How To Understand Your Business Value From Big Data

Teradata Big Data Analytics

Zagreb Nov 5

th

Stefan Ruhland

Industry Consultant Banking

Teradata Austria

•

Teradata

•

Big Data approach

•

Banking Use Cases

•

Conclusion

2014 Highlights:

- Focus on Advanced Analytics and Big Data

- Revenue 2.7B$;

- Employees growth: from 6000 (2008) to more than 12000 (2014)

- International: 77 New Accounts; +8% Revenue vs 2013

-

25 New Big Data Projects in EMEA

-

We bolstered our portfolio through acquisitions of 3 SW

companies specialized on Big Data Analytics

-

We broadened our ecosystem of technology partners, with new

and strengthened relationships with Cloudera, Hortonworks,

MapR, and MongoDB

Teradata Big Data References

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5

•

Teradata

•

Big Data approach

•

Banking Use Cases

•

Conclusion

Is not about Volume, Velocity and Variety anymor

e….

Big Data Discovery Process: a complex iterative process

Typical Challenges

Data Acquisition

1

3

Data Preparation

2

Analysis

LONG PROCESS

5

Production 7

Big Data Reference Architecture

Discovery Platform integrated with Hadoop, Teradata, Oracle data sources

Agile Discovery Process

–

Solving Typical Challenges

Data Acquisition

1

2

3

Data Preparation Analysis

FASTER PROCESS

5

Production

EDW Model vs Big Data Discovery

EDW Model 3 Releases Over 2 years

Highly Planned & Controlled Slow Release Schedule

• 3x releases 2 years

High Central funding cost Low Risk / High Success

Discovery Model Small Iterative Projects

• 40+ Discoveries / 2 years

Low cost per project

• $50k-$70k per project

BAU funded initiatives High Risk / High Fail

• Iterates to a new project

$3m

Release 1

$3m

Release 2

$3m

Release 3

40+ Projects Over 2 years

3 5 7 9 11 13 15 17 19 21 23

March May July Sept Nov Jan March May July Sept Nov

3 5 7 9 11 13 15 17 19 21 23

March

Release 1

5 7

May July Sept Nov Jan March May July Sept Nov

3 9 11 13 15 17 19 21 23

EDW Model vs Big Data Discovery

(cont’d)

EDW Model

• EDW projects must succeed

• Successful Discoveries

productionised as part of release schedule

Discovery Model

• Many projects“fail”

• Failure is accepted as part of

the process and leads to new innovations and iterative projects

• Successful projects are often

productionised on the EDW for execution

$3m

$3m

$3m 3 Releases Over 2 years

Release 2

Release 3

40+ Projects Over 2 years

Successful Project

11 _{Failed Project}

3 5 7 9 11 13 15 17 19 21 23

•

Teradata

•

Big Data approach

•

Banking Use Cases

•

Conclusion

Big Data Business Value Framework

Overview of Key Areas

Marketing

Customer

Experience

Fraud

Risk

Online fraud Unusual usage of authenticated website based on context Path to Fraud ID the detailed

multichannel steps that precede fraud

Fraud Networks

Find connections between related parties

Claims Fraud Identification of valid v fraudulent customer claims Abandon online purchase

Insight and action to drive follow up

Mktg Attribution

ID the contribution of each contact to a sale

Sales Process Improvement

ID and Improve sales process effectiveness

Path to Churn

ID the path leading to attrition

Identify broken processes

based on multi channel engagement

Customer Sat/NPS

Understand the cause of dissatisfaction and loyalty

Predict Complaint

ID root cause and identify opportunity to intervene and fix

People Like Me Affinity groupings refine people like me recommendations

Pre default risk

Path to default via golden path analysis

Connection risk

High risk associates via social or txn networks

Collections analytics

Identify path to repay via collections

Operational

(Banking)

Reduction in manual Claims review Increased productivity Automate Claims notification

Optimise handling and client satisfaction

Advanced Risk & Pricing insights

Minimise adverse selection

Behavioral-based Pricing

with Telematics data

Operational

(Insurance)

Real & live

POC/POV

Idea

Real Estate Pricing

Using new data and techniques to enhance risk-based price

Call Centre Analytics

Adherence to core processes and service standards at busy times

Sales Compliance & Miss-Selling

Detect key words that mislead client

OnlineT&C’s

Email follow up from opt-out or rapid T&C

completion

Service Efficiency

ID the paths leading to high cost service calls and rectify cause

Understand Online Cross-Session Behaviour

Using log data could help understand the customer journey

The data looks lik

e…

The web data provides you with a lot of facts:

• This visitor is interested in home loans & came from a

competitor site

• They spent 6 minutes looking at 8 re-mortgage pages

• They spent 2 minutes on the 3rd _{page reading the detail}

• They run 3 re-mortgage calculator scenarios, then abandon

• then they called the call centre

• They want to borrow more than they currently owe • We can see pressure on the level of hardcore borrowing • 2 weeks ago they visited the getting married financial

planning web pages for 30 minutes over 2 sessions 15

Remember What the Customer Tells You

Capturing data from forms adds more insight

Quote #1: Home Loan Term 12 Years

The data looks lik

e….

Quote #2: Home Loan Term 20 Years

Term then changed to 15 Years before session abandoned

The data tells y

ou….

• This visitor is happy with the Purpose, Type & Value of loan.

• They are undecided over the term…isthis about affordability of monthly payments?

• Knowing the sticking point helps:

• Gives you a reason to contact the customer • Gives you the‘hook’ to open the conversation

These financial service examples

shows the strength of an

integrated inbound real-time and

outbound solution.

When a special event occurs

online, you can let your branch

network, or personal advisor make

a follow-up call.

For for less urgent matters you can

use a cheaper channel like SMS,

E-mail or place a banner ad on the

customers next visit.

17

Real-time capture of events and actions you might take

Sales Funnels

Analysed Sales Funnels:

-

Personal Loan Quote

-

Savings Account

-

Current Account

-

New Credit Card

-

Mortgage Application

Analysing Digital Journeys

3. Affordability Details

2. Loan Quote Request

1. Application

Data is also actionable -opportunity for personalised

triggers based upon where customers abandon.

4. Personal & Financial

Details Displayed

5. Personal & Financial

Details Update

Leads can be delivered in session (via RTIM) and/or offline

via CIM into the branc

6

h

.

o

P

r

e

c

r

a

so

ll

nal & Financial

Details Displayed

7. Review

Application

Sales Funnels

Analysing Digital Journeys

Analysed Sales Funnels:

-

Personal Loan Quote

-

Savings Account

-

Current Account

-

New Credit Card

-

Mortgage Application

4. Personal &

Financial Details

Displayed

5. Personal & Financial

Details Update

5. Review Application

H o m e p a g e H o m e p a g e H o m e p a g e H o m e p a g e H o m e p a g e H o m e p a g e

Sales Funnels

Analysed Sales Funnels:

-

Personal Loan Quote

-

Savings Account

-

Current Account

Analysing Digital Journeys

- New Credit Card

- Mortgage Application

4. Personal &

Financial Details

Displayed

Personal & Financial

Details Update

Accept Rate: 36%

Review Application

Accept Rate: 27%

Closing the gap in

accept rates is worth

roughly 5-6k sales per

year (worth $3m profit

p.a).

Trial a process with forced update step, at least for certain

Churn Prevention

Customer Retention Improvement Opportunity

Churn Analysis

–

Statistical & Pattern Matching techiques

Churn

Statistical

Pattern

Matching

Space of all possible

customers at risk

of churn…

…cus

tomers that can be

identified through Classic

Statistics, e.g., SAS model

s…

…cus

tomers that can be

identified through pattern

matching via path analytics.

Events Preceding Account Closure

Discovery Process

–

First step

Events Preceding Account Closure

Iterative Process

–

Reducing the

“

Noise

”

to find the

“S

ignal

”

“Com

mission Reduction Request

”

and “Ser

vice Complaint

”

seem

to be

“S

ignals

”

Events Preceding Account Closure

Insight Identification

–

Most common event Sequences (aka

“

golden path

”)

Path to Churn Outputs

Three possible output

Business Rules

New input variables for

current models

New predictive models

Triggers that need to be analyzed to determine whether the bank should add customer names to a list of potential defectors

Identification of new statistically reliable input variables:

– Single Events

– Paths

– Frequent Sub-paths

Building news predictive model from scratch:

– Event path based model

27

•

Teradata

•

Big Data approach

•

Banking Use Case

•

Conclusion

• Strategic Consulting

 Strategic Consulting Service: address organizational

• changes

 Roadmap Service: What Big Data & Analytic Projects generate most revenue, where to start?

• Implementation

 From strategy to production: supporting the organization in making data become a first-class citizen

 Implementing Big Data & Analytic Projects in your organization

• Analytics as a Service

 Far-reaching between you r organization and Teradata

 Shared risk/benefit: for projects: only pay for what brings you value!

• Support

Services on all layers of the Stack

Strategic Consulting Implementation Analytics as a Service

Support

Big Data Governance and Models Aster Analytics Platform All major Hadoop Distributors are

Teradata Partners Platform Implementation

Data Lake Architecture

How can we help?

Consumer Credit Risk Models

«T

raditional

»

Machine-Learning Algorithms

Credit Bureaus data Transactions data Exploratory data

analysis _{variables (ADS)}Model input Forecast model _{(Decision Tree)}

Credit Bureau Data

• Total Number of Trade Lines

• Number and balance of home loans

• Balance of all auto loans to total debt

• Total credit-card balance to limits

• ... Transaction Data

• Number of Transactions

• Total inflow

• Total outflow

• Total expenses at discount stores

• Total clothing stores expenses

• Total restaurant expenses

• Total vehicle related expenses

• Total education related expenses

Deposit Data

• Savings account balance

• Checking account balance

• CD account balance

• Brokerage account balance

• ...

Account Balance

data

31 Source: MIT - Massachusetts Institute of Technology- Khandani, Amir E., Adlar J. Kim, and Andrew W. Lo.“Consumer credit-risk models via machine-learning algorithms.”Journal of Banking & Finance 34 (2010)

Consumer Credit Risk Models

«T

raditional

»

Machine-Learning Algorithms

Credit Bureaus data Transactions data Exploratory data

analysis _{variables (ADS)}Model input Forecast model _{(Decision Tree)}

“…current cred

N

it-

e

bu

w

rea

c

u

o

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m

na

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s su

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a

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y

re

A

di

n

t s

a

co

ly

re

s

s

is

[e.g. FICO score]

Account Balance

data

 Improving current model (higher lift)

 Building new predictive models

are ba _{Path & Graph Analytics techiques}

 In-database analyses, modeling and scoring of the entire dataset

sed on slowly varying consumer characteristics…”

“… machine-learning forecasts are considerably more adaptive, and are able to pick up the dynamics of changing credit cycles as well as the

– Missed payments

–• Shift in spend from discretionary items to essentials; shift in location of spend from higher-end brands to lower-end brands

– Card balances generally increasing

– Canceling recurring subscription payments, e.g. magazines – Shift in spend of debit to credit

– Changes in amount of direct deposit

• – _{Changes in pattern of spending activity, e.g. someone fills up their car consistently at 8am prior to arriving and} work and all of a sudden starts filling up in the middle of the day (potential indicator of a lost job)

– Starting to pull cash down off of a credit card, particularly telling when it is pulling cash down at particular locations like a Casino

– Increasing debt levels across all debt mechanisms – ...

Reuse Data Preparation

Same events used for Fraud Detection + some specific events

• The probability indicators of Default include:

33

Credit Risk Business Improvement Opportunity

Path Analysis of Account Balance

Find Correlation between Account Balance and Default Risk

B

CC

B

A

B

- 1.5 - 1 - 0.5 0 0.5 1 1.5 Time [day]

A

B

C

SAX

(Symbolic Aggregate approXimation)

Path Analysis Statistical Analysis Account

Banking Analytics Use cases

Marketing

Path to Churn.Enable you to study your customers’ omni-channel behavior, in order to discoverAbandonment Paths, that are sequences of events/behaviors that

frequently lead to customers churn. These insights allow to improve churn prediction models and are complementary to traditional approaches.

 Pre-built Path and Predictive analytics functions

Multi-channel Attribution.Help you quantifying channels effectiveness to drive revenue, in order to identify which channels/ads perform the best, calculate true ROI on a per ad basis and/or run time-sensitive promotions by knowing which ads convert the fastest.

 Pre-built Attribution and Path analytics functions

Location Based Offers.Allow you to analyze the locations most frequented by customers and identify the types of spend for each customers and the brand share of that spend. The goal is to improve customer loyalty by providing usage based offers for Credit and Debit Cards and select Merchants to partner with for location based offers.

 Pre-built Path, Graph and Statistical analytics functions

Banking Analytics Use cases

Marketing

Abandoned online Purchase. Enable you to understand in detail how customers progress through the online sales process, in order to identity, understand and fix broken processes where customers exit, get stuck or cycle back. Outcomes are higher conversion and efficiency at each step and more revenue from sales, at a lower cost per sale because re-work is reduced.

 Pre-built Path and Graph analytics functions

Personalized Recommendation Analysis.Allows you to make product recommendations when you knows very little about the customer (e.g. customer is inactive or holds only 1 product), using individual customer browsing combined with ‘people like you’purchase behaviors. The goal is to improve product penetration amongst segments that are either inactive or only holding one.

Banking Analytics Use cases

Fraud

Path to Fraud. Enable you to analyze cross-channel customer activities to identify common sequence of events leading up to a fraudulent transaction. This new cross-channel fraud prediction path analysis is a substantial improvement vs. the current fraud models and it's complementary to them.

 Pre-built Path and Clustering analytics functions

Fraud Networks. Enable you to use graph-based approaches to uncovering anomalies in customers' graph, where the anomalies consist of unexpected

entity/relationship and patterns that are often related with fraud behavior. As with path to fraud, these insights allow to significantly improve fraud prediction models.

 Pre-built Statistical, Graph and Path analytics functions

Banking Analytics Use cases

Credit Risk

Connections Risk–Consumer & B2B Networks. Allow you to build and analyze customers network, in order to identify explicit and implicit associations and actionable insights that can be vitally important for credit risk detection.

 Pre-built Path, Graph and Predictive analytics functions

Pre-Default Risk. Enable you to identify genuine signs of default pressure, through the analysis and comparison of events, transactions, interactions and changes over time. The key objective is to discover customer behaviors that frequently lead to customers default and translate them in Business Rules.