white paper
Using SPSS in
financial services
white paper
Using SPSS in financial services
2
trategic marketing and business analysis in financial services needs to be accurately
researched and evaluated for financial institutions to be successful. Today, more
than ever before, organizations are able to equip themselves with advanced
analytical tools they can use to develop smarter practices for a competitive edge.
S
By employing sophisticated analytical techniques, many financial service organizations
are realizing the significant benefits of statistics – increased revenues, minimizing
unprofitable activity, reducing risk, cutting costs and increasing customer satisfaction.
SPSS provides an entire data analysis solution – from access to analysis to action.
This powerful analytical package also acts as a complementary element when combined
with other business intelligence tools. Alongside business query tools, OLAP and database
capability, SPSS takes your analysis far beyond the polished hierarchical data reporting
into the realms of segmentation, prediction and modeling. To truly leverage the value of
your data, use advanced analytical tools such as SPSS.
How financial institutions are using SPSS
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A leading financial products/services organization uses SPSS to enable sophisticated
client prospecting that is both targeted and cost-effective
At a major retail bank, SPSS is central to its performance monitoring function
SPSS provides the analytical power to truly evaluate business performance.
A financial institution saved over £500,000 per month by developing a customized
credit scoring application based on SPSS software intelligence that forecasts likely
loan defaults
A personal credit organization increased response rates to its direct mail campaigns
from 2.5 percent to 25 percent through effective segmentation and targeting using SPSS
A personal financial product vendor uses SPSS to model and predict the uplift of sales
due to advertising activity and evaluate the power of high profile advertising
A major mortgage lender uses SPSS forecasting capability to predict levels of house
repossessions which enables it to prepare itself for the changing economic climate
Answer both the “What?” and “Why?” questions with SPSS
A leading financial service provider uses SPSS to find out more about it’s customers
and prospects. Queries of the last quarter’s prospect generation activity using a standard
query and reporting tool provided a simple break down of new prospect numbers.
However, the organization also wanted to discover who their prospects were, those
most likely to be recruited as future clients and why.
Before implementing SPSS, the financial provider used only standard business query
and reporting tools which provided only indications of volume arranged by hierarchy.
They didn’t have the capability to drill down and make inferences about the profile of
those who expressed an interest. With SPSS, they have the tools to address the “Why?”
question and present a reliable answer. They can strategically target all their customer
recruitment activity to specific prospect groups. Now they know which groups are most
likely to respond to particular types of financial products or services.
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Using SPSS in financial services
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To deliver the next phase, a “specialist” tool was implemented. The secret weapon
was CHAID, a technique for rapid data segmentation based on response behavior.
The data collected from new prospects provides an invaluable explanatory information
resource. Using basic analysis, the organization is able to discover some key factors likely
to indicate interest in specific financial products. They were able to uncover some
significant factors. In particular females below 35 years of age prove to be a group with
strong potential, conversely the older male clients have proved to be much less
interested (see Figure 1 below).
(figure 1) Error bar charts show the distribution, median and mean of your data.
Here you see females below 35 years of age prove to be a group with strong potential,
conversely the older male clients have proved to be much less interested.
The error bar chart provides additional insight and is a useful precursor to the final
modeling technique. Without intelligent tools like SPSS this would not have been possible.
With SPSS, the financial provider is able to develop highly detailed profiles for specific
product lines. This intelligence enables them to enhance prospect recruitment and
increase financial savings.
Monitor ongoing business performance effectively
A major High Street bank uses SPSS for all of its business performance monitoring, in
particular employee satisfaction and performance. SPSS acts as their reporting and
statistical tool and the tabular and graphical output from the software is coupled with
support from SPSS Consulting.
This combination of software and services gives the bank a customized solution and
seamless integration of an entire suite of business tools. This integrated solution saves
time for the end users, reduces the learning curve and produces results more rapidly.
Statistical analysis enables them to turn the volumes of collected data into vital
business intelligence and decision support information.
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Using SPSS in financial services
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Reduce exposure for loan applications
Financial transactions related to service and product provisions beg a range of complex
questions. What is the probability that the loan will be fully repaid? How profitable will
the transaction be? How likely is it the product will run full term? What is the profile
of those likely to default on a loan? Standard query and reporting tools cannot easily
answer these sorts of questions. However, by using SPSS for data analysis, financial
institutions can make effective use of the broad range of data that is collected on
every loan applicant.
Typically, institutions warehouse data such as age, gender, income, marital status,
personal financial data, housing tenure, length of residency, geographical data
(explored later in this White Paper), etc. Couple this exhaustive information with
the intelligence delivered by advanced analytical techniques and the result is a potent
suite of tools to tackle the problems we have outlined.
One example comes from a major lending bank. It uses SPSS to build a credit risk
analysis system. Using this system, the bank is able to score all loan applicants. The
models used are constructed on historical data holding examples of loan defaulters
and non-defaulters.
The bank realizes two major advantages by using statistical techniques in their analyses.
First, it can produce accurate scores for risk assessment. Second, it can provide clear,
easy-to-read reports that enable customers to understand the basis of their decisions –
a legal requirement of credit risk assessment. Similar implementations of SPSS have
been used to assess the likelihood of premature closing of mortgage accounts for a major
UK building society.
Credit risk analysis models built using SPSS save time and money, as well as provide
valuable insight into the potential flaws and weak points in vital business processes.
Success in these key areas has a positive ripple effect throughout a financial organization.
Send the right message and improve direct marketing efforts
Consumer credit is big business in the United Kingdom, worth billions of pounds
annually. With thousands of varying products and millions of potential customers, the
consumer credit market can be confounding. Many consumer credit organizations are
turning to SPSS to provide them with analysis to make sense of the market – and to
leverage their data.
In the consumer credit sector, there is a growing realization of the power of direct
marketing techniques, in particular, direct mail. But with such a broad product range,
how can organizations tailor their message to those who are likely to respond and
ultimately purchase? Once again the key is to use existing data in conjunction with
advanced analytical routines.
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Using SPSS in financial services
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Flexible data exploration and investigation using SPSS produces descriptive information
that is easy to understand. Once the data is effectively studied, basic inferential
techniques build a more formal understanding. More advanced multivariate analytical
routines can build tentative models and then ensure the models constructed are powerful
and robust. Finally, point performance can be monitored and efficiency evaluated.
Using powerful statistical techniques, consumer credit companies can develop targeted
direct marketing promotions and get a handle on a once bewildering market segment.
Figure 2 below shows the relationship between Employment Classification and
possession of a specific financial product.
Employment status * have savings product crosstabulation
Have savings product
Yes
No
Unknown/missing
Count
Expected count
5
12.7
215
207.3
220
220.0
Full-time employed
Count
Expected count
620
517.4
8334
8436.6
8954
8954.0
Part-time employed
Count
Expected count
45
87.5
1469
1426.5
1514
1514.0
Unemployed
Count
Expected count
46
63.0
1044
1027.0
1090
1090.0
Full-time education
Count
Expected count
12
47.4
808
772.6
820
820.0
Count
Expected count
728
728.0
11870
11870.0
12598
12598.0
Employment status
Total
Total
(Figure 2) This table reveals that employment classification and product type have a significant relationship.
This is valuable targeting information For future direct mail activity.
Evaluating advertising uplift
A financial service client uses SPSS to evaluate the impact of an expensive advertising
campaign within a defined geographical region. It assesses sales of a particular financial
product as well as the entire range of branded products. These complex issues cannot be
tackled effectively using basic reporting tools. More advanced predictive modeling must
be employed.
In this instance, the solution is linear regression. SPSS offers state-of-the-art regression
techniques which enable the organization to evaluate the exact impact of the advertising
campaign and use this as decision support information to justify further activity.
Other SPSS clients use SPSS to predict the state of the pensions market based on broad
macro economic factors. This enables them to proactively manage their business well into
the future.
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Using SPSS in financial services
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This classic application addresses one of the biggest problems in advertising, famously
summed up by Victor Remmington, “I know half my advertising budget is wasted, the
trouble is I don’t know which half.” Progressive financial organizations no longer trust
to luck when it comes to advertising spends. Through implementation of advanced
analytical tools, like SPSS, uncertainty can be minimized and fact-based decisions can
be made for continued marketing success.
Using statistics to understand the behavior of the
mortgage market
Having the foresight and flexibility to adapt to market change is often the difference
between success and failure. For major mortgage lenders, its imperative to understand
changing financial conditions. One such organization relies on SPSS to solve this problem.
Using autoregression financial institutions can easily
model housing repossessions and mortgage defaults.
This information helps lenders accurately forcast
future strategies.
The organization models the behavior of
housing repossessions by collecting data on
levels of repossessions, six monthly arrears
and twelve monthly arrears. It examines data,
identifies vital relationships and builds
models to predict how repossessions will
perform up to one year in advance. The
modeling technique the mortgage lender
uses is autoregression. Autoregression
produces reliable models that enable
accurate forecasts. Armed with this information, the mortgage lender can formulate
effective business strategies (see Figure 3).
Geographic Information Systems
Geographic Information Systems (GIS) play an instrumental role in MIS, marketing
strategies, data analysis and market penetration analysis. The purpose of this section is to
define GIS, what it can and can’t do and how it might apply to financial services industries.
GIS is a tool that enables you to visualize spatial relationships between your data instantly
and analyze data using these spatial relationships. GIS allows you to exploit the spatial/
geographical elements of your data. Typically, this may include a customer postal code,
local office location, competitor’s location, etc. GIS can then build a number of layers
of information, together with background demographics, roads, railways, towns and any
other strategic data the analyst requires. When used in conjunction with GIS, SPSS
provides an enormously powerful combination of tools which can generate levels of
understanding and analysis previously unheard of.
GIS provides answers to questions such as:
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How many of the customers from each office live within x minutes walk of the office
or x minutes drive time?
What are the defining characteristics of the geographical areas where my best
customers reside?
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Using SPSS in financial services
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Can this information be used to create better marketing strategies?
Is there a relationship between the demographics concerning purchase area and
the actual data we have on our customers? If there is, how can we best exploit
this relationship?
Can we define our sales areas to create areas of equal potential or equal sales?
Which of these two would be the best option and why?
What is the best way to redefine our sales territories/areas to reduce their overall
number while maintaining equal potential sales or actual sales in each area?
How can the answers to these sorts of questions best be visualized and distributed
to others within my organization in an easily read and understood format?
If these are the types of questions you need to answer, then GIS will provide you with
a quick and relatively simple way to get answers. If your data has any sort of spatial
element, for example, postal codes, county, district, sales area, branch area etc., then
you can utilize GIS for better solutions.
Using GIS within financial services
The financial service industry is in a very powerful position to make use of SPSS
and GIS. Because SPSS and GIS work so well with huge amounts of data, they are
particularly effective in analyzing the vast customer data tracking profiles accumulated
by financial organizations.
Most financial service customers are evaluated on a wide range of criteria before a
transaction can be completed. The organization collects information to perform credit
scoring procedures, suitability for particular products and services to comply with the
Financial Services Act, etc. Much of this data is already being used within the financial
services sector as well as outside it. When used in conjunction with GIS, SPSS provides
an enormously powerful combination of tools which can generate levels of understanding
and analysis previously unheard of. Together GIS and SPSS provide an unprecedented set
of tools to give you a higher level of understanding.
SPSS works with the MapInfo suite of GIS products. Typical uses include customer
segmentation and profiling within SPSS, the results of which can then be displayed
and analyzed spatially within our GIS solution, MapInfo.
Summary
In today’s financial services environment, it is a constant challenge to stay ahead of the
competition. To increase market share and operate more efficiently, financial institutions
should use powerful, flexible, advanced analytical methods like those offered by SPSS.
Statistics can give you and your organization the edge in the following areas:
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React faster to market changes
Empower your workers with the knowledge that SPSS can provide
Exploit the power of the data you routinely collect as a financial organization
Have increased confidence in your business critical decisions
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About SPSS
SPSS Inc. is a leader in the large markets for analytic software: business intelligence (data
mining and market research), quality improvement and scientific research. SPSS products
and services transform organizations by helping users leverage information to grow revenues
and improve processes. More than two million people use SPSS, the world’s best-selling tool
for desktop analysis, to create and distribute information for better decision making.
Celebrating its 30th anniversary in 1998, SPSS is based in Chicago with more than 40 offices,
distributors and partners worldwide. Products run on leading computer platforms and
many are translated into 10 local languages. In 1997, the company employed nearly 800
people worldwide and generated net revenues of approximately $110 million.
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