1.
Creating Opportunities with Big Data
and AI
Vince Leat - Think Big Analytics

2.
2
Big Data and Ai is getting lots of attention
Artificial Intelligence Is
Becoming A Major Disruptive Force
In Banks' Finance Departments -
Forbes

3.
3
The Resurgence of Artificial Intelligence
• Significant advances in hardware
capability
• Rapid progress in research and
applications using neural networks
• Significant technology investments
By 2019, deep learning will provide best-in-class
performance for demand, fraud, and failure prediction. -
Gartner

4.
4
Companies are doubling down on AI Investments
80%
Of
companies
are investing
in AI 59%
Telco and IT
43%
Business and
professional
services 32%
Banking and
customer
services
AI to drive revenue and savings in -
Product innovation/research and development
Customer service/ experience
Supply chain and operations

5.
5
Analyze Anything Deploy Anywhere
Move AnytimeBuy Any Way
Teradata
Everywhere

6.
6
Deploy Anywhere
Move AnytimeBuy Any Way
Analyze Anything

7.
7
“Available solutions
limit the range of
deployment options
reducing flexibility
and choice”

8.
8
Deploy Anywhere
Same Teradata Software across all deployment options enables
seamless application portability
Teradata
Hardware
Commodity
Hardware
Public
Cloud
Teradata
Cloud

9.
9
Teradata In The Cloud Outperforms The Leading Cloud
Database
Teradata on AWS performed more than 17,000x
better than the leading cloud database…
… while the leading cloud
database was 11,000x
more expensive per query
After 10+ Hours
11,000X
17,000X 17,000X

10.
10
Key Benefit
Flexible deployment options
de-risk your short and
long-term architecture
decisions and provide the
agility to change as your
business needs evolve

11.
11

12.
12
We have all of this security footage
, how can we provide additional
value added services to our
customers
New
Customer
Feature
Extraction
Cropped Faces
Feature Selection
Action
Detection
a. Feature Position movement
b. Feature Shapes Changes
ISOM VA Metadata:
Face Box, Skeleton Point List,
Eye Open, Mouth Open,
Mask, Glass, Facial Mark
Point , Detect Zone, Isom
Line, Line Direction & etc.
Emotion Labels: Angry, Neutral,
Happy, Sad, Surprise & etc.
Classification
Model: DNN/
SVM/Adaboost/
etc.
Train
ClassifierInput Emotion
Recognition
Customer
Information:
Identity,
Demographics
& etc.
Identify

13.
13
Facial Recognition Services
Provide a range of value
added services for your
customers and generate
additional revenue
EDW and Advanced Analytical
Services
Security Footage
Facial Recognition Models
and Framework
Cloud Based Services

14.
14 © 2017 Teradata
Auto insurance fraud is
increasing dramatically
Fraudsters are becoming
more and more
sophisticated
We are interested in
understanding fraud
behaviour

15.
15
Vehicle relational networks
Simple Aster code
Dot – Car
Arrow – Collision
Relationship
Line Thickness –
Collision
Frequency
There is a high
suspicion of
insurance fraud
according to the
collision network
of claimed cars

16.
16
Are car purchasers loyal ?
How do I make them loyal and
become repeat buyers?
Customer
demographics
Finance / insurance
contract with customer
Quality
Features
Warranty claims Repeat Repairs
Customer
Management
Household
situation
Delivery Process Campaigns
Personal Situation
Aftersales transaction
Social media
influence
Customer
Product
Dealer
Marketi
ng
Lots of data to work with but what should I focus on

17.
17 © 2014 Teradata
Model A
>90% accuracy
identifying loyal
customers
Key drivers: typical
Loyalty indicators
not actionable
Model B
>90% accuracy
identifying non-loyal
customers
Key drivers for Churn:
partly actionable
Combined Model for maximal accuracy
Predictive algorithms used: Random Forest, GLM
Actionable metrics
used in the final
predictive model –
• Number of leads
submitted
• Answers to CSI surveys
• Number of workshop
visits (non-warranty)
• Number of campaigns
received
• Number of warranty
incidents
Predicting loyalty / attrition

18.
18
In Banking Fraud attacks are
rapidly increasing
At this bank current models
can only catch ~70% of all
fraud cases
How do I improve the models?
Deep
Learning
Classic
Machine
Learning
Rules
Engine
False Positive Rate
TruePositiveRate

19.
19
Current models can
only catch ~70% of
all fraud cases
Fraud & false positive reduction through deep learning
Traditional ML models
view transactions
atomically
Often missed fraud
transactions are part
of a series
Capturing
correlation across
many features
Copyright 2017 Teradata
• From design to models in Production in 8 sprints
• Analytics Ops capabilities to support business units
• Leverage success to expand AI value to additional use cases

20.
20
Every day, tens of thousands of
containers are flowing in and out
of repair shops all over the
world.” It costs the company
more than USD 300 million,
annually.
Several billion lines of data: all inspections,
all repairs, every movement, every booking

21.
21
Significant Savings
Optimizing single repair shops saved the client up to 2 mil. USD per year.
Client spends $1 billion per year repositioning empty containers. Optimization of container distribution,
reduce idle times & empty journeys

22.
22
Teradata
Teradata CommunityTeradata Labs
Experts in Deep Learning,
Image/Audio/Video Processing,
Computer Vision, GPU
200+ Practitioners delivering
Artificial Intelligence Business Value
on Customer Projects
500+ Solution Architects, Business
Consultants and Software Engineers with
knowledge of Artificial Intelligence Tools,
Techniques and Technologies. Deep
expertise across key industries.
Experts
Practitioners
Interest
Industry
Collaborations
Academic
Collaborations
Analytics
Ops
Data
Management

23.
23
Some final thoughts….
Talent
Build teams that
have data, analytic
and business
acumen, organized
to drive value
Quality, Variety
consistent and
integrated data
Data Governance
Specialised HW
and SW with
evolving
frameworks
Technology Business Focus
Philosophy that
drives business
alignment and
creates business
value
Innovation and
improvement to
create shareholder
value
Intent

24.
24 © 2017 Teradata
Thank You