Presidion | Case Study – Santam Insurance

Case Study – Santam Insurance

ROI: 244%

Payback: 3.7 months

Avg annual benefit: R34,407,024 (US$3,815,085)

Source: Nucleus Research Report N36 – IBM SPSS Analytics Decision Management ROI Case Study: Santam Insurance.

Solution Components

  • IBM SPSS Decision Management
  • IBM SPSS Modeler
  • IBM SPSS Collaboration and Deployment Services

“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

Santam Insurance

Using IBM SPSS predictive analytics to identify risks and accelerate claims settlement

 

Overview

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.

 

Business need

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.

 

Solution

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.

 

Benefits

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.


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