target Market
Dormant Customers
Up to One Third of
Have Little/No
Use Data Analysis to
of Financial Products
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EXISTING CUSTOMERS: USING TRANSACTION
DATA TO UNDERSTAND PROPENSITY
Account Balance
Savings Accounts Distribution*
Customer Age
*Source: WSBI/HFC
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TARGET PRODUCTS AT LIKELY RESPONDERS
Cluster
Description
Percentage of Overall
Accountholders
Target Product
Wealthy & Engaged
Older, high balance, active savers
19%
Mortgage/Retirement Offers
Wealthy but Inactive
Older, high balance, non-savers
19%
Savings Promotions
Youth Accounts
Young, some balance & activity
19%
Mobile Banking/Savings
High Utilization
Low balance, active savers
14%
Checking Promotions
High Churn Risk
Low balance, non-savers
28%
Retention Promotions
TOTAL AVERAGE
100%
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NEW CUSTOMERS: USE MOBILE DATA TO QUALIFY AND CONTACT
70%
PROBABILITY OF ABOVE AVERAGE INCOME
61,5%
60%
50,6%
50%
40%
35,8%
31,6%
33,2%
MOBILE POPULATION
30%
24,8%
22,6%
22,1%
20%
11,6%
10,5%
10%
0%
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Cignifi Score
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OTHER APPLICATIONS FOR MOBILE DATA
• Financial Product Marketing
• Credit Scoring
• Insurance Propensity
• Airtime Credit
• Mobile Value Added Services
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