1. InsuranceLet’s build a smarter planet
Founded in 1918, Santam has grown to become South Africa’s largest
short-term insurance company. With more than 650,000 policy
holders and assets under management of 17 billion South African Rand
(US $2.4 billion), the company enjoys a market share of more than
22 percent. It offers customers a wide range of services in personal,
commercial, agricultural and specialist insurance and risk management.
Operating in a market where fraudulent activity can account for an
estimated six to ten percent of all premiums, Santam’s claims division
faced a considerable challenge.
“On the one hand, we need to assess claims carefully to avoid exposure
to fraudsters, which affects our bottom line and increases the cost of
premiums for our good customers,” explains Anesh Govender, Head
of Finance, Reporting and Salvage at Santam. “On the other hand,
to maintain our excellent reputation for customer service, we need to
be able to settle legitimate claims quickly. We wanted to find a way to
treat each case on its merits and deliver a better service in terms of both
speed and safety.”
A new operating model
The claims division began to develop a new operating model, which
would pass claims down different channels depending on their assessed
level of risk. The five channels, ranked from lowest to highest risk, are:
• Immediate: these claims are settled as quickly as possible, without
further assessment.
• Digital: these claims require photographic evidence, which is analysed
and verified by a team at Santam’s head office.
• Mobile: these claims are assessed by a Santam operative who visits the
site of the claim.
• Complex: these claims require a more sophisticated assessment by an
expert team, usually due to legal issues.
• Merit: these are the claims that are considered most likely to be
fraudulent, and are passed to a special unit for thorough investigation.
Santam Insurance boosts
customer service and
beats fraud
Using IBM SPSS predictive analytics to identify risks
and accelerate claims settlement
Smart is...
Using advanced analytics to
automatically assess the risk of
fraud and accelerate settlement of
legitimate claims
Santam wanted to find a way to improve
its service to customers by settling claims
faster and keeping premiums low. To
achieve this, the company needed to
maximise operational efficiency and find
smarter ways to combat fraud.
Santam worked with Olrac SPSolutions,
an IBM®
Business Partner, to design a
claims segmentation solution based
on IBM SPSS®
predictive analytics
software. Each claim is automatically
scored according to its risk level, and then
distributed to the appropriate processing
channel for settlement or further
investigation.

2. InsuranceLet’s build a smarter planet
Leveraging predictive analytics
To ensure that claims would be handled by the appropriate channels,
Santam needed to find a method of segmenting claims as quickly
and effectively as possible. The company became interested in the
possibility of using predictive analytics software to manage this
segmentation. Following a detailed evaluation of the analytics software
available on the market, the Santam team drew up a shortlist of two
vendors: SAS, and Olrac SPSolutions (formerly known as SPSS South
Africa), who proposed a solution based on IBM SPSS software.
“We ultimately chose Olrac SPSolutions for two main reasons,”
explains Anesh Govender. “First, we believed that IBM SPSS offered
a better range of functionality, particularly in terms of its ability to
integrate with our core claims management application, which runs
on a mainframe platform. Second, we were hugely impressed with the
team from Olrac SPSolutions. Even though we were proposing to build
a very innovative and leading-edge solution, their technical skill and
business experience gave us a lot of confidence that IBM SPSS could
deliver what we needed.”
Proving the concept
To make a solid business case for the adoption of the IBM SPSS
solution, Santam’s in-house team worked with Olrac SPSolutions
to deliver a proof of concept that focused on one key business area:
personal motor insurance for accidental damage, collision and
overturning.
“We were able to demonstrate the power of using predictive modelling
and business rules to score claims based on a number of known risk
factors,” explains Anesh Govender. “For example, based on existing
claims data, we already knew that statistically most car accidents occur
around 10am, but that most fraudulent ‘accidents’ happen between
10pm and 5am. So the time that the accident is reported is a key factor
in determining the risk score of the claim. By building business rules
based on numerous risk factors like this one, we were able to develop a
reliable model that enabled us to segment claims effectively.”
Business Benefits
• Enhances Santam’s ability to detect
fraud, foiling a major crime syndicate
and saving 17 million South African
Rand (US $2.4 million) in the first four
months of operation.
• Improves customer service by enabling
legitimate claims to be settled within
an hour, more than 70 times faster than
before.
• Reduces the need for claims adjusters
to visit clients to assess low-risk claims,
significantly reducing operational
costs.
Smarter Insurance Fighting fraud and boosting customer service
Instrumented When a claim is submitted, Santam captures data related to a
number of key risk indicators and automatically transfers it to the
new SPSS solution.
Interconnected The analytical engine uses a combination of business rules and
sophisticated predictive models to assess claims for potential
fraud and transfer them to the appropriate processing channel.
Intelligent By segmenting claims according to risk factors, Santam can focus
on investigating high-risk claims and catching fraudsters, while
rewarding good customers with fast settlement and better service.

3. InsuranceLet’s build a smarter planet
Easy expansion
Following the proof of concept stage, Santam worked with Olrac
SPSolutions to move the solution into the production environment,
where it would handle claims segmentation in real time. After a
successful go-live, the company began extending the solution to handle
the whole of its motor insurance business, as well as other business
lines.
“The initial project took six months, because it required the
development of some quite complex interfaces with our mainframe,”
comments Anesh Govender. “We were then able to re-use these
interfaces in the next project, so it only took two months. Olrac
SPSolutions helped us lay the groundwork very efficiently, so from now
on, each new project should be quicker and easier than the last.”
Increasing flexibility
Santam is currently in the process of upgrading the solution to IBM
SPSS Decision Management 6, which will offer further flexibility,
especially in terms of creating and managing the business rules that
govern the claims processing channels.
“At the moment, some of the workflows are still driven by mainframe
systems, which makes it relatively difficult to adapt them to changing
business needs,” says Anesh Govender. “With SPSS Decision
Management 6, we will be able to manage almost the entire process
within SPSS itself, which will give us much greater agility and
flexibility.”
Faster service, huge savings
The current version of the SPSS solution has already delivered
spectacular real-world results for Santam in terms of fraud detection,
customer service and return on investment.
“Before this solution, the minimum time it took to settle a claim was
three days,” explains Anesh Govender. “Now, the low-risk claims that
pass down the ‘immediate’ channel can be settled within an hour – so
customers with legitimate claims get much faster service. This has
also allowed us to significantly reduce the number of claims that need
to be assessed by mobile operatives, which will lead to considerable
operational cost savings.
“The most startling results, though, have come as a result of enhanced
fraud detection. In the first month of using the SPSS solution, we were
able to identify patterns that enabled us to foil a major motor insurance
fraud syndicate. Within the first four months, we had saved R17 million
on fraudulent claims, and R32 million in total repudiations – so the
solution delivered a full return on investment almost instantly!”
“In the first month of
using the SPSS solution,
we were able to identify
patterns that enabled
us to foil a major
motor insurance fraud
syndicate. Within the
first four months, we
had saved R17 million
on fraudulent claims,
and R32 million in total
repudiations – so the
solution delivered a full
return on investment
almost instantly!”
— Anesh Govender, Head of Finance, Reporting
and Salvage, Santam Insurance
Solution Components
Software
• IBM®
SPSS®
Decision Management 6
IBM Business Partner
• Olrac SPSolutions

4. Looking to the future
Over the next two to three years, Santam plans to enhance the claims
segmentation process even further by using the sophisticated predictive
modelling capabilities of SPSS to fine-tune the scoring process.
“At the moment, the scoring process is based primarily on sets of
predefined business rules – but as we process more claims and get more
data in the system, we will be able to build more complex predictive
models,” says Anesh Govender. “We’re particularly interested in
building propensity models to look at customer behaviour. This would
enable us to group customers into different risk profiles, so that we can
segment our best customers and offer them more tailored products
and higher service levels. We may also be able to build models using
seasonal data and weather data to help us react more effectively to
catastrophes such as fires and floods.”
He concludes: “IBM and Olrac SPSolutions have helped us build
a solution that has not only transformed our claims processing
methodology in terms of speed and efficiency, but also provides game-
changing insight – helping us identify suspicious claims more quickly
and protect our business and our customers by bringing fraudsters to
justice.”
For more information
To learn more about IBM SPSS software, contact your IBM sales
representative or visit: ibm.com/spss
YTC03286-ZAEN-00
© Copyright IBM Corporation 2011
IBM Southern & Central Africa
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Produced in South Africa
July 2011
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