Processing transactions is at the core of any bank’s business. Danske Bank’s journey started with recognising the value that could be gleaned from generating insights from the data to improve customer behaviour analytics. Today, the company streams large volumes of transactional data in near-real time onto its Hortonworks data Platform to improve fraud detection and customer marketing. In this session, Nadeem will outline the bank’s vision, how it was socialised across the executive board team and the resulting sponsorship, the technological path, challenges overcome and the results that have not only improved the customer experience but quantifiable metrics fraud and opening new revenue streams. Furthermore, Nadeem will cover future use cases around maintenance and operations.

1. Depositing value from transactional data
Advanced Analytics Platform for Fraud
Nadeem Gulzar,
Senior Development Manager of Advanced Analytics and Architecture, IMD, CBIT

2. 11
• Senior Development Manager
• Head of Advanced Analytics and Architecture
• IT at Danske Bank
− Approx. 2000 people, more than 2000 systems
• Education:
− M.Sc. from Copenhagen University in Computer Science and
Mathematics, with a bachelor in psychology
• Employed in Danske Bank Group since: 2003
• Experience Summary:
− 15+ years of IT experience, primarily within software
development and management
Nadeem Gulzar

3. 22
Agenda
Big Data team
Big Data journey in Danske Bank
Introduction to fraud
Advanced Analytics Platform for Fraud
Next steps
Q&A

4. 33
Agenda
Big Data team
Big Data journey in Danske Bank
Introduction to fraud
Advanced Analytics Platform for Fraud
Next steps
Q&A

5. 44
Big Data team
Platform
Engineers
Data
Engineers
Data
Scientists
Business

6. The Data Scientist Venn Diagram (v.2)
The
Perfect
Data
Scientist
The Good
Consultant
Drew
Conway’s
Data
Scientist
R Core
Team
The Data
Nerd
The Hacker
The
Accountant
The IT Guy
The Stats ProfHot Air
Comp Sci
Prof
The
Number
Cruncher
The
Salesperson
Head
of IT
Ana-lyst
Communication
Statistics Programming
Business

7. 66
Agenda
Big Data team
Big Data journey in Danske Bank
Introduction to fraud
Advanced Analytics Platform for Fraud
Next steps
Q&A

8. 77
Core team
 Architects
 Data scientists
How did we start this journey?
 Data engineers
 Platform engineers
Coverage
 Hackathon
 Proof-of-concept
 Roadshows
 Feedback loop
 Partner up
 Business value
 Reference architecture
 Use cases
 Pilot project
 Infrastructure + Data science
laboratory
 Operating model and
organization structure
 Legal and compliance use
cases
 Security
 Data governance
 Automated data science
1–2 month 3–4 month 1 year
Plan
Build
Learn
Adoption lifecycle: Hadoop
 Business

9. 88
Big data Eco system
Big Data
Eco system
Technology
Exploring
process
DataCompliance
Competencies
Benchmark
Governance

10. 99
Agenda
Big Data team
Big Data journey in Danske Bank
Introduction to fraud
Advanced Analytics Platform for Fraud
Next steps
Q&A

11. 1010
• Initial system built many many years
ago
• Was built on a rule based engine with
minimal statistical capabilities
• Very good detection rate, but
generates a lot of false positives
• Very difficult to get an overview of all
rules
• Difficult to do an impact analysis of
changes
• Minimal ”control” in the hands of the
Global Graud Investigation Unit
Introduction to Fraud handling in Danske Bank

12. 1111
”Old” fraud engine: expert-driven approach
Global
Payment
face
eBanking
MobilePa
y
Business
Online
Global
Payment
Interface
Central Fraud Engine
Fraudulent ?
(next slide)
Yes/maybe
Create verification
Manual evaluation
Fraudulent ?
Yes !
Return Payment
to customer
Fraud payment Update
frauddata
NoProcess payment
to beneficiary
No

13. 1212
”Old” fraud engine: expert-driven approach
• How it works:
• Understanding the domain
• Manually generating IF-THEN-ELSE rules based on the domain expertise
• Manually maintaining the rule engine: validation of existing rules, addition of new rules
and/or exceptions
Pros:
• Easy to understand
• Easy to explain
• Easy to develop
Cons:
• Limited abilities to detect complex
relations between the attributes
• Subjective inference of the rules
• Rather long rule detection and
inference time
• Requires manual maintenance

14. 1313
Agenda
Big Data team
Big Data journey in Danske Bank
Introduction to fraud
Advanced Analytics Platform for Fraud
Next steps
Q&A

15. 1414
Advanced Analytics Platform for Fraud - goals
• Develop a scalable and expandable
platform which follows the Danske Bank
blueprint of digitalization
• 100 % data-driven approach to find patterns
in the data and complement the existing
fraud engine
• Use Hadoop to handle the large data
volumes for training models on transaction
data
• Implement a real-time solution that can
score live transactions in under 5.000 ms
• Reduce amount of false-positives by at least
20-40 %

16. 1515
Advanced Platform for Fraud: data-driven approach
Global
Payment
face
eBanking
MobilePa
y
Business
Online
Global
Payment
Interface
Central Fraud Engine
Fraudulent ?
(next slide)
Yes/maybe
Create verification
Manual evaluation
Fraudulent ?
Yes !
Return Payment
to customer
Fraud payment Update
frauddata
NoProcess payment
to beneficiary
No
Basic
customer
data
Advanced Analytics
Platform for Fraud
Historic
transaction
data
Weblog
data
Customer
product
data
Aggregated
customer
data

17. 1616
Advanced Platform for Fraud: data-driven approach
• How it works:
• Understanding the domain
• Gathering and preparing the data
• Automatically generating the rules and recognizing fraudulent patterns by training
models on historical data
• Automatically maintaining the engine by retraining the model
Pros:
• Automatic/data-driven/objective
inference of the rules
• Ability to detect patterns in a high
dimensional data input
• Fast detection of new/changing
fraudulent patterns
Cons:
• Might be unintuitive and hardly
interpretable
• Data preparation and feature
aggregation is time consuming

18. 1717
Expert vs. data-driven approach
Data understanding Pattern definition Rule application Case handling
Data understanding Pattern definition Pattern recognition Case handling
Expert – driven approach
Data - driven approach

19. 1818*Depends a lot on what fraud-appetite GFU has.
Advanced Analytics Platform for Fraud - outcome
Goals
• Scalable and expandable platform
following the Danske Bank blueprint
• 100 % data-driven approach
• Large data-set management on
Hadoop
• Implement a real-time solution that
can score live transactions in under
5.000 ms
• Reduce amount of false-positives
by at least 20-40 %
Accomplished
• Danske Bank blueprint is the spine
of our architecture
• Transaction- and customer data
• Hadoop used for ingesting- and
enrichment of data
• AAPF can deliver a transaction
score in under 300 ms (avg)
• Depending on GFU risk-appetite,
initial results indicate >90 %
decrease in false-positives*

20. 1919
Big Data team – lessons learned
Success: From PowerPoint to production in 12 weeks (8 sprints)
Team effort: Thorough collaboration across IMD, GFU and Think Big
Synergy: Successfully spearheaded innovation in all involved systems
Inspiration: Incorporation Danske Bank advanced analytics blueprint
sets a generic scene for combatting new challenges in advanced
analytics
Agile influence: Without following the agile approach we would not
have been able to deliver in this timeframe (daily standups, Atlassian,
sprints,…)

21. 2020
Agenda
Big Data team
Big Data journey in Danske Bank
Introduction to fraud
Advanced Analytics Platform for Fraud
Next steps
Q&A

22. 2121
Next steps
• Reduce scoring time
• Data quality
• Data enrichment
• Spark for ‘heavy lifting’
Improve
current setup
• All transactions
• All countries
• Credit Cards
• MobilePay
New use cases
• Weblog data
• Behavioural analysis
• Deep Learning
• Predictive Maintenance
Adv. Analytics
2017

23. 2222
Questions