Predictive analytics can help banks in several key areas: 1) Predictive models can analyze customer data to better understand customers, identify new customers, estimate lifetime value, maximize spending, and reduce attrition. 2) Risk management models can assess default risk, optimize lending policies, and proactively restructure loans to manage credit risk. 3) Revenue models can help target marketing, make customized offers, and increase sales and loyalty by anticipating customer needs.

1. Predictive Analytics for
banking
www.pi-cube.com

2. www.pi-cube.com
Banking trends: 2016
Customer experience
(Sales + service)
Effective use of analytics
Decisions driven/supported by data
Digital channels expand and thrive
Market Consolidation (M&A)
2

3. www.pi-cube.comData & Analytics
Bank Data
Information
Knowledge
Wisdom
Business
Intelligence
Predictive
Analytics
Prescriptive
Analytics
3

4. www.pi-cube.com
4
Business Intelligence Predictive Analytics
Orientation Rearview Future
Types of questions What happened?
When, who, how many?
What will happen?
What will happen if we change this one thing?
What next?
Methods Reporting (KPI’s, metrics)
Automated monitoring/alerting
Dashboards & Scorecards
Ad-hoc queries
Predictive modeling
Statistical analysis
Data/text/multimedia mining
Simulation/optimization
Data types Structured Structured/unstructured
Knowledge creation Manual Automated
Business value Reactive Proactive

5. www.pi-cube.com
SMART Banking with
Predictive Analytics
Driven by YOUR data
Surfacing quantified insights
Enabling YOUR informed decisions
Bring in the future with Predictive Analytics
POWER-UP your lending business
5

6. www.pi-cube.com
SMART banking
Data-driven
Risk-averse
Regulation-
compliant
Customer-aware
6

7. www.pi-cube.com
SMART Banking
(web/mobile enabled)
COMPLIANCE
RISK
REVENUES
• Identify customer personas
• Find new customers
• Estimate customer lifetime value
• Maximize customer wallet share
• Reduce customer attrition
• Manage credit risk
• Optimize lending policy
• Re-structure loans proactively
• Assess default-risk
• Manage cash reserves
• Predict delinquency rate
• Enhance loan underwriting
• Improve loan applicant
selection
Proactive customer awareness
Proactive risk-aversion
Proactive regulatory compliance
DATA
7

8. www.pi-cube.com
8
Know your customer’s needs. Ahead of time.
• Customize your marketing to the
individual customer
• Make an offer at the point of
need
• Increase sales
• Create custom offers for your
best customers
• Increase customer loyalty
• Reduce customer attrition

9. www.pi-cube.com
9
Protect your loan assets. Proactively
• Manage your loan portfolio
proactively
• Identify future problem loans
• Re-structure potential delinquent
loans
• Make better loans to your best
customers

10. www.pi-cube.com
Predictive Analytics
Life Cycle
Predictive Analytics RoadMap
(PARM)
Business
Understanding
Identify use case
objectives
Data Exploration
& Prep
Collect, review, select &
cleanse sample data
Model build
& validate
manipulate data &
draw conclusions
Implement &
Deploy
Integrated web
application &
dashboard
Identify points of
contact
Review & prioritize
business objectives
Map business
objectives to use
cases
Joint SOW
(Develop, review,
signoff )
Use case 1 Use case 2 Use case 3 Use case …
10
Predictive Analytics Roadmap
Identify problem
(use case)
Collect
sample data
Build & present
Predictive
Model(s)
Pilot Phase
(proof-of-concept)
Maintain
Model optimization
Predictive Analytics Framework (architecture, process, connectors)

11. www.pi-cube.com
11