10 Lessons Learned from Meeting with 150 Banks Across the Globe

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10 Practical Lessons about
BigData & Analytics Journey
Real Insights from Customers

1. There are clear
leaders in each market
Every Market has a select few companies
which are ahead of the pack and already
showing substantial benefits from using
BigData and Machine Learning.
The Gap is widening with the rest of their
peers and the cost of catching up Increases.

Amazing Results
already
• Marcus:
• New Digital Offering
• Risk Modeling at the core
• USD$4 Billion in loans in 2 years
• Contract Intelligence (COiN) platform
• Manual review of 12,000 annual commercial credit
agreements normally requires approximately 360,000
hours
• Machine learning technology same amount of
agreements could be reviewed in seconds
• Risk Management
• Shameek Kundu, Standard Chartered’s chief data
officer says most of its AI risk projects “are about
improving our risk appetite, reducing the risk but also
increasing our ability to take risk in some situations”

2. Real Transformation
requires Buy-in from the Top
• Needs full corporate focus to acquire data
and transform enterprise
• Reflected in New organization structures:
• New Innovation Centers
• New Positions - U.S. Bank’s Artificial
Intelligence Innovation Leader
• New Parallel Organizations
• Separate faciliies

3. Follow the Money
• Don’t start a project without clear revenue impact objectives
• Pick Projects based on Return
• Measure Pre-Post
• A/B Sampling

4. Old vs New

Cost Reduction vs New Revenue
⬢ Easiest ROI to measure
⬢ Fastest economic impact
⬢ Long Implementation
cycles
⬢ Short Lived impact
⬢ Does not transform a
business
⬢ Selling a dream
⬢ Harder to Structure
⬢ Harder to Justify
⬢ Needs a business purpose
⬢ New measures to be created
⬢ Lasting Impact
⬢ Transforms a business
Cost Reduction New Revenue

5. KISS - Short Cycles
(Fail-Fast)
• "Keep it simple”
• Teams improve with full
cycles
• Complexity is the enemy of
reliable models

6. Intersection of Disciplines = Mixed Teams
Domain
Knowledge
Technology
Infrastructure
Mathematical
Models
How each market behaves
Buying/Selling Dynamics
What strategies work
Moving Average
Models which signal Divergence
Complex Multi-Variant models
Vast Data Storage
Analytics Platform
Back-Testing Platform
Interfaces to interact with the market

7. Data is Your Most precious Resource
⬢ Assign Data Owners:
– Senior
– Liable
⬢ At the Source:
– Clean Data
– Label Data
– Cross Reference Data
⬢ Keep Everything in its
raw format
⬢ Label-Label-Label
⬢ Create Domains for
each Use
⬢ Data Governance
across all Data
⬢ Business driven
Access Policies
Organizational Data Policies

8. The Cloud is Here
FSI Clients Cloud Adoption (Informal Survey)

Hidden Costs
Cloud Lock-in
Different Paradigm, Complex Choices
Mistakes can be Public
Governance, Risk & Compliance
Caution!

Develop an Enterprise Cloud Data Strategy for Your Business
Provide a single pane of glass for your enterprise data assets
Apply Universal data governance and security policies no matter where it resides
Avoid vendor lock-in - Remain Cloud Vendor Independent
Manage cost, reduce risk and optimize resources

9. Making Models Great Again!
• Models improve with two things:
• More Data
• Frequent Running
• Machine learning algorithms have been
around since 1950 or earlier
• The breakthrough is the ability to perform
complex mathematical computations in a
short amount of time over huge sets of
Data (Big Data), over and over, faster and
faster
• Correlation does not mean Causation
Mathematical
Models
AI Algorithms
Parallel
Computing
BigData
Cheap
Storage &
CPU
Run Complex Models
In useful time
Data is the
Food for Models
BigData is possible
Due to Cheap Storage
And Hadoop
Fast Parallel Computing
Possible due to Fast Cheap
Computation Hardware
Store Large Amounts
Diverse Data
Scalable
Cost Efficient
Fast
Run Models in Parallel
Use Fast GPU’s
Use fast RAM
Off-the shelve Basic
Models
Adapt and train
models to my data
Run Models in Parallel
Simple Parallel
Processing Platform
& Implementation
Machine
Learning
Renaissance

10. How does a leader look like?
• Full organization cultural transformation
• Data driven products, Data driven business
• Data Driven Organization
• Internal Data augmented with External
Data
• Data Library
• Data Domains
• Predictive
• Real-time
The Analytics Journey
DESCRIPTIVE
Analytics
What Happened?
PREDICTIVE
Analytics
What Could Happen?
PRESCRIPTIVE
Analytics
How Can We Achieve
The Best Outcome
COGNITIVE
Analytics
Tell Me The
Best Course of Action
INSIGHTS
HOW TO BE RIGHT
MOST OF THE TIME
DIAGNOSTIC
Analytics
Why did it Happen?
account distributions
average balances
household characteristics
branch statistics
small business information
cross-sell ratios
CompetitiveAdvantage
Alerts, querying,
searches, reporting,
static
visualizations,
dashboards,
tables, charts,
narratives,
correlations, simple
statistical analysis
Regression analysis,
A|B testing,
pattern matching,
data mining,
forecasting,
segmentation
Machine learning,
SNA,
geospatial pattern
recognition,
interactive
visualizations
Graph analysis,
Neural-networks,
Machine & Deep
learning,
AI
DESCRIPTIVE DIAGNOSTIC PREDICTIVE PRESCRIPTIVE COGNITIVE
FORWARD LookingBACKWARD Looking
Complexity

10 Lessons Learned from Meeting with 150 Banks Across the Globe

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