1) The document discusses ING's strategy to shift from batch processing to real-time streaming analytics using Apache Flink. 2) It proposes building a streaming analytics platform to enable use cases like fraud detection and actionable customer insights. 3) The platform would process data streams with low latency and scale to handle large volumes, using technologies like Kafka, PMML models, and Flink for fault tolerance and exactly-once processing.

1. Bas Geerdink & Martijn Visser, 13 September 2017
Streaming Analytics at

2. IT Chapter Lead at ING Analytics
Academic background: A.I. & Informatics
Working in IT since 2004
At ING since 2013
@bgeerdink https://nl.linkedin.com/in/geerdink
Bas Geerdink

3. Product Owner / Data Analyst at ING
Academic background: ICT & Law
Working with data since 2008
At ING since 2013
@martijnvisser82 https://nl.linkedin.com/in/martijnvisser1982
Martijn Visser

4. Strategy

5. ING is a top financial enterprise, operating since 1881
Customers
35 Million
Private, Corporate and
Institutional Customers
Countries
40+
In Europe, Asia,
Australia, North and
South America
Employees
51,000
Market leaders Benelux
Growth markets
Commercial Banking
Challengers
5

6. ING’s Think Forward strategy requires a data-driven approach
6
1. Earn the primary relationship
2. Develop analytics skills to understand our customers better
3. Increase the pace of innovation to serve changing customer needs
4. Think beyond traditional banking to develop new services and business models
Empowering people to stay a step
ahead in life and in business.
Simplify &
Streamline
Operational
Excellence
Performance
Culture
Lending
Capabilities
Purpose
Customer
Promise
Strategic
Priorities
Enablers
Creating a differentiating customer experience
Clear and Easy Anytime, Anywhere Empower Keep Getting Better

7. We’re making a shift from batch to real-time
7
Secure and reliable
Relevant
Personal
Omnichannel
Predictive Actionable

8. We’re building a Streaming Analytics platform with
high-end capabilities
8
• High performance & low latency to manage and process
users’ events.
• Scalability & availability to build new features, scenarios
and integration with other external systems.
• Fault tolerance mechanism to ensure high quality output
with strong information consistency.
• Online machine learning and business rules management
capabilities.

9. • Fraud detection:
• Detect possible fraudulent transactions
• Identify possible money laundering accounts
• Actionable Insights:
• Small financial insights and robotized advise
• Customer-defined alerts and push notifications
• Offer chat/call when customers don’t finish their customer journey
• Marketing and commercial offerings (‘Next Best Action’)
Main use cases for a Streaming Analytics Platform
9

10. • Wisdom of the crowd:
• making use of similar customers to make better predictions
• Online learning:
• making use of customer feedback in real-time to train models
• Beyond banking:
• enabling IoT
• robo advise
Future scenarios
10

11. Use case: Insights

12. Do people still need Banks, or just Banking?
Whoever controls the customer experience controls the customer value.
Alibaba, the most
valuable retailer,
has no inventory
Uber, the world’s
largest taxi
company owns no
fleet
AirBnB, the largest
accomodation
provider owns no
real estate
Facebook, the most
popular media
owner, creates no
content
We want our environment to be the front and center of customer financial
needs.

13. Problem: apps & site going everywhere
Current

14. One app to rule them all… ??
14

15. Omnichannel
Design for One Experience

16. • Actionable Insights rather than data –
but select problems that are easy for
machines but diffiicult for people e.g.
forecasting
• Real time alerts can be the real
differentiators e.g. when a card auth is
received, when a customer makes a
payment that he has made a few times
previously
• Hierarchy of alerts – absolutely tell me
when things are going wrong but
occasionallly celebrate with me too
• UI adapts to confidence in AI. E.g. The
message diiffers when we have high
confidence in forecast
• UI also needs to adapt to customer
behaviours e.g. easy login when a
customer logs in to the same device
• The key role of AI is to help me stay a
step ahead in life – so UI needs to make
it easy to find items to act upon
• Digital experience is personalised to what’s
important to me
• Make the benefit of using your system clear
to me immediately rather than making me
discover the benefit
• I expect consistent experience Everywhere
e.g. once I dismiss an action online, I don’t
want to be prompted for it on mobile again
• I am used to certain way of working e.g. I am
used to settings being in one place, and I am
used to certain interaction patterns. So reduce
my learning curve by reusing those
AI + UI + I
Formula for creating compelling digital experience

17. AI + UI + I
New Experience

18. 18

19. Actionable Insights
19

20. Decision Engine: Batch Data Processing & APIs
Insert in cache
Send resultsApplication
Executes SQL
selections to
determine Insights
Data Lake
20
MVP: Batch implementation
Data warehouse
Determine
Insights
XFB File Transfer
Cassandra
[ODBC]
Realtime
Validator
InsightsAPI
Inbound response
Inbound request
1 API request response for
applicable Insights and rules
2 API request response
System Of Records
3 API request response
4 API request response for
Insights Content
Actionable Insights

21. 21

22. If only we had realtime capabilities…
22

23. Streaming Data Platform

24. The Streaming Data Platform has to be probabilistic in nature
24
Deterministic Probabilistic
• Business rules / decision trees
• Fixed logic (if-then-else)
• Rigid
• Machine learning
• Fuzzy logic
• Flexible

25. High-level Streaming data platform architecture
25

26. Streaming Data Platform Architecture
RawToRelevance Model Execution Engine Post-Processor
“filter and detect
pattern”
“determine
relevancy”
“produce
notification/alert”
Application Flow:
Business Flow:
“Data generator”
Formats outgoing messages
Get
Customer
Profile
Apply
Selection
Criteria
Apply
Model
Detect
Pattern
Get
Intermed
Event
Filter
Send
Output
Create
Intermed
Event
Get
Business
Event
Create
Business
Event
Format
Event
“Data gatherer”
Load stream of raw events and
filter/combine/aggregate them into a
meaningful business event
“Event scorer”
Scoring of PMML models to determine
the personal and relevant follow-up
actions
Get Raw
Events

27. Streaming Data Platform Architecture – Example 1
Application Flow:
Business Flow:
-150 -100 -100
Transactions
9:14
AMS
10:10
AMS
10:12
BER
Prediction:
Possible fraudulaus
transaction
Alert:
Block transaction
Option 1:
Do nothing
Option 2:
Stop money flow
Option 3:
Block card
0.12
0.91
0.73
RawToRelevance Model Execution Engine Post-Processor
“filter and detect
pattern”
“determine
relevancy”
“produce
notification/alert”
Sliding window,
triggered by transaction
10:22
Pattern:
Magic carpet

28. Streaming Data Platform Architecture – Example 1
28
Not actual ING code

29. Streaming Data Platform Architecture – Example 2
Application Flow:
Business Flow:
-101 -13 -98
Transactions
13:58 14:24 15:02
Prediction:
Customer balance will
be zero at 16:11
Pattern:
Customer is shopping
Alert:
Ask customer to
transfer money
Option 1:
Do nothing
Option 2:
Send Whatsapp
Option 3:
Transfer money
0.34
0.13
0.57
-71
15:22
RawToRelevance Model Execution Engine Post-Processor
“filter and detect
pattern”
“determine
relevancy”
“produce
notification/alert”
Sliding window,
triggered by balance < 400
15:22

30. Streaming Data Platform Architecture – Example 3
Application Flow:
Business Flow:
P1 P2 P3
Page visits
18:01 18:03 18:06
Prediction:
Customer is unclear
what to do
Pattern:
Customer is
interested in a loan
Helpdesk calls
customer to give
support
Option 1:
Do nothing
Option 2:
Call customer
Option 3:
Send email
0.23
0.81
0.61
P4
RawToRelevance Model Execution Engine Post-Processor
“filter and detect
pattern”
“determine
relevancy”
“produce
notification/alert”
18:21
Sliding window,
triggered first page hit

31. Streaming Data Platform Architecture – Example 3
31
Not actual ING code

32. Streaming Data Platform Architecture
32
Application Flow :
Kafka Events:
Business Flow:
Business
Users
System configuration:
Machine
Learning
Environment
Configuration
GUI
(future scope) Data
Scientists Feedback loop
DSL
Offline training
RawToRelevance Model Execution Engine Post-Processor
“filter and detect
pattern”
“determine
relevancy”
“produce
notification/alert”
Get
Customer
Profile
Apply
Selection
Criteria
Apply
Model
Detect
Pattern
Get
Intermed
Event
Filter
Send
Output
Create
Intermed
Event
Get
Business
Event
Create
Business
Event
Format
Event
Get Raw
Events
Raw
Event
Business
Event
Alert / Notification
Event
Intermediate
Event
openscoring.io
Control
Stream
Online scoring
Error
Stream

33. Technology

34. Benefits for ING:
• true streaming: per-event processing, no micro-batching
• open source, active community
• high volumes, low latency
• connectors to Kafka
• enterprise ready
• Scala api
Features that we use:
• state management and fault tolerancy: savepointing, checkpointing  exactly-once
semanctics
• time windowing: event time, flexible (e.g. sum all transactions in the past minute)
• Complex Event Processing
ING chose Apache Flink for its Streaming Data Platform
34

35. Performance and scalability tests
35

36. PMML is the de facto standard for model representation
• XML-based interchange format for predictive models
• Support of hot-replacing an algorithm (configuration) at runtime by just providing an updated PMML
model and interpreting (scoring) it with openscoring.io
• PMML models can be created by a large number of tools, including: Spark, SAS, R, Knime, Python, H20.ai
• Large number of supported machine learning models
• Working together with the open source community on FlinkML
• Next steps: PFA, TensorFlow
PMML (Predictive Model Markup Language) bridges the gap
between data scientists and data engineers
36
Type Model support
Simple business rules • Rulesets, score cards
Complex business rules • Sequences, association rules
Predictive models • Naive Bayes
• k-Nearest Neighbors
• Regression: linear, logistic, regularized
• Trees: decision trees, random forest
• Support Vector Machines
Advanced models • Bayesian Network
• Neural Network (Deep Learning)
• Gaussian Process
Other • Time Series
• Cluster Models
Openscoring.io

37. PMML Example
37
Not actual ING code

38. • Cross functional squads
• Business, dev, ops, data scientist
• Reusing ING standard building blocks and processes
• Infrastructure, monitoring
• Continuous integration & delivery in DTAP environment
• Develop: Gitlab  Jenkins  Artifactory
• Using Flink’s savepointing mechanism
• No data loss, guaranteed
• Limited downtime
• Scalability, resilience and robustness
• Flink gives us automatic restart on failure, using checkpointing mechnism
• Rolling updates of infrastructure and middleware leads to no downtime
Flink fits the development & operations model with the ING way
of working
38
Design
Develop
TestDeploy
Monitor

39. Squad
In ING’s one way of working, ‘business’ and ‘IT’ go hand-in-hand
SquadSquad
Chapter
Guild
DS
DA
OpsOps
CJE
DS
DA
DS
CJE
DS
Ops
DS
CJE
Dev DevDevDevDev
Tribe

40. Conclusions

41.  ING is an “IT company with a banking licence”, whose competitive advantage is in its use
of data and analytics. To excel, we’ll handle bigger volumes of streaming data with higher
performance demands.
 ING build a streaming analytics platform as a generic, multi-purpose solution with fraud
detection and actionable insights as it first use cases.
 Enterprise-readyness and security of Flink is continuously improving; although major
improvement points are on the topics of data retention and lineage (GDPR requirements).
 With PMML we have a standard format that is very well suited for multidisciplinary
teams, where data scientists work on the same user stories as engineers.
 We set up a streaming analytics community within ING, and work together with industry
leaders (Lightbend, DataArtisans).
Why is Streaming Analytics with Flink the right choice for ING,
moving forward?
42

42. 43
Questions

43. We’re hiring!
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