In the latest edition of FINsights, Infosys partners with FICO to walk you through the various issues and roadmaps of analytics with the goal of providing insights to help launch successful analytics initiatives.

1. Tap into the true value
of analytics
Organize, analyze, and apply data
to compete decisively

2. Preface
Welcome to the Analytics in Financial Services issue of FINsights!
Analytics. As the Information Age advances, business and social discussions about analytics abound.
Particularly among executives who are focused on profitable growth and risk management, the
effective use of analytics is increasingly viewed as critical to success.
In response to growing demand from our clients for information and services related to analytics, we've
dedicated this issue of FINsights, Infosys' thought leadership journal for the financial services industry, to
the topic of analytics. Indeed, this quarter we decided to do something special: we've merged the expertise
of Infosys and FICO to create a one-stop compendium of viewpoints, roadmaps and research pieces
addressing a topic which carries increasing importance in our data-drenched world.
The last decade has been characterized by an explosion in the volume and complexity of information.
Organizations have developed enormous data warehouses cataloguing everything from transaction
details to online activity, to dates of birth. For financial institutions, this abundance of information
represents both a powerful opportunity and a daunting challenge. When effectively organized,
analyzed, and acted upon, it can drive customer retention, reduce credit risk, and improve cross- and
up-selling. However, arriving at the point of proper application requires significant knowledge of and
investment in analytics—not only the science, but also the practice of applying it when facing
tightened regulatory, fiscal and other real-world challenges.
Though the potential of analytics is nearly unlimited, many organizations get caught up in the “whens”,
“wheres”, “whats” and “hows”. This issue has been created as an enabler—a tool for our readers to tap into
the true value of analytics and fully realize the potential of the information available to them. In it, we
address a variety of analytics trends and challenges which have emerged in recent years. From mastering
the intricacies of unstructured analytics, transaction analytics and adaptive analytics, to the governance
and management of information, this issue fuses the know-how of Infosys and FICO to help inform
your analytics strategy.
We hope you find the result of our partnership enlightening. Each article in this issue was forged from
a unique combination of domain expertise, discussions with clients and analysts, exhaustive research,
and inter-company reviews. Our contributors put a tremendous amount of time and thought into
each article, with the goal of producing an issue of lasting value – one that you keep close at hand in
the months ahead. From ideation to publication, this issue has been exciting to develop, read, and
discuss. We hope you'll agree and look forward to hearing your thoughts, comments and suggestions.
Ashok Vemuri
Member, Executive Council and Global Head, Banking & Capital Markets Practice
Infosys Technologies Limited
Charles ill
Executive Vice President, Sales and Marketing
FICO

3. Content
Preface
From the Editors’ Desk
Analytics for a New Decade
01. Post-Crisis Analytics: Six Imperatives 05
02. Structuring the Unstructured Data: The Convergence of 13
Structured and Unstructured Analytics
Revitalize Risk Management
03. Fusing Economic Forecasts with Credit Risk Analysis 21
04. Unstructured Data Analytics for Enterprise Resilience 29
05. Why Real-Time Risk Decisions Require Transaction Analytics 37
Optimize to Drive Profits
06. Ten Questions to Ask of Your Optimization Solution 47
07. Practical Challenges of Portfolio Optimization 55
Understand Your Customer
08. Analytics in Cross Selling – A Retail Banking Perspective 61
09. Analytics as a Solution for Attrition 69
10. Customer Spend Analysis: Unlocking the True Value of a Transaction 77
0
11. A Dynamic 360 Dashboard: A Solution for Comprehensive 85
Customer Understanding
Fight Fraud More Effectively
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
Improve Model Performance
15. Productizing Analytic Innovation: The Quest for Quality, 117
Standardization and Technology Governance
Leverage Analytics Across Lines of Business
16. Analytics in Retail Banking: Why and How? 125
17. Business Analytics in the Wealth Management Space 135

4. From the Editor’s Desk
In 1992, Walmart launched the first terabyte database—the race for information was on. In the nearly
20 years since, large financial institutions and retailers alike have piled up data at a seemingly
exponential rate. 100 terabyte databases are no longer the exception, they are the norm. Businesses
across industries have been remarkably effective at amassing information. Where they have struggled,
however, is effectively and consistently utilizing this information to drive revenues and cut costs.
To make this data actionable, having a strong analytics program is a competitive necessity. From fraud
to risk to marketing to collections, financial institutions have been accelerating their use of analytics,
moving from rearview analysis of historical data to forward-looking predictive analytics—models that
are predictive and drive optimal decisions. A recent Forrester survey found that 31% of IT decision-
makers are implementing or planning to implement advanced analytics packages in the near future. If
your bank isn't investing, your competitors are.
The information race of the last 20 years has paved the way for an analytics race over the next decade.
Firms want to know with more certainty how their customers are going to behave—who is likely to
attrite, where they will spend next, and who is most likely to default on a mortgage or credit card—and
what actions will guide customer behavior in a mutually beneficial direction. How effectively
strategies are developed and implemented to extract and leverage information will likely define the
winners and losers of the financial services industry in the 21st century. In this day and age,
information is indeed power.
This issue of FINsights has been created to help your organization tap into the true value of analytics.
We start by looking forward, delving into the musts of a post-crisis business environment, the
potential of unstructured analytics, and the advances in credit risk analysis. From there, we provide a
series of roadmaps and recommendations for applying analytics to three of the major challenges
banks face today: fraud, improving understanding of the customer, and risk management. The issue
wraps up with a series of articles addressing analytics optimization, model performance, and vertical-
specific analytics. We hope this issue serves as not only an interesting read, but also as a tool in
launching and transforming your analytics initiatives.
Chisoo Lyons
Vice President,
Analytic Science,
FICO
Srinivas Prabhala
Delivery Manager,
Head BCM STAR Technology Group,
Infosys Technologies Limited

5. FINsights Editorial Board
ASHOK VEMURI
Member Executive Council, MOHIT JOSHI
Senior Vice President and Global Head of Sales
Head - Banking and Capital Markets Practice Banking and Capital Markets Practice
Infosys Technologies Limited Infosys Technologies Limited
RAJESH MENON LARS SKARI
Partner Partner and Practice Manager
Infosys Consulting Infosys Consulting
BALAGOVIND
KESAVAN CHISOO LYONS
Head of Marketing Vice President
Banking and Capital Markets Practice Analytic Science
Infosys Technologies Limited FICO
SRINIVAS PRABHALA
Delivery Manager
Head BCM STAR Technology Group
Infosys Technologies Limited

6. Analytics in
Financial
Services
01
Post-Crisis Analytics: Dr. Andrew Jennings
Chief Research Officer
and Head of FICO Labs,
Six Imperatives FICO
While today's business press is filled with the message that analytics can help companies
“do business smarter”, the largest gains will come from using analytics smarter. FICO has
identified six imperatives for analytics in an environment where the past may no longer be
a good model for the future. At the core of these imperatives is the decision model—a working
model that explains the relationships between all the drivers of a decision and its results.
The decision model can be used in business planning and is a critical element in strategy
optimization. Using such tools is essential to understanding the business situation and
creating lasting business advantage.
More analytics does not mean more of the same,
Introduction however. Lessons learned from the crisis must
now shape the way organizations build and
One response to the severity of the recent use analytics. Grave dangers lie in the naïve
economic crisis is an increasing demand application of pre-packaged analytic techniques.
for analytics. Forrester Research predicts that
the market for predictive analytics and data With that in mind, this article describes six
mining will grow at a rapid pace to US $1.8 ways analytics needs to change to drive business
billion by 2014. performance to higher levels.

7. buy a particular product, which related
1. Turn the “360-degree
Customer view” Inside Out products are they most likely to buy within a
specific range of time? Which channels does
The new normal requires that companies will this customer use most, and does that pattern
ultimately achieve integration and vary by season or by day of the week?
coordination across multiple business lines, By using analytics to answer these kinds of
products, channels and customer lifecycle questions, you can predict an individual
management areas. At the same time, top customer's sensitivity to the specific attributes
performers are pushing beyond this effort to of an offer. You can also automatically generate
bridge corporate views of the customer and population segments with similar sensitivity.
focusing on developing a more holistic This is the key insight on which differentiation
understanding of the customer's view. In a can be built.
sense, it is about “getting inside the
customer's head” and taking a 360-degree 2. Model the Decision to
look outward. Optimize Performance
To construct this inside-out customer view,
While companies must continue to improve
companies are bringing together data from
scores and other predictive analytics, the
a widening range of internal and external
largest performance gains going forward will
sources. The sense of urgency that came
come from improving how analytics are used
with the crisis is impelling them to tackle
in decision strategies. It is, after all, better
the thorny integration and organizational
decisions that improve business performance,
issues involved in sharing data across
not better predictive models in isolation.
product lines, channels and customer
lifecycle decision areas. This means there is a need to model more
than individual customers and their
There is another aspect to this effort. No
behavior. There is a need to model the
one doubts that more data and more
decision itself. Decision modeling is a
relevant data lead to better models.
fundamental technique for understanding
Winners, however, won't just use that
and improving decision strategies.
data to build better models—they will use it
A decision model, as shown in Figure 1, can
to ask better questions.
incorporate any number of predictive
Take, for example, the way response analytics along with a multitude of other
and propensity modeling has traditionally inputs.
been done in marketing. Often what you
All decision makers have a view of the way a
are actually modeling is the offer you
particular business situation operates
just made—e.g., “how many customers in
whether they recognize it formally or not.
this new target population will respond to
The great advantage of the decision model
and accept this existing offer?”
concept is that it makes that view explicit
A smarter use of analytics is to model the and ties it to an objective, like maximizing
offer you are about to make. That sales or profit. It also makes clear the decision
means asking questions about which variables—like credit limit and interest rate,
products individual customers are most or channel and price—and the constraints
likely to buy next, and when they are most that need to be met, such as expected losses
likely to make the purchase. If customers not exceeding some pre-determined value.
6

8. Simplified view of a decision model for a credit line increase Figure 1
At the core of this model are predictions initial expectations, rather than simply
about how customers will react to potential following a long list of metrics.
actions. Generally, these are the models that
n because there is an explicit
Third,
determine the effectiveness of the decisions
objective, the decision model forms an
that get created. However, it is very common
excellent basis for the comparison and
for decision makers and analysts to create
simulation of one possible strategy
their decisions with a complete absence of
against another.
any formal understanding of these action-
reaction relationships. Not surprisingly, this In addition to making the drivers of a
leads to lost opportunity. decision clear, a decision model can be
“solved” by mathematical optimization.
The decision model concept creates the best
Optimization pinpoints the single best
starting place for three crucial steps to
strategy for maximizing or minimizing a
improving decisions:
particular goal. The output of optimization is
nadapting the parameters and
First, the assignment, at the level of individual
constraints of the model for some new customers, of the best actions or treatments
circumstance forms a sound way of for what you're trying to accomplish at the
creating new decision logic. You are portfolio or organizational level. The resulting
addressing the change at the structural solution can be expressed as a decision tree.
level, not editing some derived construct.
Using optimization and simulation with a
n because the decision model
Second, well-developed decision model, you can
makes the underlying relationships explore how much impact a current
explicit, it becomes much clearer what constraint is having on your projected results
information should be tracked to and what would happen if you adjusted it. For
understand if a strategy is playing out as example, you could answer the question: “If
expected. You can track results against we allowed a slightly higher level of bad debt
7

9. Optimizing a credit line management decision strategy Figure 2
losses, could we reduce attrition and improve only the economy, but changing customer
profit?” As shown in Figure 2, this process behavior and new regulations. These
identifies a spectrum of optimized developments are causing even more changes
strategies—an “efficient frontier” of potential in consumer behavior.
operating points. Exploring this frontier
To cope with such upheavals, top performers
helps you better understand key performance
are continuing to analyze historical data, but
dynamics and select the optimal operating
also probing their markets by conducting
point that is currently best for your business.
rapid-cycle designed experiments. They're
The drivers of an effective decision are there analyzing the results to learn as quickly as
whether the decision maker recognizes them possible about changing market dynamics,
explicitly or not. An analytically smart and to identify which offers, policies and
business will realize this, and be well along the actions are working best now. They are
path to improving business performance. following the proven principles of
experimental design to learn more from fewer
3. Rethink What it Means to tests. This mature discipline aims at creating
be Data-driven When the experiments whose results can be accurately
Future is Not Like the Past analyzed and causes of variations
understood. A well-designed series of
To use analytics in smarter ways, companies experiments eliminates the need to test every
can no longer rely solely on historical data. option—or even all the best options—because
This can be seen clearly in the financial enough has been learned already to
services industry, where consumers have extrapolate the outcome.
changed their behavior in response to In a high-performing analytic organization, a
economic stress. Creditors have changed certain proportion of new or “challenger”
their offers and policies in response to not strategies will be purposefully designed to
8

10. produce “controlled variation.” If you test individual with a credit score of 620 is likely
only challenger strategies that are close to to have a default rate much closer to that
how you currently do business, you will limit traditionally associated with a score of 614.
what you can learn from your data. If, In a very severe recession, the default rate is
however, you push the design of some likely to look a lot more like that of a 600.
challengers outside of the bounds of business This does not mean that the score is
as usual, you will introduce variation into “broken”—it may rank-order risk just as
your data and expand what you can learn strongly as ever, but the performance of
from it. customers at each score has degraded.
In a practical situation, it is never as simple With this kind of economic impact
as the challenger beats the champion. simulation, lenders can select the economic
Well-designed strategies do not even set forecast they believe is probable, then use the
out with this goal in mind. They set out to associated index metrics to adjust their score
push the relationships that underlie the cutoffs for credit approval. In this way, it is
decision model so that learning increases possible to maintain a fairly consistent
understanding. Think of them not as default rate across changing economic
challengers to replace a champion, but as conditions.
learning strategies. This cycle, in turn, leads
to a new champion and another round of 5. Balance Automation
testing. By exploring a wider range of with Expertise
possibilities, you increase the chances of
discovering unique insights that might More analytics does not mean less need for
lead to competitive differentiation. You are human expertise. Automated discovery of
also less likely to be caught flat-footed when patterns and automation of modeling
the forces of change shake up the status quo. processes are important tools that can speed
the process of improving decisions. The
4. Factor Macro-economic economic meltdown has shown, however,
Forecasts into Analytics that letting the machines do all the thinking
can lead to catastrophe.
It is clear that abrupt economic changes, such Analytic expertise informed by deep domain
as severe recessions, can cause actual customer knowledge is essential to building effective
behavior to shift from what historical data predictive and decision models. This
says it should be. In the recent downturn, expertise is indispensable for dealing with
credit default rates rose significantly above everything from data bias to inadequate
the rates historically associated with standard sample sizes.
credit score ranges.
Today's analytic announcements focus on the
Today, there are new analytic methods for need to use analytics to crunch through
forecasting how such macro-economic petabytes of data. While this is true, it is only
conditions are likely to impact customer part of the story. Given today's dynamic
behavior and change results. These analytics market conditions, businesses need analytics
are now being used to generate an index of to make decisions for which they do not have
how much default rates are likely to increase petabytes of data—or at least not petabytes of
or decrease under a range of economic relevant data. That is why you need to rely on
forecasts. This index enables lenders to see, analytic experts to get the most predictive
for example, that in a moderate recession, an value from what you have to work with.
9

11. Expertise makes the difference between analytic complexity must be justified.
models that perform so-so and those that A senior manager should be able to get clear
perform at a very high level. This answers to: “Why does this customer decision
performance edge comes from the expert's need to be so complicated? What value are
ability to interpret nuances in the data we getting from it? What are we learning?
in order to find the best predictive How is this going to make tomorrow's
characteristics for a desired performance decisions better than today's?”
outcome. It comes from knowing how to
Some complexity, of course, is good. It is
validate models without “over-fitting” them
how a company finds new niches of
to the data they were developed from, and
customers and tries to improve the decisions
how to fine-tune models to a company's
it makes about those customers. Much
specific real-world business conditions.
unnecessary complexity, however, can be
Above all, the analyst's understanding of the
traced back to two root causes: 1) Trying to
context in which a model will be used—and
improve decisions by starting with the
the decision it is supposed to improve—is
decision tree rather than the decision model;
absolutely critical.
and 2) The inefficiencies of the decision tree
as a means of representation.
6. Justify Complexity and
Increase Transparency Editing a decision tree of any size is an
inherently risky venture. This form of
Businesses are operating in an increasingly representation is visually complicated,
complex world, and it follows that analytics and therefore, working within it, one can
must often be complex as well. Nevertheless, easily make mistakes. By comparison, the
Comparison of two decision trees Figure 3
New strategy design tools simplify the visualization of complex strategies. This helps in the comparison of different
strategies, such as in this example, which highlights the score cutoff change difference in two collection strategies.
10

12. underlying decision model can be much nodes, and it is not unusual to have trees
simpler to understand and change. Edit the with many thousands of nodes, hence the
decision model, and you will have a better magnitude of the problem.
grasp of the structural reasons for the changes
you are making, and thus can have Conclusion
confidence in the decision tree derived from
it, no matter how complex. In the post-crisis era, the companies that
When it is necessary to edit or “prune back” succeed with analytics will not be those that
the resulting decision tree a bit, you need simply use more of them, they will be those
tools that help you navigate across it to focus that use them in smarter ways. Above all, to
in on just the places requiring attention. You really succeed with analytics, you need to
also need tools that help you understand how understand the context of the data, the
much fidelity to the decision model you are operational context of the decision and the
giving up by making these edits. underlying business relationships that you
are modeling. You just cannot do that by
The best tools also enable you to compare the
going click-click-click.
original decision strategy to the changed
strategy. Starting with the decision model,
you understand the structural reasons for the References
changes. Comparing the trees then shows you
the differences in the decision logic. This just 1. Market Overview: The Business Intelligence
is not possible when comparing “raw” trees Software Market, Forrester Research, Inc.
with anything beyond a small number of 10/23/09, p7
11

13. Analytics in
Financial
Services
02
Structuring the Unstructured Bala Venkatesh
Group Project
Kiran Kalmadi
Senior Consultant,
Shivani Aggarwal
Consultant,
Data: The Convergence of Manager,
Infosys Technologies
Infosys Technologies
Limited
Infosys Technologies
Limited
Structured and Unstructured Limited
Analytics
Today, 80% of business is carried out on unstructured data—documents, call center logs,
blogs, wikis, tweets, and surveys. Neglecting to analyze such data leads to ignored risks,
uninformed decisions, and missed opportunities. Financial services firms are increasingly
analyzing unstructured data to understand customer needs, prevent frauds and expand the
customer base. Analytics plays a key role in analyzing unstructured data and transforming it
into actionable intelligence. The rapid adoption of social media by the financial services
industry has resulted in an even higher percentage of unstructured data being generated.
This has prompted firms to increasingly look at social analytics to derive structured insights
out of social media. As unstructured and structured data analytics are converging, financial
institutions are looking for analytic vendors to come up with products that blend
unstructured analytics (like social analytics) with structured analytics (risk analytics).
This article analyzes unstructured data, the various analytics vendors in the space, and
applications in the financial services industry.
have significant business value. However, due to
Unstructured Data: What is it?
insufficient search techniques and inadequate
technologies, businesses are usually not able
Unstructured data refers to data that does to derive the right answers—leading to
not exist in a database. Unstructured data can inappropriate decision-making.
be textual or non-textual and takes the form
of text, audio and images (refer to Figure 1 for Unstructured analytics help businesses analyze
more on the different sources of unstructured unstructured data and transform it into
data). Unlike structured transaction data— actionable insights. These primarily consist of
which tells what customers did—unstructured text analytics, audio (or speech) analytics
data provides insights into why they did it, and video (or image) analytics. Social media
what else they want to do, and what problems analytics are an important form of text analytics
they may have. The answers to these questions making inroads.

14. Types of data Figure 1
Structured Semi-Structured UnStructured
? & Legacy
Relation XML
? Web
?
Databases EDI Documents
? E-Mails
?
Spreadsheets
? Wikipedia
?
? with Proper
Flat Files Multimedia (Video,
?
Record Formats Audio)
RSS Feeds
?
Within Corporate Messages
?
Static Real Time
Internal
? ? Center
Customer
Documents Call Logs
Sales Report
? ? Center
Customer
Marketing
? Representative Notes
Material ?Rooms
Trading
Analysts
? ? News
Breaking
Reports ?Pricing
Market
Internet
Formal/Legal
? ? Events
Weather
Filings (SEC, ? Actions
Corporate
FDIC) Chat Rooms
?
Journals
? Social Media
?
Platforms
Text Analytics
Unstructured Data Analytics
Text analytics enable businesses to derive
Analysis of any form of unstructured value from large quantities of text. This text
data that helps transform it into can be available either in existing repositories
actionable intelligence is called or can be newly generated or acquired. This is
“Unstructured Data Analytics”. Structured done by extracting and interpreting relevant
data analytics uses business intelligence information to reveal patterns and
relationships. Figure 2 elaborates the text
tools for querying and reporting,
analytics process in detail.
whereas unstructured data analytics
utilizes text processing and keyword Text analytics is gaining importance in all
searches (to locate documents in servers). industry segments (specifically the financial
Unstructured analytics has evolved over services industry), mainly because of the huge
time, moving towards next generation chunks of textual data being generated
techniques like video and audio analytics month after month by every organization
(which are rarely used in the financial (both within and outside the organization).
services industry) and text analytics With the advent of Web 2.0 & 3.0, there is a
(also known as text mining). greater emphasis on information-sharing
14

15. Text analytics process Figure 2
Information Transforming Analytics Reporting Delivery
Retrieval Text
Collect and
? Content
? Selecting
? Different
? Taking steps to
?
retrieve cleaning, attributes, mechanisms augment
information removing discovering for notifying existing data &
from both duplicates, patterns, results, like store enriched
internal & language interpreting & dashboards, information
external recognition, analyzing the alerts, etc.
sources etc. results
and user-collaboration using social In recent months, a host of social media
networking sites. Interactions and posts on analytic products--from vendors such as
these sites play a huge role in shaping IBM, SAS, Scout Labs, and Radian6--have
consumer sentiments about businesses, hit the markets. These products will help
services, competition, and markets. Hence, banks and other financial services companies
corporations are investing in social media monitor and measure the performance
analytics tools that use text analytics to of their social media campaigns—enabling
understand customer sentiments, and banks to make informed decisions.
address them proactively.
Social Media Analytics Key Vendors in Unstructured
Data Analytics
Social media analytics derive and measure
key results from social media. Social media The unstructured data analytics software
analytic tools use algorithms and approaches market is relatively new, and the vendors in
for automated analysis of blogs, chats, emails this space are still emerging and shaping
and other related social media. Key areas their lines-of-businesses. SPSS (acquired by
addressed by social media analytic tools are: IBM) and SAS are the major players.
There are a number of other vendors who
n the relevant blogs - Identifying
Finding
specialize in offering text analytic—tools and
the relevant blogs and forums for a
tools for the monitoring and measurement
business
of social media.
n
Detecting sentiment - Detecting the
The Road Ahead for Vendors
sentiments expressed about a company,
product or new launch The future lies in bridging the gap between
structured and unstructured data and
n
Measuring the influence and authority
creating products that blend unstructured
of key bloggers - Identifying the key
analytics with structured analytics. The
bloggers and how they are influencing the
challenge for vendors is creating structured
thought process of others on the web
data from unstructured information. The
n topics of interest masked in
Detecting combination of various data sources and
conversations on social network sites. different types of data to drive business is
15

16. definitely worth exploring for vendors. and perspectives about a company's services
The road ahead for vendors will be or products through social media. Most
characterized by: financial services firms still rely only on
structured data analytics for customer
n
Integration of social media analytics
intelligence. Unless these structured data
with text analytics- Vendors will be
analytics are blended with social media
looking to integrate social media
analytics, it is very difficult to achieve
analytics with text analytics. In recent
actionable customer intelligence.
times, social media monitoring vendors
have started to merge with text mining Stock Market Prediction
analytic vendors. Social media
Predicting stock market movements is a
monitoring as a stand-alone capability of
challenge for investors due to lack of
vendors will, thus, not stay for long.
consistent prediction methods. However,
n
Text analytics will be made available research shows that there is a strong
as a component of other applications- relationship between news stories and stock
Text analytics applications will move price movements. Predicting the stock price
away from being siloed applications movements based on news items is gaining
towards being a part or a component of increased importance in the text mining
other applications in the business. In community.
such a set-up, insights derived from
Fraud Detection
analysis of unstructured textual content
will automatically flow on a real-time Financial institutions lose millions of dollars
basis into the business for key decision- to fraud every year. In banking, fraud arises as
making. a result of stolen credit cards, forged checks,
misleading accounting practices, etc.
n
Convergence of various types of
analytics- There will be a rapid growth of Financial services firms need to have
combinations of different types of improved analytical capabilities to reduce
analytics. For instance, text analytics fraud levels and the associated costs.
with predictive analytics is expected to A common thread in the above three
make rapid headway. Financial services application scenarios is a proper blend of
firms will begin using a combination of structured analytics and various forms of
text analytics and predictive analytics for unstructured analytics. A well-blended
risk management and fraud management solution is much better than traditional
on a large scale. analytics solutions.
A key challenge in using unstructured
Applications of Unstructured
Analytics in Banking, Financial data analytics is that the unstructured
Services and Insurance (BFSI) data rarely has a consistent internal
infrastructure, or metadata (unlike the
Figure 3 highlights a few of the specific structured data), and hence it is far more
application areas within the BFSI segment complex to analyze and model. Despite these
where unstructured analytics can be readily difficulties, businesses are incorporating,
deployed. and should continue to incorporate,
unstructured analytics efficiently into their
Customer Relationship Management
processes, mainly because of the extensive
Customers share ideas, insights, experiences business value derived from such analysis.
16

17. Application areas of unstructured analytics Figure 3
Customer Relationship Management
Fraud Detection
Stock Market Prediction
Improving customer experience in a bank using unstructured analytics
Scenario:
noffered several services to customers through its online banking channel. Each
A bank
typically involved multiple complex user interactions.
n using these services felt that the website was not user friendly and they started
Customers
moving to other banks based on that sentiment. They also started sharing negative
sentiments about the bank in online forums.
n
Many customers did not even have the patience to respond to the online customer survey
feedback form.
Challenges Faced by the Bank Solution Recommendation using
Unstructured Analytics
¡
Lack of tools for achieving a 360
? Optimum Solution: A blend of
view of customers, resulting in poor structured analytics using business
customer retention rate. intelligence tools and social media analytics.
Public relations disaster due to the
? ? utilizes social media analytic
The bank
negative sentiments expressed in tools to analyze online consumer
online forums. forums and blogs. Along with this, the
bank analyzes the Voice of Customer
results collected from its own Business
Intelligence (BI) tool.
This blended solution helps to
?
accurately predict the customer
sentiment and invest intelligently
on customer experience management.
This a shift from the traditional
approach of only analyzing the Voice of
Customer survey and Net Promoter
Score, which would not have yielded
the desired result.
17

18. Stock market prediction using unstructured analytics
Scenario:
Stock market research is primarily based on two trading philosophies, namely, a) Fundamental
?
- in which the prediction is based on the security's data—price to earnings ratios, return on
equities, etc., and b) Technical - which uses charts and modeling techniques for prediction.
? fundamental and technical analyses, information from quarterly reports and
Apart from
breaking news also plays a major role in the movement of share price. Prediction has always
been a challenge, since there has not been much success in analyzing this textual data.
Challenges faced Solution Recommendation using
Unstructured Analytics
? stock market prediction
Inaccurate Optimum Solution: A combination of
when a breaking news story or Structured analytics (using technical
quarterly results are declared, leading approach) and Unstructured analytics
to huge losses for the brokers and (using fundamental approach).
investors.
In a technical approach, the historical
?
data of a stock is analyzed and a linear
regression is run to determine the price
trend.
In a fundamental approach, article
?
terms in financial news, shareholder's
reports etc. are assigned a weight. Using
the Bag of Words, Noun Phrasing &
Named Entities techniques*, the textual
key words that relate to “earnings” or
“loss” are identified.
? of both the approaches are
The results
combined to arrive at a predictable
outcome (up, down, or unchanged
movement of the stock price).
? 4 for understanding the
Refer Figure
solution steps in a typical financial
news analytical system.
Financial news analytical system Figure 4
Fundamental Approach Technical Approach
(Unstructured Analytics) (Structured Analytics)
Textual Analytics
New Regression Stock
Techniques like DB
Article Analysis Quotes
Bags of Words
Stock
Error Analysis Stock Market
Market
Prediction Model
Prediction
* These are specific approaches that use linguistic textual representations.
18

19. Detection of fraud in insurance companies using unstructured analytics
Scenario:
? the insurance industry can occur in any stage of the transaction and can be
Frauds in
committed by any party (i.e. new customers, policy holders, third party claimants or any
other party involved in the transaction).
Typical frauds include inflating actual claims, misrepresenting facts, and submitting claims
?
for damages that never occurred.
Challenges Faced by Insurance Solution Recommendation using
Companies Unstructured Analytics
?approach for fraud detection
·Reactive Optimum Solution: A combination of
whereby the insurance companies structured predictive analytics, speech
investigate only after a fraudulent analytics and social media analytics.
claim is made.
? modeling combined with text
Predictive
·Fraud investigation process is lengthy
? and social media analytics can be used
and expensive, as an investigative to detect and prevent fraud.
officer has to investigate personally to
detect any suspicious activity in ? the predictive model are from
Inputs to
claims. internal & external watch lists of
criminals who previously engaged in
Limited fraud prevention mechanisms.
? fraud, diagnostic fraud indicators based
on surveys taken by the claim handlers,
anomaly patterns, profile details of
individuals, etc.
? of the predictive model
The results
combined with the result of claimant's
speech analytics (to detect whether the
claimant is lying or not) along with the
results of social media analytics (to
detect the relationship among the
policy holders) can be used to generate
a risk score of the claimant.
?score can then be used to
This risk
detect fraud customers even before
issuing the policy.
coming up with solutions to integrate
Conclusion structured analytics with unstructured
analytics. By converging unstructured analytics
There is an increasing requirement into the structured analytics mix, businesses
within organizations to inquire and are seeing substantial improvements in the
accuracy and relevance of their analytic
analyze across structured and unstructured
initiatives. Incorporating analytics blends
data. Integrating unstructured data with into business processes is a growing trend;
structured data is quite a challenge. Despite however, they must be correctly applied to a
the challenges, many analytics vendors are specific business scenario, and companies
19

20. must act on the results appropriately.
The convergence of unstructured analytics
with structured analytics is no longer an “if”,
but rather a “when”.
20

21. Analytics in
Financial
Services
03
Fusing Economic Forecasts Dr. Andrew Jennings
Chief Research Officer
Carolyn Wang
Senior Manager,
and Head of FICO Labs, Analytics,
with Credit Risk Analysis FICO FICO
As many in the financial services industry prepare for measured economic recovery, it's
critical to take stock of a key lesson of the financial crisis: that risk, by its nature, is dynamic.
Today's economic realities call for a paradigm shift in risk management—one that includes
new analytics that go beyond the traditional assumption that past risk levels are indicative of
future risk. This article discusses new methods for systematically incorporating economic
data into scoring systems, allowing lenders to balance consumer level information with
changing economic trends. It also shares results from lender applications of this methodology
to “get ahead of the curve” by more closely aligning risk strategies with future performance.
In 2005 and 2006, for instance, a default rate of
Why a New Approach is
Needed 2% was associated with a score of approximately
650–660. By 2007, this default rate was associated
Traditional assumptions that recent default rates with a score of about 710—a 50 to 60 point shift.
will be representative of future defaults works This risk shift was most likely due to economic
reasonably well if the lending environment decline after a period of more lenient lending
remains relatively stable. But in situations where practices, such as adjustable rate mortgages
external factors are changing rapidly, it can be and no-documentation (“no-doc”) loans.
dangerous to assume the risk levels associated Lenders in 2007 who made decisions assuming
with scores will remain stable over time. that 670 still represented 2% default saw increases
Take the recent credit crisis as a case in point. in their default levels, when the actual default
Figure 1 (next page) shows the observed default was about 4.5%.
®
rate at each FICO Score range for real estate loans. Many lenders do attempt to anticipate changes
Each line represents a large random sample of in economic conditions and adjust strategies
existing accounts evaluated over different time accordingly—for example, tightening origination
periods and the default rates one year later. policies and reducing credit lines in a recession,
As the graph shows, the risk levels associated or loosening policies in an upturn. But lenders
with accounts in 2005 (blue curve) versus 2006 often make these changes judgmentally, and as a
(red curve) were already diverging. One year later, result, there is a tendency to over-correct and miss
there were much greater risk levels associated key revenue opportunities, or under-correct and
with all but the very highest scores (green curve retain more portfolio risk than desired.
shifts more dramatically upward).

22. Risk levels can shift dramatically over time Figure 1
Next Evolution of influence the risk profile of newly booked
Predictive Analytics and existing accounts.
n
Competition. As consumers are
To restore profitability in a post-crisis
presented with more (or less) attractive
economy, lenders need an objective and
offers by the competition, attrition will
empirical approach to measure changing
change the population.
conditions and translate these into more
effective strategies. The next generation More specifically, lenders need analytics
of predictive analytics must go beyond that predict the impact of the above factors
the assumption that only past risk levels on future risk levels for each account. In other
are representative of future risk. Instead, words, the analytics must consider the
risk estimates should also account for the macro-economic view of market conditions
impact of: within the micro-analysis of individual
consumer risk. The analytics must be
n
The economy. Consumers' ability to flexible enough to take into account what
repay changes as the economy shifts. is known—the historical data available
Some lower-risk consumers may to derive past patterns and current economic
refinance in downturns, leaving behind conditions—as well as what is expected—
a portfolio of riskier consumers. forecasted views of the future.
Others may reach their breaking points
through job loss or increased payment Given the ready availability of economic data,
lenders can first take into account the impact
requirements. Higher-risk consumers
of economic factors on future risk estimates.
get stretched further, resulting in more
In addition, lenders should begin to track
frequent and severe delinquencies and
changes in their strategies and competitive
defaults.
factors, which can be incorporated into
n strategies. Changes in lender
Lender future risk estimates as the data becomes
policies across the account lifecycle can robust enough.
22

23. Analytics Tuned to Future are expected to behave differently under
Performance varying economic conditions.
This methodology builds upon existing
Next-generation analytics can provide
risk tools used by lenders, enabling quick
lenders with an understanding of how
and seamless implementation. It can be
the future risk level associated with scores
applied to a variety of scores, such as
will change, based on current and projected
origination scores, behavior scores, broad-
economic conditions. Based on past
based bureau scores like the FICO® Score,
dynamics, the analytics derive the
and Basel II risk metrics.
empirical relationship between the default
rates observed at different score ranges When applied to these scores, lenders gain
(e.g., the risk score's odds-to-score an additional dimension to their risk
relationship) as seen on the lender's predictions so they can better:
portfolio, and historical changes in
n
Limit losses. Lenders would have greater
economic conditions. Using this derived
insight on how to tighten up credit
relationship, lenders can then input current
policies sooner and for the right
and anticipated economic conditions to
populations during a downturn.
project the expected odds-to-score outcome
under those conditions. They can examine n
Grow portfolios competitively. Lenders
economic indicators such as unemployment could more quickly determine when and
rate, interest rate and Gross Domestic how to proactively loosen up credit
Product (GDP). policies as markets recover.
With such a relationship, it is possible to n for the future. Lenders could
Prepare
relate the impact of economic factors on simulate the impact of future macro-
odds, default rates and scores. This relationship economic conditions on scores,
can be derived at an overall portfolio level or to better adjust longer-term strategies
more finely for key customer segments that and stress-test portfolios.
More accurately predict default rates with analytics Figure 2
tuned to economic impact
23

24. n
Meet regulatory compliance. Lenders Over a three-year time span, predictions
®
could better set capital reserves by from the FICO Economic Impact Service
2
creating more accurate, forward-looking, reduced the error rate by 73%, compared to
long-run/downturn estimates required the traditional prediction.
by Basel II.
In practice, economically adjusted Grow Portfolios Responsibly
analytics generate a more accurate
risk prediction that's better tuned to Within originations, the lender primarily
economic conditions. For instance, FICO determines whether or not to accept the loan
recently applied this methodology—called as well as the initial loan price/amount. Using
®
the FICO Economic Impact Service—to economically adjusted analytics, a lender can
a behavior score used by a leading US set a cutoff score based on the anticipated
credit card issuer. Figure 2 (previous page) future default rate, as opposed to the historical
compares three metrics: default rate. The lender can maintain the
desired portfolio risk levels by adjusting cutoff
n
The actual bad rate observed on the
scores as the economy changes.
portfolio over time (blue line).
Figure 3 shows how a lender can view the
n rates predicted by the behavior
The bad
current default rate by score range (orange
score aligned to historical odds
line) and predict how the default curve would
performance (orange line)—traditional
1 shift under different economic conditions.
approach .
During a recession, the curve may shift to the
n rates predicted by the behavior
The bad dark blue line. The lender can update its
score aligned to anticipated odds cutoff score from the “Current Cutoff” to the
performance (green line)—FICO ® “Cutoff–Recession” based on empirical
Economic Impact Service approach. The guidance. This allows the lender to limit
economic conditions used were limited losses by proactively tightening credit in
to what was known at the time of scoring. anticipation of the downturn.
Flexibly adjust score cutoffs under different economic conditions Figure 3
24

25. Conversely, during a time of economic traditional behavior score across the full
growth, the curve may shift to the light range of account management actions.
blue line. The lender can update its cutoff
As an example, for a US credit card issuer,
score to the “Cutoff–Growth.” This allows
FICO retroactively used a FICO® Economic
the lender to loosen credit policies in
Impact behavior score in place of the
anticipation of an economic upturn, and
traditional FICO ® TRIAD ® Customer
bring in more profitable customers ahead
Manager pooled behavior score for a credit line
of competitors.
decrease strategy and for collection actions. The
FICO recently partnered with a European key question was: in April 2008 (a period of
® ®
lender to apply the FICO Economic relative economic calm), could FICO
Impact Service to its personal loan portfolio. Economic Impact Service help the lender
Faced with sky-rocketing delinquencies anticipate the economic turmoil six months
that were 2-3 times historical levels, the later and minimize its financial impact?
lender sought to beat the market by
FICO analyzed performance in October 2008
proactively adjusting origination strategies
and compared the different decisions made
ahead of its competition. Using the
by the two scores. Figure 4 shows the line
results from the economically adjusted
decrease results.
analytics, the lender saw opportunity to
improve its profit per applicant by US $11.50. The columns of interest are the “Swap In”
and “Swap Out”, since they illustrate where
A similar approach can be taken for initial
different decisions would be made. The
loan amount and pricing strategies.
second column identifies accounts that
would have received decreases using the
Improve Account
Management Decisions Economic Impact score, but did not
receive decreases by the traditional behavior
Lenders use behavior scores to help score (the accounts would be “swapped in”
manage accounts already on their books if the lender had used the Economic
for credit line management, authorizations, Impact score). The third column identifies
loan re-pricing and cross-sell decisions. accounts that the Economic Impact score
An “economically impacted” behavior score would not have decreased and the traditional
could be used in place of or along with the behavior score did decrease.
Economic Impact score better identifies higher-risk Figure 4
accounts for line decreases
25

26. The highlighted cells show that the behavior also would have not decreased accounts less
score for these two populations are almost the sensitive to the downturn, reflected by
same (swap-in: 643 vs. swap-out: 646). In other slightly higher scores.
words, the behavior score identified both
The actual bad rates seen six months later
populations as at relatively the same risk level.
reinforces that the Economic Impact score
®
However, the FICO Economic Impact score identified riskier accounts (swap-in: 10.5% vs.
was better able to distinguish risk among swap-out: 7.9%). If the lender had decreased
these populations. Using this score, the credit lines on the appropriate accounts, it
lender would have decreased more accounts could have realized a yearly loss savings of
that would be negatively affected by the roughly US $2.4 million and a net savings of
downturn (average score of 625). The lender US $1.7 million, shown in Figures 5–6.
®
Yearly loss savings using FICO Economic Impact Service Figure 5
®
Yearly net savings using FICO Economic Impact Service Figure 6
26

27. Figure 7 illustrates how the lender could
Limit Collection Losses
have saved close to US $4 million by
taking aggressive action earlier. FICO
In times of economic turmoil, it's even more calculated this using the number of actual
critical for lenders to proactively manage bad accounts that would have received
collection efforts. Economically adjusted accelerated treatment, average account
scores can be applied on existing behavior or balance and industry roll rates.
collection scores to help lenders identify
which accounts will become riskier and Combining this roughly US $4 million in
should receive increased collection priority. collection savings with the US $1.7 million
savings from the credit line decrease strategy,
For the same US card issuer, FICO the lender would have saved US $5.6 million.
retroactively used an economically impacted This illustrates the aggregate benefits of the
behavior score in place of the traditional service when used across two areas in a
behavior score to treat early-stage (cycle 1) customer lifecycle. Clearly, the benefits
delinquent accounts. Prioritizing accounts by would be scalable for larger portfolios.
®
risk, the strategy using the FICO Economic
Impact behavior score would have targeted Set more Accurate Provisions
41% of the population for more aggressive and Capital Reserves
treatment in April 2008. FICO then
examined the resulting bad rates six months When setting provisions and capital reserves,
later (October 2008), and saw that these it is important to understand the risk in the
accounts resulted in higher default rates. portfolio under stressed economic
conditions. Having forward-looking risk
In other words, the Economic Impact score
predictions is explicitly mandated by Basel II
better identified accounts that should receive
regulations, and should be part of any
more aggressive treatment in anticipation of
lender's best practice risk management.
the downturn six months later. Using this
strategy, the lender would have been ahead of FICO worked with an Eastern European
its competition in collecting on the same lender to apply FICO® Economic Impact
limited dollars. Service to its Basel II Probability of Default
®
Yearly loss reduction using FICO Economic Impact Service Figure 7
27

28. (PD) models. Using the derived odds-to-score aligned to current and future expected
relationship between its PD score and economic conditions, lenders can more
economic conditions, the lender can quickly adjust to a dynamic market and steer
simulate the expected PD at a risk grade their portfolios for the uncertainties ahead.
level under various economic scenarios.
Thus, the lender can more accurately References
calculate forward-looking, long-run PD
estimates to better meet regulatory
1. Some scores are periodically “aligned” to
requirements and calculate capital reserves.
maintain a consistent odds-to-score
relationship over time—for example, to
Redefine Risk Management ensure a behavior score of 675 equals a
Best Practices
target odds of 30 to 1. Traditionally,
behavior scores have been aligned to the
There's no better time for lenders to re-
odds observed in the last six months.
evaluate risk management practices in order
to better prepare for measured growth or 2. Error rate is defined as the absolute
buffer against a lingering recession. Forward- difference between the actual bad rate and
looking analytic tools will become the risk predicted bad rate as a percentage of
management best practices of tomorrow. actual bad rate.
With improved risk predictions better
28

29. Analytics in
Financial
Services
04
Unstructured Data Dilip Nair
Project Manager,
Srinivasan V Ramanujam
Engagement Manager,
Allen Selvaraj
Senior Technical Architect,
Banking and Capital Banking and Capital Systems Integration
Analytics for Enterprise Markets Practice, Markets Practice, Practice,
Infosys Technologies Infosys Technologies Infosys Technologies
Resilience Limited Limited Limited
The recent financial crisis has shown us how various industries and entities are linked to each
other through a complex web of relationships. This crisis also exposed the ability (or lack
thereof) of companies, industries and even countries to plan and respond to the changes
around them. This article talks about how an analytics platform can help organizations to
monitor and manage changes proactively—thereby infusing a dose of resilience against rapid,
unexpected changes.
Two large financial organizations, Bank-A and its
Introduction
primary competitor, Bank-B, relied on the same
offshore Vendor-X. When Vendor-X had financial
Enterprise Resilience is an effort across the
troubles, resulting in huge attrition and filing of
organization to anticipate and successfully
bankruptcy, these two banks were at risk of
navigate adversity. Literally, resilience refers to
experiencing potential disruption of ongoing
the power or ability to regain the original shape,
projects.
form or position after being bent, compressed or
stretched (subjected to adverse conditions). The Bank-A identified and responded to this incident
term 'Enterprise Resilience' has been subject to in real-time; they worked with other vendors to
various interpretations in the industry as it varies ensure that their projects were de-risked before
from government agencies, to non-government the actual bankruptcy of Vendor-X occurred.
organizations, to financial organizations, to IT Bank-B, on the other hand, was slow to identify
vendors. Enterprise Resilience is not disaster and respond to the incident. Their projects
recovery or business continuity, but is the new continued to depend on Vendor-X, with
DNA for risk management that alters the insufficient back-up, because Bank-A had the
organization's approach from proactive to first mover's advantage to scoop up available
adaptive, assuming that the bridge from reactive resources. What followed is predictable. Bank-A's
to proactive has been crossed. projects were delivered on time and under
budget, and they were able to gain market share,
Consider the following hypothetical example. It
whereas Bank-B lost market share.
is based on a real life scenario involving two
organizations encountering the same incident, While this is a hypothetical scenario, the reality
but responding very differently. of market share gained and lost defines the new

30. world of business. In this flat world, the reported their 20 t h billion tweet).
definition of risk, dependencies, and Another study puts the value of social
risk management have all changed. networking sites beyond the Gross Domestic
Traditionally, risk management delivered Product (GDP) of some countries (Facebook
one-dimensional solutions—focused on – 11.5 billion, Twitter – 1.4 billion and
mitigating risks by addressing vulnerable growing). This valuation is undoubtedly tied
areas. This approach has failed to include to the enormous amounts of unstructured
and address the various interdependencies data that users create on their profile pages,
that characterize the flat world we operate walls or tweets.
in today. By understanding the broader The best way to be prepared for any threat
risk, and managing risk across the is for an organization's operation to mine
extended enterprise, Bank-A demonstrated this huge amount of data and extract
greater Enterprise Resilience. meaningful scenarios. In this article, the
With globalization, enhanced regulatory focus is on how to mine unstructured
scrutiny, a competitive marketplace, data channels with a specific objective
in mind—build better resilience for an
organizational interdependencies (at a
organization. For simplicity, two data
level never seen before), and evolving
channels will be featured in this article
technological challenges emerging, the types
(highlighted section in Figure 1). The
of risks being faced by the organization
concept can be replicated for most other
are new, unique and challenging. Being
information sources:
proactive and adaptive requires that
organizations be aware of the ever-changing n (non-formal): With a strong user-
· Twitter
business and geo-political environment. base, any breaking news is immediately
Doing so requires that organizations harness propagated through 'tweets'. (Tweets are
every bit of data that is available—federal text-based posts of up to 140 characters
and regulatory updates, geo-political news, displayed on the author's profile that are
weather updates, emergency service updates, visible either publicly by default or by
business and financial updates, and social restricted followers.)
networking sites. There are several services nnews websites (formal): Websites—
· Media
that offer the above in a packaged format. such as NewzCrawler, FeedDemon,
However, one of the most undertutilized Google Reader, Omea Reader, Bloglines,
data sources is the unstructured data NewsGator and scoopitonline—act as
emerging from social networking sites. collectors of breaking news from various
According to data scientists, nearly formal news channels and RSS feeds.
80% of today's data is unstructured.
New information channels—like the Mining/ Modeling –
Identification and
internet (Facebook, Twitter), email, instant
Classification
messaging (chat), text messaging (SMS),
and voice-over-IP (phone calls)—are The data channels described in Figure 1 are
generating enormous stores of non- unstructured. The next step in the process
traditional data at a mind-boggling is to mine/ model this data to derive potential
pace. According to recent estimates, Twitter scenarios (highlighted section in Figure 2).
users create approximately 7 Terabytes of There are many unstructured data analytics
data in a day (on 30th July 2010, Twitter packages available in the market (for example,
30

31. Step 1: Twitter and media websites as data channels Figure 1
Rich
Profile Data
(Location
Attributes
and Past
Events)
Twitter
RS
S
Fe
ed
Improved
s
Data Google Mash-ups Analytics Response
Landing Text Store for Geo-locational Reports &
Zone Analytics Engine Times & Cost
(ODS) Tagging Dashboards Savings
Identification
s
eed
& Classification
SF
Media
RS
Websites
HIMI from Infosys, AeroText from Rock perform sentiment analyses to enable
software, Attensity360 from Attensity, behavioral and predictive capabilities.
Lexalytics, Enterprise Miner data-mining
n
· Categorization of data and classifying
workbench from SAS). These packages
it into appropriate buckets, like
provide a very good GUI-based approach
environmental disasters, man-made
for viewing data to build, test and publish
incidents/ threats, stock market changes,
models using:
geo-political activities like administration
n for words, word patterns or
· Searches changes, policy changes, civil unrest, strikes
strings to identify emerging events and to and boycotts, and other forms of
gain insights into potential threats. The disruption to business.
input to this would be a library of words,
n fine-tuned by client, industry
· Patterns
word patterns or strings that are pre-
segment and geography. The objective is
selected and fed to the tool.
to vary these patterns so they can capture
n
· Distinguish message patterns (tweets) for small, medium or large incidents while
emotions and stress. This can be used to avoiding false positives.
Step 2: Identification and classification of incoming data Figure 2
Rich
Profile Data
(Location
Attributes
and Past
Events)
Twitter
RS
S
Fe
ed
Improved
s
Data Google Mash-ups Response
Landing Text Store for Geo-locational Analytics Reports &
Zone Analytics Engine Times & Cost
(ODS) Tagging Dashboards Savings
Identification
s
d
Fee
Media & Classification
S
RS
Websites
31