Analytics
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in Financial
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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
Structured and Unstructured Analytics
13
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
Analytics in
Financial
Services
10
Customer Spend Analysis: Unlocking
the True Value of a Transaction
Vinay Prasad
Principal Architect,
Banking and Capital
Markets Practice,
Infosys Technologies
Limited
Financial institutions have compiled a wealth of customer transaction data over the years.
When properly analyzed, such data can unlock a treasure trove of predictive information—
when the customer will spend, where such spending will occur, and how much will be spent.
This article analyzes spend events, the techniques to identify spend events, and the process of
utilizing spend patterns to predict customer spending behavior.
Introduction
To conduct such an elaborate analysis, a firm
must know:
Transactional information stored in a financial
institution is embedded with information that
forms the basis of a spend analysis. Moving into
the second decade of the 21st century, a key
imperative for banks is to extract this
information and convert it into actionable
insights. Imagine the capability to predict:
1. What was purchased
n
· When the customer will take his/ her next
vacation
2. When was it purchased
n
· When the customer will eat out, where he/
she will go, and how much he/ she will spend
n
· When the customer will spend at the mall
and at what stores
It is no longer enough to know how much the
customer is likely to spend. Marketing managers
globally now want to know the “when”, “where”
and “what”—details which make the analysis
much more useful.
One crucial piece of information missing in
transactional data is the details of the specific
goods or services purchased. As a proxy, the
merchant type can be used to determine the
kind of goods or services the client has
purchased.
Here the transaction date and time is
important, as it will help in identifying the
sequence of events. This will also help in
identifying the frequency of spend on a
particular type of good or service.
3. Who made the purchase
Was the transaction conducted by the main
account holder or by one of the dependents?
This can help gather some demographic
information on the actual purchaser.
4. Where was the purchase made
The Geo-code of the merchant will be of
help in identifying the location of the
purchase (unless the purchase was an ecommerce transaction).
5. How much was spent
one cannot group customers purely based on
demographics with adequate confidence.
Spend analytics in this article will focus on
analysis using Model 2, working from spend
data available mainly in the form of credit
card transactions.
This information is stated on the
transaction.
Using this information, one can take a
number of approaches to define a predictive
model for customer-spend analytics. The
choice of the appropriate model will be based
on various conditions, specific to the case.
Two such models are highlighted below.
Model 1: Pre-defined
Segmentation
Customer
Customers are grouped based on certain
demographic data, with an assumption that
people of similar demographic backgrounds
are expected to behave in a consistent way.
The credit card transaction of a customer in a
particular group is analyzed to derive a
pattern. Here, the bank looks for similar
transactions done within a predefined period
by a group of customers. Once a pattern is
recognized, any new customer who falls
within the group is expected to behave in the
same fashion.
Model 2: Customer Behavior-based
Segmentation
Transactions are analyzed to bring out similar
behavior that has occurred at least a certain
number of times across the customer
population. Once such behavior is identified,
the subset of customers exhibiting such
behavior is analyzed against the rest of the
customer base to bring out the discriminating
factors. Any new customer exhibiting these
discriminating factors is then expected to
behave as per the identified behavior pattern.
This approach is to be used when the
customer behavior pattern is very secular and
78
What is a Spend Event?
A spend event is a set of transactions a
customer makes to fulfill a need. Customers
often make a similar sequence of transactions
if they have the same need. For example,
consider a customer who has the need to go
on a vacation:
a) The customer may book his/ her
itinerary well in advance (say x days as
this can vary).
b) On the day the vacation begins, the
customer spends on a taxi (in this case it
is paid by card).
c) The customer checks in at the airport,
maybe using the credit card.
d) The customer rents a vehicle, requiring a
credit card swipe.
e) The customer checks in at the hotel and
swipes the card.
f)
The customer dines out more frequently
during the vacation, swiping his card at
various restaurants.
All of these transactions would have
occurred in a cluster on the time axis, and
the time span across the transactions in the
cluster would be fairly consistent across the
client base.
To start the analysis, the initial question that
needs to be answered is the time period across
which the data should be analyzed—it could
be one statement month, multiple statement
months, or any other time window based on
the hypothesis being tested.
The next question to be answered considers
the granularity of the data. The level of
granularity will depend on the target
audience of the analysis. For example, while
analyzing the data, a bank may not be
interested in expenses related to restaurants.
This may lead to aggregation of all restaurantrelated expenses during the day as one
expense. Overall, aggregation reduces the
number of records to be analyzed—removing
unwanted details.
Each transaction can be classified by the
merchant's industry. Hence, the transactions
can be associated with the type of goods/
services purchased at a broader level. The
customer-spend across months (statements),
classified by the type of goods and services
purchased, leads to the critical “when”,
“where”, and “what” information discussed
in previous sections:
1. What is the regular set of products and
services a customer spends on
2. Where does the customer usually spend
on these goods/ services
3. How much does the customer spend on
any of these goods/ services
4. Who makes the purchase of specific
goods/ services—the primary card-holder
or the dependent
5. When does the customer make such
purchases—not just the time of day, but one
type of good/ service spend event in relation to
another type of good/ service spend events
Identifying Spend Events
Identifying spend events involves the
clustering of transactions, using a singledimensional distance measure (based on time
gap) or multi-dimensional distance measure
(based on time gap and other attributes like
amount spent). Once the transactions are
clustered, they may be converted into a
spend event by capturing the different
types of goods/ services bought (based on
merchant type), place and time of transaction
(if transactions are not aggregated
across merchants in a merchant type),
time span across all transactions, transaction
amount, and merchant details. For the
purpose of Model 2 (customer-behaviorbased segmentation), the spend event defines
a building block for the construction of a
spend pattern across a larger time frame.
Spend events can be classified as irregular
or regular, based on the recurrence
across time windows being considered in
an analysis (goods/ services may also be
used as a proxy for identifying regular and
irregular spend events, though one has to
be careful, as eating out at a restaurant
for one customer may be a regular spend,
whereas for another, it may be an irregular
one). The identification of the spend
event is based on a number of criteria
that the bank has to determine based on the
nature of the analysis. For example, if the
target is to identify irregular spend events
from the data in Figure 1 (on the next page)
the following factors can be used:
a) Number of transactions per day
b) Location of transactions
c) Day of the week
Based on these criteria, the bank is able
to identify that:
a) The regular spending location is the
NY/ NJ metropolitan area
b) The number of transactions on a regular
work day can range from 2 to 3
Hence, an irregular spend event (marked in
blue) is characterized by:
a) Number of transactions increased to
8 on 6/13
79
An excerpt from a card statement highlighting an
irregular spend event
80
Posted Date
Payee
Address
6/11/2009
ORB MADAQT
ORBITZ.COM IL
ORBITZ.COM IL
6/13/2009
SEARS ROEBUCK 1684
WOODBRIDGE NJ
6/13/2009
Figure 1
Amount
Day of week
-107.07
Thursday
WOODBRIDGE
NJ
-24.99
Saturday
MAID OF THE MIST STORE
NIAGARA FALLSNY
NIAGARA FALL
NY
-11.09
Saturday
6/13/2009
NIAGARA PK CAVE OF WIN
NIAGARA FALLSNY
NIAGARA FALL
NY
-14.58
Saturday
6/13/2009
NIAGARA PK CAVE OF WIN
NIAGARA FALLSNY
NIAGARA FALL
NY
-92
Saturday
6/13/2009
PETRO #371 WATERLOO
WATERLOO NY
WATERLOO
NY
-27.81
Saturday
6/13/2009
KOHINOOR INDIAN RESTUR
716-284-2414 NY
716-284-2414
NY
-35
Saturday
6/13/2009
COMFORT INN OF BINGHAM
BINGHAMTON NY
BINGHAMTON
NY
-135.55
Saturday
6/13/2009
HKK SUPER SERVICE
FLANDERS NJ
FLANDERS
NJ
-21.13
Saturday
6/14/2009
EXXONMOBIL 97360424
WEST HENRIETTNY
WEST HENRIET
NY
-15.91
Sunday
6/14/2009
HOLIDAY INN GRAND HOTEL
GRAND ISLAND NY
GRAND ISLAND
NY
-136.98
Sunday
6/14/2009
PILOT
00001701
BINGHAMTON NY
BINGHAMTON
NY
-13.79
Sunday
6/14/2009
DNC SCOTTSVILLE TRVL
F W. HENRIETTA NY
W. HENRIETTA
NY
-12.01
Sunday
6/15/2009
ZAHRAS CAFE AND BAKE
JERSEY CITY NJ
JERSEY CITY
NJ
-7.44
Monday
6/15/2009
PATHTVM NEWARK BM BW
212-METROCARDNY
212-METROCAR
NY
-54
Monday
6/15/2009
BLIMPIES
JERSEY CITY NJ
JERSEY CITY
NJ
-6.51
Monday
6/16/2009
RELIANCE COMMUNICATION
888-673-5426 NY
888-673-5426
NY
-33.58
6/17/2009
TOYS R US #6318
ISELIN
NJ
ISELIN NJ
-27.76
6/17/2009
WEGMANS #032
WOODBRIDGE NJ
WOODBRIDGE
NJ
-32.83
Tuesday
Wednesday
Wednesday
6/18/2009
TASTE OF INDIA
JERSEY CITY NJ
JERSEY CITY NJ
-2.14
Thursday
6/18/2009
TASTE OF INDIA
JERSEY CITY NJ
JERSEY CITY NJ
-6.37
Thursday
6/20/2009
TASTE OF INDIA
JERSEY CITY NJ
JERSEY CITY NJ
-7.44
Thursday
Excerpt from a card statement highlighting a shift in regular
spending habits against data in figure 1
Figure 2
Posted Date
Payee
9/8/2009
SUBZI MANDI
9/11/2009
NJT LIBERTY ST.DLY TV7 JERSEY CITY NJ
JERSEY CITY NJ
9/11/2009
WEGMANS #032
WOODBRIDGE NJ
-33.34
9/11/2009
HESS 30215
WOODBRIDGE NJ
-25.86
NETFLIX.COM CA
-18.18
9/11/2009
Address
ISELIN
NJ
ISELIN NJ
WOODBRIDGE NJ
WOODBRIDGE NJ
NFI*WWW.NETFLIX.COM/CC NETFLIX.COM CA
Amount
-50.52
-6.8
JERSEY CITY NJ
-7.44
NJT LIBERTY ST.DLY TV7 JERSEY CITY NJ
JERSEY CITY NJ
-3
9/12/2009
WEGMANS #032
WOODBRIDGE NJ
WOODBRIDGE NJ
-18.87
9/12/2009
WEGMANS #032
WOODBRIDGE NJ
WOODBRIDGE NJ
-51.38
9/12/2009
NEW JERSEY E-ZPASS
9/14/2009
TASTE OF INDIA
9/14/2009
9/12/2009
TASTE OF INDIA
9/12/2009
JERSEY CITY NJ
888-288-6865 NJ
888-288-6865 NJ
-25
JERSEY CITY NJ
-8.69
NJT LIBERTY ST.DLY TV7 JERSEY CITY NJ
JERSEY CITY NJ
-3
9/14/2009
USPS 33382504929213949 ISELIN
ISELIN NJ
-12.95
9/14/2009
BHAVANI CASH & CARRY ISELIN
ISELIN NJ
-28.01
9/14/2009
WEGMANS #032
WOODBRIDGE NJ
-14.98
9/15/2009
TOYS R US #6318
ISELIN
NJ
ISELIN NJ
-80
9/15/2009
TOYS R US #6318
ISELIN
NJ
ISELIN NJ
-21.39
9/16/2009
NJT LIBERTY ST.DLY TV7 JERSEY CITY NJ
JERSEY CITY NJ
-16.25
9/16/2009
NJT LIBERTY ST.DLY TV7 JERSEY CITY NJ
JERSEY CITY NJ
-3
JERSEY CITY NJ
NJ
NJ
WOODBRIDGE NJ
81
b) Location around Niagara Falls, NY
c) Preceded by a booking on a travel site
(orbitz.com) which was done 2 days in
advance.
Note: The classification of a spend event as
an irregular/ regular spend event is based on
the time span across which the data is
analyzed. The same spend event may be
classified as “regular” if the time span covers
multiple years where every summer there are
such regular weekend trips.
Similarly, if the objective is to identify a shift
in regular spend events (marked in gray across
Figure 1 and Figure 2 on the previous two pages),
the bank would look at the following criteria:
a) Merchant segment – In this case, focus on
transportation for results
b) Location – Being same
c) Average spend – Look for a significant
change
d) Number of transactions – Look for a
significant change
Translating Spend Events
into Spend Patterns
A spend pattern is defined as a sequence of
spend events observed across multiple
customers, thus outlining the following:
a) Sequence of occurrence of such events
b) Time period between two events
Spend events across the transaction
history of a customer are taken to form a
spend sequence which is associated with
the age or other demographic data obtained
from the customer's records. Spend sequences
across customers go through a discriminate
analysis to identify factors that identify
customer segments with similar spend
patterns. The customer segment will need
to be updated on a regular basis to get a better
picture of the customer spending pattern.
82
Tapping into the Predictive
Powers of a Spend Pattern
A customer may be missing a couple of
spend events here and there, but generally,
all clients belonging to a spend pattern
should have the same general sequence of
events, and the time gap between the events
should be more or less the same.
To identify a spend pattern, it is
recommended that the bank define an
error limit for the time gap, so that two
sequences can be considered similar. If
the difference between related time gaps
across two sequences is within the error limit,
then they are considered to be part of the
same spend pattern.
If each spend event is denoted by a “letter”, a
spend sequence can be thought of as a “word”.
To identify if two such sequences are part of a
pattern, the bank would have to use a
sequence alignment algorithm—such as the
Needleman/ Wunsch technique. Here, the
user will have to define the weights to be
associated with the match and mismatch of
residues and also with gaps in the sequence.
This will finally lead to a score for the
alignment between the two sequences.
The user can also define a limit on the score
between two sequences, for the two to be a part of
one pattern. A set of life-cycle events is denoted
in Figure 3 and Figure 4 (on the next page).
For example, Figure 4 shows the sequence of
spend events across multiple customers over a
period of time.
There are two patterns — FIC and HG — in
the data highlighted in Figure 4. FIC, as a
pattern, indicates increase in disposable
income and hence, Customers 1 and 3 may be
more attractive to financial services and
lifestyle firms. HG, as a pattern, indicates
readiness for healthcare products.
Notions to be used in a spend pattern for individual spend events
Figure 3
Spend Event
Denoted By
International Vacation
I
Domestic Vacation
D
Drop in Payments to Financial Institutions
F
Increased Transaction on a Dependent Card
C
Medical Expenses(Hospital Payments)
H
Expenses Related to Gym
G
Example of spend patterns
Figure 4
Customer
Spend Pattern
Customer 1
HFICG
Customer 2
HG
Customer 3
FICD
Privacy
Needless to say, the above analysis can
be seen as an invasion on customer
privacy, and to avoid any breach to
customer privacy, the bank should take
care of the following:
a) Create an inability to link the
transactional and demographic data
back to customer identity information.
b) Intrinsically, a repetition of a given
sequence of events is required to form a
pattern, based on which the discriminate
analysis would provide the demographic
information—leading to categorization
of customers. Customers in a category
would be treated similarly, hence
shielding individual spending habits.
c) Care needs to be taken in disposing of the
intermediate data created during analysis,
as it contains customer specific patterns
(though if data is devoid of customer
identity information, linking the two
becomes extremely hard).
Conclusion
Transactional data stored within financial
services firms provides a wealth of
information that can be used to better
integrate the customer into the financial
services firm and the business ecosystem. The
information extracted can provide goods/
83
service providers powerful insights into
customer behavior—driving improved
targeted marketing efforts.
Care should be taken while doing such
analysis to safeguard the customer identity
84
data, never allowing it to be linked to the
analysis process. The information gathered
from this analysis process should be linked to
demographic segmentation for further
marketing actions.