This document discusses how analytics will transform banking in Luxembourg. It notes that data is now digital and ubiquitous, creating opportunities for insights through big data analytics. The analytics life cycle is described, from problem identification to model deployment and evaluation. Different levels of analytics usage and culture in organizations are outlined. The document advocates for a hybrid approach to analytics using automated rules, anomaly detection, predictive modeling and other techniques. A case study describes how a bank used analytics for improved risk management, customer insights, and executive decision making. The conclusion is that Luxembourg can become a leader in analytics adoption to transform outdated business models.

1. How Analytics will
transform Banking in
Luxembourg
TOMMY LEHNERT

3. Client behavior
• Previously • Today

4. PERSPECTIVE Data is EVERYWHERE
Data was digital
1986
6%
Data is digital
Today
99%

5. PERSPECTIVE
Where is the wisdom we have lost in
knowledge?
Where is the knowledge we have lost in
information?
T.S. Eliot

6. Source: IDC Digital Universe Study, sponsored by EMC, May 2010
Evolution
2015
8
ZETTABYTES

7. VOLUME
VELOCITY
VARIETY
TODAY THE FUTURE
DATASIZE
THRIVING IN THE BIG DATA ERA
VARIABILITY
COMPLEXITY

8. Big Data
STEPSTO CONQUER
COMPLEXITY
TURNCHALLENGE INTO OPPORTUNITY
• 36% annual increase in business data
• 93% believe in revenue increase
• 97% significant changes over the next 2 years in
leveraging data
GAIN MAXIMUMVALUE FROMYOUR DATA
• Advanced Analytics +
• PowerfulVisualizations +
• Sharing
• High Speed Performance +
• Cost Efficient Scalability
Source: Economist Intelligence Unit 2011 Report, 2011
Source: Lavastorm Report, 2015 - IBM Report, 2014

9. Trusted, analytical-based decisions are needed across the organization
IMPACT SPANSTHE
ENTIRE
ORGANIZATION

10. WHY SHOULDYOU CARE?
YOUR
COMPETITIVE
ADVANTAGE
Orient
Observe
Act
Act
Orient
Decide
MARKET
OPPORTUNITY
Decide

11. Source: The Current State of Business Analytics: Where Do We Go From Here?
Prepared by Bloomberg Businessweek Research Services, 2011
EXTERNAL
VIEWPOINT
CHALLENGES IN ANALYTICSADOPTION

12. Analytics Culture
• Analytically
new
Level 1
• Analytically
Aware
Level 2
• Analytically
Informed
Level 3
• Analytically
Driven
Level 4
• Analytically
Innovative
Level 5

13. ANALYTICALLY
NEW
ANALYTICALLY
AWARE
ANALYTICALLY
INFORMED
ANALYTICALLY
DRIVEN
ANALYTICALLY
INNOVATIVE
LEVEL 1
LEVEL 2
LEVEL 3
LEVEL 4
LEVEL 5
Isolated
analytics use.
Basic tools and
limited or no
best practices
Predictive analytics
usage is part of
mission critical
applications only.
Full benefits are not
understood by a
majority in the
organization.
Analytics usage
consists primarily
of tactical and ad
hoc approaches.
Analytics dev. and
deployment is
constrained, yet
departments have
their own experts
and/or initiatives.
Analytics talent
is centralized into
larger groups.
Management
understands and
supports analytics
for strategic value,
thus bringing
business units into
alignment
Company is
committed to
analytics as part of
its future growth
plan.
Business units
embrace their own
transformational
analytical plans.
ANALYTICS
USAGE
Varying Levels ofAnalytics Use and Expertise

14. IDENTIFY /
FORMULATE
PROBLEM
DATA
PREPARATION
DATA
EXPLORATION
TRANSFORM
& SELECT
BUILD
MODEL
VALIDATE
MODEL
DEPLOY
MODEL
EVALUATE /
MONITOR
RESULTS
Domain Expert
Makes Decisions
Evaluates Processes and ROI
BUSINESS
MANAGER
Model Validation
Model Deployment
Model Monitoring
Data Preparation
IT SYSTEMS /
MANAGEMENT
Data Exploration
Data Visualization
Report Creation
BUSINESS
ANALYST
Exploratory Analysis
Descriptive Segmentation
Predictive Modeling
DATA MINER /
STATISTICIAN
How can
you create
strategic
advantage
?
THE ANALYTICS LIFECYCLE

15. Hybrid approach to analytics
Automated
Business Rules
Anomaly
Detection
Predictive
Modeling
Text
Mining
Entity
Matching
Network
Generation
Generation
Process

16. Yesterday’s methods are insufficient
to address tomorrow’s challenges
Resources &
Expert Knowledge
Technology &
Advanced Analytics
It takes more than…

17. ANALYST VS. PREDICTIVE MODEL
Indicator
Age
Gender
Marital Status
Indicator Weight
Age 13%
Gender 18%
Language 14%
Marital status 17%
Monetary inflow 22%
Postal Code 2%
Education Level 3%
Client relationship age 2%
99%
accurate

18. Innovative
Strategies for
Data Analytics
• A flexible enterprise architecture
that supports many data types
and usage patterns
• Upstream use of analytics to
optimize data relevance
• Real-time visualization and
advanced analytics to accelerate
understanding and action
• Common analytical framework
across the enterprise

19. Copyright © 2012, SAS Institute Inc. All rights reserv ed.
Cosmos Bank BANKING
BUSINESS ISSUE
• Provide access to risk, customer information and analytic results to all affiliates, business
units
• Give executives access to big data insights to make more informed decisions
• Improve costly, timely process to produce monthly/quarterly reports due to many
different data sources resulting in inconsistent data
SOLUTION
• Analytics
RESULTS
A solution with instant access to large stores of information and data analysis that is fast, smart
and mobile, resulting in:
• A more accurate view of customer behavior
• Real-time insights for risk management, customer development, product marketing and
finance
• Integrated corporate and consumer data
• User generation and sharing of reports, dashboards and visualizations
“This is an era of visualization.
We provide ranking officers
and board members with eye-
catching tables and charts, so
they can quickly grasp the
data's meaning and make
informed decisions. If they
want more details, they have
immediate access to relevant
tables or charts.”
James Lin
Chief Risk Officer

20. CONCLUSION Final Thoughts
 Big Data and Analytics affect people and
businesses everywhere.
 Era of Analytics has begun and represents the
opportunity to transform obsolete business
models.
 Invest in people and technology.
 Especially in Luxembourg, we can be capable of
becoming an example in analytics adoption.

21. “The Greatest Value Of A Picture
Is When It Forces Us To Notice
What We Never Expected To See.”
John W. Tukey, Exploratory Data Analysis 1977

22. Thank you
tommy.lehnert@sas.com
How Analytics will transform Banking
in Luxembourg
T O M M Y L E H N E R T