Tech 1. May 18th 2018. Data Driven Innovation 2018. Engineering Department, University of Roma Tre

1. Artificial Intelligence high ROI case
studies from around the world
Pranay Dave
Director Data Science, Artificial Intelligence at Teradata
Approach, algorithms and
operationalization

2. About Us
Business Outcome Led, Technology Driven
• ~1,400 + Customers in 77 Countries
• ~15,000 Employees including
~5,000 Consultants
• Market Cap: US $4 Billion+
• World’s Most Ethical Companies –
Ethisphere Institute
Fortune: Top 10 US Software Company
Forbes 12/2017 : Teradata « 1 Customer focus »
Top in Gartner and Forrester Quadrant

3. 30% improvement in
popularity model
$34M identified in
fraudulent activity
75% of viewings via
personalized
recommendations
99% on-time arrival rate
for trains
20% increase in
customer retention
$3.5M net profit
increase from IVR
flow redesign
$6M revenue
increase via next best
offers
$10M cost reduction
optimizing patient stay
$1M saved via
identifying high risk
churners
2X leads via behavior
based triggers
5-Day reduction in
close cycle time
200% increase in
customer spend
50% time savings for
users working with raw
data
$80M in revenue
identified
40M customer
accounts supported
$3M saved by closing
gaps in member care
10% reduction in RFQ
cycle time
360º real-time view of
customers
28% uplift in
incremental sales
Business Outcomes
And many more…

4. Use-case selection
Human Intensive
Intellectual
Activity
ROI Potential
SAMPLE CLIENT EXAMPLE
Size of bubble is ROI Potential
Few Examples of
intellectual activity
Creating Breakthrough Products Improving Customer Experience
Operational Excellence

5. High ROI use-case from around the world
AI-enabled GPS
Japan
Creating break-through products

6. AI-enabled GPS
• Currently only 7% of cars globally have a
dedicated system to detect stopped vehicles
• We have helped developed AI based GPS
systems which alert of stopped vehicles
Context and Business Problem
Result
• This would enhance the navigation system
they sell and demonstrate command of
advanced technical capability to the public
and competitors
• Detecting Stopped Cars has to be done in
Real time
• We used YOLO Framework which provides the
fastest object recognition
Solution Highlight
YOLO : You Only Look Once
Improving driver safety

7. AI-enabled GPS
Creating training data from Video Training Integrate in GPS
(with Camera)
OPENCV YOLO MULTINET SEGMENTATION

8. High ROI use-case from around the world
Fraud Detection
Denmark
Operational Excellence

9. Fraud Detection
Staying Ahead of Fraudsters
• Danske Bank is one of top banks in Nordics
area
Context and Business Problem
Result
• Decrease in false positive by 60%
• Converting banking transactions into 2D
Solution Highlight
Tens of Millions
€ lost each month
High Fraud Loss Fast evolving fraud
sophistication
FRAUD
NON-
FRAUD

10. Fraud Detection
Staying Ahead of Fraudsters
Customer Initiated Fraudster Initiated

11. Fraud Detection
Staying Ahead of Fraudsters
Current models can
only catch ~70% of all
fraud cases
Traditional ML
models view
transactions
atomically
Often missed
fraud
transactions are
part of a series
Capturing
correlation
across many
features
DEEP Learning Opportunity

12. Fraud Detection
Staying Ahead of Fraudsters
Converting Banking Transaction as 2D image
Non-fraud Transaction Image
Non-fraud
Fraud Transaction Image
X-axis: features, Y-axis: time
Fraud
Non-fraud

13. Fraud Detection
Staying Ahead of Fraudsters
• Decrease in False Positive by 60%

14. High ROI use-case from around the world
Chatbot
Central-Asia
Improving Customer Experience

15. Telco AI powered Chatbot
Improving Customer experience
• Customer is largest Telco group in Asia
• One of the main problems was response time
to customer queries , which was in many
minutes
Context and Business Problem
Result of AI based Chatbot solution
• 90% of customer requests are now replied in
seconds compared to many minutes earlier
• Use Machine learning to identify subject such
as Internet, 4G, Billing etc…
Solution Highlight
• Use AI to understand the question and
generate reply. Use of « standard-box »
responses
• Use of Fast Text , which is open-source Deep
Learning Library from Facebook based on
messenger chats
• Able to identify different word with same
meaning: Package: pakage, package,
pack

16. Telco AI powered Chatbot
Improving Customer experience
Roman/
English
Language
Detection
•Internet
•Billing
•4G
Subject area
Classification
User
Query
1 or more
reponses
Classifier
Matches
Queries to
Queries
InformationRetreival
Match
Query to
Responses
using IR
approaches
FastText (AI-based)
Deep Learning based
Sequence to Sequence
Models

17. Operationalize AI

18. 18
1. Getting into Production fast
2. Staying Relevant over Time
Operationalization is about getting value
Sustained competitive advantage requires
analytic models to be deployed easily and
continuously improved.

19. 19
General Situation
• Business reviews
reports on models
• Multiple stakeholders
and objectives
• IT sent software to re-
implement and deploy
• Ad-hoc process
• Data Scientist sits in
Analytic Silo
• Custom datasets
• Variety of modelling
techniques and
technologies
• Focus on trained model
historical performance
Performance
Reports
Trained
Models
Analytics
Business
IT
MISSING
FRAMEWORK

20. #TDUNIV
Analytic Ops…A smarter way to operationalize
Analytics
Ops
external data
iteration
data-mining
statistics
value-driven
flexibility
exploration
discovery
modelling
blue-sky ideation
security
governance
compliance
curation
deployment
maintenance
integration
testing
engineering
process-driven
Analytics
Operations

21. #TDUNIV
Analytic Ops Framework
Data Scientist
making models
The business using
a trained model
Develop
• Recipe Templates
• DS Lab
• Model scripting (untrained
models)
• Testing, Training, Model
Evaluation
• Version Control (Gitlab)
• Dependency Management
Automate
• Dockerize
• Model Training
• Storage of trained models
• Model Evaluation
• Model Business
Approval/Report Creation
• Comparison vs current Live
model
(Champion/Challenger)
Consume
• Real-time model scoring
engines
• Automatic deployment of
trained model artefacts
• Dashboards and forecasts
updated using new models
• Multi Model management
• Model output logging
Involving: Analysts, Data Scientists, Engineers, Dev Ops, Business Stakeholders
Methodology

22. #TDUNIV
Analytic Ops Framework - Develop
Develop
• Recipe Templates
• DS Lab
• Model scripting (untrained
models)
• Testing, Training, Model
Evaluation
• Version Control (Gitlab)
• Dependency Management
Optimized Code
template for
operational purposes
Github
Version controlling

23. #TDUNIV
Analytic Ops Framework - Automate
Automate
• Dockerize
• Model Training
• Storage of trained models
• Model Evaluation
• Model Business
Approval/Report Creation
• Comparison vs current Live
model
(Champion/Challenger)
MethodologyAbstraction by Docker
Containerization
Dockers simplifies operational
management
Business receive reports to
validate in “business”
terminology

24. #TDUNIV
With Analytic Ops
Consume
• Real-time model scoring
engines
• Automatic deployment of
trained model artefacts
• Dashboards and forecasts
updated using new models
• Multi-model management
• Model output loggingChampion
Challengers
Customer Propensity Engine
MULTI-MODEL SCENARIO

25. #TDUNIV
Teradata Analytic Ops Accelerator
Build
Model
Pipeline
Merge
Request
Model
Consump
-
tion
Engine
• Build images
• Run containers:
• Train model
• Test model
• Generate report
• Save metadata
• Trained model
summary
• Review trained
models
performance
• Deploy approved
models
• Review new model
code
• Trigger automated
modelling
pipelines
• Develop models
Data
Lab
• Unapproved
Models
• Metadata
• Approved
Models
• Metadata
Recipe Templates

26. Key Takeaways
/ Key Areas for High-ROI use-cases
- Creating Breakthrough Products
- Improving Customer Experience
- Operational Excellence
/Operationalization is key for getting business value

27. #TDUNIV
Grazie
#TDUNIV

28. #TDUNIV
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