AI can be used to create sophisticated tools to monitor and analyze behavior and activities in real time. Since these systems can adapt to changing risk environments, they continually enhance the organization’s monitoring capabilities in areas such as regulatory compliance and corporate governance. AI systems can adapt to changing risk environments continually enhance the organization’s monitoring capabilities Better manage regulatory compliance and corporate governance.

1. Karan Sachdeva
IBM Asia Pacific
karan@sg.ibm.com
M- +65 9028 3694
AI: A risk and way to manage risk

2. Digital Risk: Where There is Money, There is Risk
“Opportunity and risk come in pairs”
Bangambiki Habyarimana,
The Great Pearl of Wisdom

3. risk manŸageŸment
noun
(in business) the forecasting and evaluation of financial risks together
with the identification of procedures to avoid or minimize their impact
What is Risk?
artificial intelligence
noun
Understand Reason Learn Interact

4. Risk Management Scenarios addressed with AI and Data science
Predictive
Analytics
Anomaly
Detection
“I don’t know what to measure”“Here’s how big my problem is”
Applications-
Credit Risk
Transaction fraud
Identity theft
Insurance claims
Applications-
Rogue trading
Money laundering
Terrorist financing
Compliance
Anomaly detectionPredictive Fraud Analytics
Data Science = Applied AI

5. Risks with AI and Data Science
1. Algorithmic bias
2. Data Quality Issues
3. Programmatic errors
4. Risk of cyber attacks
5. Legal risks and liabilities
6. Reputational risks

6. Data is the Primary Resource for Risk Management
Customer
Insight
Compliance is mandatory for any data strategy
Data Science and AI can transforms risk
from a cost center into a profit center and
enables immediate rather than staged
benefits.Cost Savings
Compliance
Competitive
Advantage

7. 7
Risk Management Challenges are compounded by the ever increasing
volume of data and the need for AI
of data is either inaccessible,
untrusted or unanalyzed80%
of data scientists’ time is
productively utilized – rest is spent
finding, cleaning, organizing data20%
only
AI
Create a trusted analytics
foundation
COLLECT
Make data simple & accessible
ORGANIZE
ANALYZE
AUTOMATE
Scale insights on demand
TRUST
Achieve trust & transparency
Apply ML everywhere
of enterprises do not yet
understand the data required
for AI algorithms
81%
IBM Cloud / © 2018 IBM Corporation

8. Top 5 Best Practices to manage risk with AI and Data Science
2. Getting the foundation right- Single Integrated Data Platform
3. People: Data Engineers, Data Scientists and Business Executives
4. Defining ROI and charging back
5. Trust and Ethics- Deliver in constraints of regulatory pressures and data
privacy.
1. Identifying risk areas and business problem

9. 1. Use Case Generation and Prioritization

10. 2. Integrated Modern Data Platform- IBM Cloud Private for Data
IBM Cloud Private for Data (Multi-Cloud)
Business
Users & Analysts
Data
Engineers
App
Developers
Data
Scientists
Data
Stewards
Custom
Extensions
Enterprise Cloud
Microservices
Containerized
Workloads
Multi-Cloud
Provisioning
Data & AI Microservices
Analyze Data Trust AI Infuse AIOrganize DataCollect Data
10

11. 11
3. Get the people equation right
Architects data pipelines and
ensures operability
Gets deep into the data to draw
insights for the business
Works with data to apply insights
to business strategy
Plugs into analysis and code to
build apps
DEPLOY COLLECT Data Engineer
Data Scientist
Business Analyst
App Developer
Governs data and ensures
regulatory compliance
Data Steward
CXO
Sys
Admin
Access
data
Transform:
cleanse
Create
and build
model
Evaluate
Deliver and
deploy model
Communicate
results
Understand
problem and
domain
Explore
and
understand
data
Transform:
shape
ANALYZE ORGANIZE

12. 5 X
R O I
4. ROI- Much More then $$$

13. 13
Manage fluid data with built-in
protection and compliance
(e.g., GDPR)
Profile, cleanse, integrate
and catalog all types of data
AI-based Metadata
Management and Data Lineage
Persona-based experiences
with built-in industry models
Govern data lakes and data
warehousing offloading
5. Trust & Ethics
Create a trusted, business-ready analytics foundation
Containerized Integrated End to End Analytics Platform
Seamless hybrid and
- multi-cloud support
Ethical
and
Trusted
Data
IBM Cloud
Private for Data
Policy and business driven
visibility, discovery and reporting

14. Benefits of choosing IBM Cloud Private for Data based
architecture for Risk Management
1) Big Data: Wide velocity, volume and variety of fraud-based
data from multiple sources;
2) Faster: Automates labor-heavy fraud data tasks, such as
data preparation and organization;
3) Easier: Easily create & test the best-fitting anti-fraud data
science models.
4) Secure: Robust data governance and metadata
management capabilities for AI model inbuilt..
14

15. 15
One of largest bank in APAC required centralized risk management intelligence to
enable proactive identification, validation, and management of risk across a broad array
of retail portfolios.
IBM delivered a comprehensive set of risk management information requirements –
including standard and custom risk and finance metrics. The system delivers
centralized analytical capabilities, ad-hoc reporting, and dashboards modeled on the
risk management value chain.
Benefits
§ Centralized and efficient risk analysis, intelligence, and reporting
§ Integration with Basel II data, portfolio segmentation, and Economic Capital inputs
in addition to traditional and other emerging risk metrics.
§ Capability for broad, deep, and reliable view of risk from many perspectives
§ Scalable and extendable to meet emerging business needs
“IBM delivered the expertise,
sense of urgency and
collaborative approach
required to design, develop
and validate the Risk MI
Platform. IBM neatly
integrated into our business
and technology teams. The
combination of IBM
leadership, business, technical
and collaborative skills were
key to our success in
articulating the vision,
delivering on the promise and
easing the transition aspects of
moving to a new enterprise
platform.”
— VP of Retail Credit Risk
Improvements In Risk Management Intelligence And Integration Of Risk And Finance
Challenge
Solution
Top Global Bank- Risk Management Transformation using Data Science

16. IBM industry leadership
The Forrester Wave
Predictive Analytics & Machine Learning
The Forrester Wave
Machine Learning Data Catalogs
The Forrester Wave
Conversational Computing Platforms
IBM
IBM
16
IBM #1 in AI
Market Share
Industry Design
Awards
Reddot
Design Awards
IBMIBM

17. Engage experts to monetize your data and get results in less then 4
weeks
IBM’s Data Science Elite team IBM Cloud Private Experiences
What do we offer?
ü Free 14 days Sandbox for IBM Cloud Private for Data.
ü Experience a 20 minute guided journey to build AI-
powered applications
ü Schedule 30 mins expert consultation
ibm.biz/experienceICP4D
Ibm.com/analytics/expert-advice
Join us at APAC AI Council
An exclusive community of like minded business and technology leaders to be the first to learn about a new ideas in AI, ML
and data science space
https://goo.gl/forms/Z4funOJnWf6OFKHz2
What do we offer?
ü Free onsite engagement
ü Identify use case(s) & Minimal Viable Products via
discovery & design workshops
ü Collaboratively build & evaluate data science and
machine learning models
ü Mentor & enable client teams hands-on
www.ibm.com/analytics/
globalelite/ibm-analytics-data-science-elite-team

18. 20
18
Karan Sachdeva
IBM Asia Pacific
karan@sg.ibm.com
M- +65 9028 3694