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Preface

From the Editors’ Desk

1. Post-Crisis Analytics: Six Imperatives 05

2. Structuring the Unstructured Data: The Convergence of 13

Structured and Unstructured Analytics

3. Fusing Economic Forecasts with Credit Risk Analysis 21

4. Unstructured Data Analytics for Enterprise Resilience 29

5. Why Real-Time Risk Decisions Require Transaction Analytics 37

6. Ten Questions to Ask of Your Optimization Solution 47

7. Practical Challenges of Portfolio Optimization 55

8. Analytics in Cross Selling – A Retail Banking Perspective 61

9. Analytics as a Solution for Attrition 69

10. Customer Spend Analysis: Unlocking the True Value of a Transaction 770 11. A Dynamic 360 Dashboard: A Solution for Comprehensive 85

Customer Understanding

12. Developing a Smarter Solution for Card Fraud Protection 93

13. Using Adaptive Analytics to Combat New Fraud Schemes 103

14. To Fight Fraud, Connecting Decisions is a Must 109

15. Productizing Analytic Innovation: The Quest for Quality, 117

Standardization and Technology Governance

16. Analytics in Retail Banking: Why and How? 125

17. Business Analytics in the Wealth Management Space 135

Analytics for a New Decade

Revitalize Risk Management

Optimize to Drive Profits

Understand Your Customer

Fight Fraud More Effectively

Improve Model Performance

Leverage Analytics Across Lines of Business

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Content

The case for cross-selling to the existing customers of a bank is an easy one—the difficult

part is executing it. Today, there are several different techniques for cross-selling effectively.

The common thread that runs across them is data and analytics. Predictive analytics based

on various models have created offers that are just right, just in time. Data mining and

analytics have helped in discovering trends and populating models that are the backbone

of predictive analytics. Value analytics is another approach to cross-selling that is available.

The call center, the branch, the web—every distribution/ service channel—all leverage

analytics in some way to cater to the entire gamut of customer needs—not just what the

customer seeks. This article analyzes the different ways in which cross-selling works

with analytics, its intrinsic challenges, and the emerging trends in the analytics field.

08Yamini Aparna KonaSenior Consultant,Infosys TechnologiesLimited

Balwant C. SurtiIndustry Principal and Head-Solutions Architectureand Design Group,Finacle Solutions ConsultingPractice,Infosys TechnologiesLimited

Why Cross-Selling is Imperative

The experience of many financial institutions

shows that the cost of selling an additional

product to a current customer is one-fifth

the cost of selling the same product to a

new customer. This explains why cross-

selling, i.e., selling a bundle of products and

services to the client (usually an existing one),

is being increasingly considered the cornerstone

of the retail financial industry.

As other sources of organic growth (for example,

loan demand) have slowed, and adding new

clients becomes increasingly difficult and

expensive in a highly commoditized industry,

selling more products to existing customers

makes great business sense for a bank. It is an

excellent way to increase revenues and indirectly

improve customer retention, because customers

with more products tend to be more loyal.

Customer attrition rates are inversely proportional

to the number of products held—the more products

you sell to the customer, the lesser is the chance of

the customer leaving you. As a result, moving

from a silo-product mentality to a consultative

selling approach has resulted in a proliferation of

cross-sell initiatives in the banking segment.

Analytics in Cross Selling – A Retail Banking Perspective

Analytics in Financial Services

Approaches to Cross-Selling

Cross-selling is selling additional products

to existing customers or prospects. It may

happen along with the initial sale or after

the initial sale is made. Often, the customer

may not explicitly mention specific needs

or be aware that the bank offers products

that meet their needs—cross-selling taps into

this unmet potential using a variety of

techniques:

1. Person-based Approach: This is based

on either the skill of the Customer

Service Representative (CSR) or through

a structured question-based approach. In

either case, the emphasis here is to elicit

the need through customer interaction.

Often, the skill of the CSR is the deciding

factor of success, and little or no use of

analytics is made.

2. Rules-based Approach: The system

defines a set of rules and uses the

information collected from the customer

to arrive at a cross-selling offer. Some

analysis of the customer data is made. For

example, while processing a loan

application, enough information is

available to decide whether the prospect

qualifies for a credit card as well.

3. Value-based Approach: This follows a

portfolio approach to the customer's

assets and liabilities with the bank. Here,

a customer is given a scenario with one

product that he or she has asked for.

Then, based on other information

obtained from the customer, alternate

scenarios are offered. Certain value

metrics (for example, net assets,

installments per month, average rate of

interest paid, etc.) under multiple

scenarios with additional products are

presented to the customer— highlighting

benefits and opportunities for growth.

Value-based approaches are often more

effective in the hands of a skilled advisor

who can extract portfolio-related

information from a client. This approach

also has the advantage of revaluing the

portfolio at periodic intervals and

coming up with other opportunities for

cross-selling.

4. Predictive Analytics-based Approach:

This refers to a set of approaches where a

model (or a set of models) characterizes

customer buying behavior for financial

products. Past customer data is used to

build, refine and modify predictive

models. These models are used to predict

future customer buying—information

used to generate customer offers.

In many circumstances, current or recent

transactions are used as trigger points in

the system, and very often, the current

customer interaction is used as the means

to deliver the offer. Trigger-based models

can range from simple to sophisticated.

Advanced versions can analyze a current

online transaction and couple it with past

data to present relevant offers. Offline

offers are also often analyzed to come up

with the best channel for delivery of the

offer (for example, by mail, through a

call, etc.) and some offers may be made

using a combination of channels used in

an orchestrated manner to get the

customer hooked (for example, a teaser

mail, with a click to a website or a phone

number to call or meet a particular

branch officer). The success or failure of

an offer is also an input to the model to

improve future success rate.

5. Social Networking-based Approaches:

These are not yet prevalent in retail

banking, but here again, a person's social

networks, likes, dislikes, preferences,

recommendations from network friends,

and products used by others in the

network, can be analyzed using

sophisticated models to arrive at probable

cross-selling opportunities. One relevant

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Increased role of data and analytics in cross-selling Figure 1

Person

Rules

ValuePredictive

Social networks

non-financial example is Amazon's

product recommendation engine that is

based on users who make similar

purchases. (Refer Figure – 1 for “Increased

Role of Analytics in Cross-Selling”.)

Barring the first approach, where the number

crunching is done mostly in a person's brain,

every other approach calls for heavy use of

analytics—the analysis of data, as well as the

creation of models, rules engines, and offer

databases.

1. Data Mining can uncover potential

customers who can be targets for cross-selling,

and lead to generation of off-line offers.

2. CRM Systems for sales, marketing and

servicing, can use online analytics to

make cross-selling offers.

3. Predictive Analytics can be used to

make both online and offline offers by

predicting most likely choices of the

customer based on past data.

Analytics in cross-selling Figure 2

Other technology used in cross-selling includes event processing, rules engines and more.

Reporting

BusinessIntelligence

DataMining

PredictiveAnalytics

TextAnalytics

Cross-selling

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Role of Analytics in Cross-selling

The role of analytics in cross-selling is

described in Figure 3.

Role Illustrative Examples of Analytics Used

1. Actual process of cross-selling Predictive Analytics, Portfolio Analysis

2. Analyzing past data to uncover trends and changes in customer preferences

Data Mining, Reporting, BusinessIntelligence

3. Measuring effectiveness of cross-selling Reporting, Web-analytics, Channel Analytics

Role of analytics in cross-selling Figure 3

Cross-Selling Solutions

1. Home-grown or Assembled Solutions:

Amongst internal initiatives to use

predictive analytics, the most common

application is often cross-selling. In-

house data warehouses provide the data,

and business intelligence tools, predictive

analytics tools, rules engines and coding

provide cross-selling solutions.

2. CRM Solutions: CRM solutions from

leading vendors—such as SAP, Oracle,

etc.—come with cross-selling modules,

which can be configured and used along

with the sales and marketing modules of

the solution. CRM analytics are used to

provide the data and power the cross-

selling engine, with the operational CRM

providing the delivery. Some core

banking solution suites that offer a CRM

solution also offer cross-selling solutions

through their customer analytics module

(for example, Finacle Analyz).

3. Point Solutions: These are specific

solutions that are made for the primary

purpose of cross-selling. Though they

may not be part of a suite of products,

point solutions are easy to integrate with

existing point-of-sale/ service solutions.

Often, these solutions are an easy way of

bringing cross-selling to an existing

environment with minimal changes to

existing systems. Most of them rely on

specific technologies and some rely on a

combination of technologies. Examples

include Finacle Customer Analytics,

Customer XPs, and TIBCO's Cross-

Selling Solutions.

4. Channel-specific Solutions: Some

solutions are designed around specific

channels—a call center, for example. These

solutions can monitor call center volumes,

and trigger extensive cross-selling with

incoming calls if the call volume is low.

When call volumes are high, opportunities

for follow-up are generated. Similarly,

outbound call prioritization can be done,

based not only on probable success rates,

but also based on higher probability of

cross-selling.

Challenges in Leveraging Analytics

Analytics certainly present a summative view

of customer transactional and behavioral

patterns. However, the following challenges

are slowing down the adoption of analytics by

financial institutions:

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Lack of Expertise: A combination of

domain knowledge and data analysis

ability, a pre-requisite for effective

implementation of analytics, continues

to be elusive. A banking end-user,

though an expert in his domain,

often faces a challenge to interpret

and analyze the myriad statistics

thrown up by the analytics platform.

A data analyst can compile the statistics

quickly, but is dependent on the business

user's domain expertise to organize

and analyze the data and communicate

it in the form the end-user needs it, to

facilitate an actionable decision.

The whole process may involve several

iterations, resulting in a significant

lag time between data collection and

action and frustration on both sides.

Predictive analytics, especially, are

considered a niche realm, requiring

extensive training for effective

implementation.

· Need for Clean Data: Statistical

models are only as good as the data

fed into them. The majority of statistical

models not only demand accurate data

with the least possible approximations,

but also require that data be scrubbed

and neatly formatted in a particular

way to ensure quick and meaningful/

actionable recommendations. However,

a significant portion of the customer

data, maintained by banks happens to

be inconsistent and siloed, making it

difficult to meet the formatting standards

of analytics models.

· Operational Difficulties: The process

of deploying sophisticated analytics

models usually involves accessing

data from and/ or transferring data

among numerous machines and

operating platforms—requiring seamless

interoperability of various applications

Emerging Trends in the Analytics Field

Over the past couple of years, business

intelligence—of which analytics are a

part—has been catching the attention of

financial services industry decision-

makers, who are realizing the need to

transform the increased amount of

available disparate customer transaction

pattern data into actionable information.

Keeping with the growing interest, the

following important trends are observed in

the analytics field:

and software. This adds to the cost of

implementing analytics models, which

are already considered on the pricey

side—especially by small and medium

banking enterprises. In addition, lengthy,

interactive database queries and complex

analytics scoring processes can congest

networks and adversely affect database

performance.

· Need for Real-time and Advanced

Analytics: End users are no longer

content with analyzing historical data

and understanding past sales patterns.

Financial organizations now want real-

time data streaming and analysis that

facilitates on-the-spot business decisions.

User demands are fast moving from

“what happened” scenarios to “what

may/ will happen” to be prepared with a

ready action plan. Analytics models are

expected to answer what will be the

possible outcomes out of action A vs.

action B. This requires high performance

analytics models that are capable of real-

time data analysis. There is growing

interest among banks in advanced

analytics—though implementation has

yet to pick up. (Refer Figure - 4 for

“Industry Level Advanced Analytics

Adoption Trends”.)

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Expanding/ upgrading implementation

Implementing/ implemented

Planning to implement in the next 12 months

Planning to implement in a year or more

Interested but no plans

Not interested

Don’t know

Industry-level advanced analytics adoption trends Figure 4

“What are your firm’s plans to adopt the following business intelligence technologies?”

Reporting tools

Data visualization, dashboards

Specialized database engines

Business performance solutions

Decision support solutions

Data quality Management

Advanced analytics

Complex event processing

Text analytics

In-process analytics

31% 31% 12% 9% 10% 5% 2%

17% 22% 18% 13% 19% 9% 3%

18% 15% 9% 8% 21% 22% 7%

8%16%27%11%10%11%16%

15% 11% 10% 10% 28% 20% 7%

8%18%28%10%11%

11%

15% 10%

10% 10%9%

9%

8% 6% 6%

6%

28%

28%

5%

7%3%

3% 29%2%

1%

4%

29% 22% 9%

13%

13%

34%

33%

41% 19%

Base: 853 North American and European software decision-makers responsible for packaged applications (percentages may not total 100 because of rounding)

n

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Packaged Analytics Applications are

in Demand – Business users, especially

financial institutions, are increasingly

d emand ing p a ck a g ed an a l y t i c

applications that are specifically

designed for online marketing/ cross-

selling, fraud detection, online credit

analysis, online trading/ investment

advisory, and others. To date, many

organizations have attempted in-house

customization of analytics applications

to meet such specific ends. Such

re-architecture may no longer be

necessary with the emergence of

sophisticated event-driven/ complex

event-processing products and predictive

analytics platforms that can support

these capabilities.

Software as a Service (SaaS) Finds

Demand with Smaller Banks – SaaS

business intelligence vendors are

expected to find great traction. Many

small to medium-sized banks are leaning

towards SaaS models that allow the user

to use the application through

affordable monthly subscriptions

without heavy IT or manpower

investments. Small and medium-sized

banks will leverage SaaS to architect

analytics applications that meet with

their specific requirements.

Open Source Solutions Gain Traction

– Open source analytics solutions are fast

eating into the market share of on-

premise solution providers. Apart

from low cost, convenience is also a

contributing factor—open source

solutions can be deployed alongside on-

premise solutions. Open source is

providing an opportunity for recession-

n

Source: "The State Of Business Intelligence Software And Emerging Trends: 2010." Forrester Research. May 10, 2010

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hit organizations to experiment with a

mix-and-match model and acquire

components of analytics solutions from

various providers at a fraction of the

price. Just as one might assemble spare

parts in the backyard, businesses are

toying with the concept of reaching out

to best-of-breed open source vendors for

various phases of the analytics

process—from chart ing to data

crunching, statistic modeling, predicting,

and reporting. The soaring sales of

vendors—such as Pentaho and

JasperSoft—bear testimony to the

growing popularity of open source in the

analytics field.

Mash-ups Make an Entry – Over the

next couple of years, many analytics

applications are expected to be deployed

through coarse-grained application

mash-ups, which provide a cost-effective

means to embed analytics into business

process—without involving major

re-architecture work.

Improving Analytics Literacy –

Vendors are realizing that providing

applications with rich graphical

r e p r e s e n t a t i o n s a n d c o m p l e x

dashboards is not enough to satisfy

business users, unless the users have

a means of deciphering the output. That

is why we will begin to see vendors

churning out flexible and user-

friendly models with built-in training

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features that will support simulation

using historical data, which helps

experimentation before starting the

actual analysis.

Green Initiatives will Catch Up with

Analytics Vendors –Initial green efforts

in the analytics/ business intelligence

field have come from hardware vendors,

resulting in reduced energy consumption.

Software vendors are expected to enter the

market with offerings that will enable

companies to monitor their emissions

and sustainability exercises.

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Conclusion

Analytics have a key role to play in

helping the banks to increase revenue

by discovering and fulfilling genuine

customer needs. The pressure to increase

sales is even more urgent now than ever

before and the use of online analytics and

predictive analytics can make the job of

cross-selling a non-invasive, seamless part

of every customer interaction. Predictive

analytics provide the much-needed,

data-based support to cross-selling, which

will convert the task of “selling more” into

an act of “fulfilling a customer need” by

preemption. By ensuring that the cross-sell

is aimed at optimizing value to the

customer, banks can gain additional

business as well as customer loyalty and

stickiness.

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