Building infrastructure for data analytics in financial industry

1. Feyzi Bagirov
26 Jan 2015
1

2. 2
 Only about half of financial services organizations
have a single system to comply with anti-money
laundering directives (Accenture)
http://www.forbes.com/sites/jasonbloomberg/2014/0
7/29/three-way-big-data-banking-battle-brewing/

3.  Data Mining
◦ Retail Use cases
◦ Data Mining Process
 Data Mining Methodologies
 Data
◦ Data Training
◦ Types of Business Information Systems
◦ Data Warehouses
◦ Data Mining Tools
◦ Data Visualization Tools
◦ Big Data
3
A
B
CD
E

4.  Machine Learning is a scientific discipline that
explores the construction and study of
algorithms that can learn from data (Ron
Kovahi; Foster Provost (1998). “Glossary of
terms”.
 Data Mining is the process of achieving
Machine Learning.
4
A
B
CD
E

5.  Response modeling for direct marketing
 Uplift modeling for direct marketing
 Customer retention with churn modeling
 Churn uplift modeling
5
A
B
CD
E

6. 6
Lifeline Screening: Response up
38%, cost down 20%, 62K more
customers annually
PREMIER Bankcard: Direct mail
response up 3-5%
Sun Microsystems: Doubled the
number of leads per phone call
A
B
CD
E
Based on the past experience, who will respond tomorrow?

7. 7
A
B
CD
E

8. 8
A
B
CD
E
Leading financial institution:
incremental conversion up 0.02% to
0.43%; Revenue per contact up by
over 20 times

9. 9
Reed Elsevier’s Caterer &
Hotelkeeper: Reduced churn by
16%; Retention ROI up by 10%
PREMIER Bankcard: $8 million est.
retained
Leading North American Telecom:
Identified customers with a 600%
increased risk of churn with social
network analysis.
Optus (Australian telecom):
Doubled churn model performance
with social data
A
B
CD
E

10. 10
A
B
CD
E
Telenor: Reduced churn 36%; Cost-
of-contact down 40%; Campaign
ROI up 11-fold
US Bank: Costs down 40%, lift up 2
times, and cross-sell ROI up 5
times

11. 11
CRISP-DM
(Cross Industry Standard Process
for Data Mining)
SEMMA
(Sample, Explore, Modify, Model, Assess)
A
B
CD
E

12. Business Task
Data Set
Data Preparation
Data cleaning
Modeling
Evaluation and
validation
Use of DM
results/deploymen
t
Results of action
based on DM
results
Development
12
Strategic
Objectives
Operational
Objectives
Marketing
Objectives
Other
Objectives
A
B
CD
E

13. 13
Age: 25-35
Gender: Male
Marital Status: Married
Education: Graduate
Historically
Historically
Training Data
Unknown Data
Prediction
Supervised
Unknown Data
Historically
Unsupervised

14. 14
Transactional
Information
Systems
Analysis-Based Information Systems

15. 15
Data Warehouse 4 main features:
• Topical Orientation (customer, product, etc.)
• Logical integration and homogenization (relational integration)
• Presence of a reference period (vs operational)
• Low volatility (should not change often)
3 components of Data Warehouses:
• DBMS (Database Management System)
• DB (Database)
• DBCS (Database Communication System)
Snowflake Star

16. 16

17. 17

18. 18

19. 19
1. Velocity
2. Variety
3. Volume

20. 20
Q&A?

21. 21