Santam is South Africa’s largest short-term insurance company. With more than 650 000 policy holders, and assets of more than R17 bn under management, the company enjoys a market share of more than 22%. Santam operates in a market where fraudulent activity can account for an estimated 6 to 10% of all premiums.
Santam wanted to improve its’ service to customers by settling claims faster, and keeping premiums low. To achieve this, they needed to maximize operational effciency and fnd more resourceful ways to combat fraud. Santam worked with OLSPS Analytics to design a claims segmentation solution using IBM SPSS Software.
Our solution needed to integrate seamlessly with the current Santam claims process, and allow for real-time scoring of each claim with a fast response rate. Each claim is automatically scored according to its risk level, and then moved to the appropriate processing channel, for either settlement or further investigation. This allows high risk claims to be sent to the investigation unit, while low risk claims can be processed quickly, improving the overall customer service.
The solution offered by the OLSPS Analytics’ team:
• Enhanced Santam’s ability to detect fraud, saving them R17 mil in the first four months
• Improved customer service by enabling legitimate claims to be settled within an hour, more than 70 times faster than before
Millicom International Cellular, also known as Tigo, is a mobile network provider who services more than 30 million customers in 13 emerging markets in Latin America and Africa. They specialise in affordable, widely accessible, and readily available prepaid cellular telephone services.
Tigo approached OLSPS Analytics to assist them with two business challenges:
• To understand their customers profile, and how customers used their services
• To predict the customers that are most likely to leave them for the competition
The first business problem was solved with the OLSPS Customer Segmentation Solution, which identifies different behavioural profiles within the customer base. The information gained from these profiles enables Tigo to conduct highly targeted marketing campaigns. This information is also used to develop products and services to fit each profile.
The second business challenge was solved with the OLSPS Churn Solution. Most of Tigo’s customers make use of prepaid services, which allow them to move to another service provider, to churn, without any warning. This poses a problem for Tigo, as the cost to acquire new customers is considerably higher than retaining existing customers. The OLSPS Churn Solution scores each of the customers to predict the probability of the customers churning in the next few days.
A similar solution to that of Santam was implemented for Mutual & Federal using SAS software. Working closely with the Mutual & Federal data science team, we managed to produce a fully integrated, real-time solution that flagged fraudulent claims in the motor insurance sector.
After implementation of the solution, further training of the Mutual & Federal data science team meant that the solution could be maintained and managed internally. The deployed solution is still too young to evaluate its success quantitatively, however from the outset the solution made a tangible difference to the operations of the claims management system.
P-Cubed, a leading management consulting firm, specialises in delivering critical and complex programmes and portfolios for governments, agencies, and major institutions across all industry sectors. P-Cubed has a requirement for an automated scorecard process to facilitate credit-based response modelling. OLSPS Analytics developed a scorecard within IBM SPSS Modeler, which presents the credit risk scoring as functional nodes within the IBM SPSS Modeler environment.
Overall, the OLSPS Scorecard Solution provided for rapid automated development of scorecards alongside assessment utilities, and is used as a key component of P-Cubed’s business process.
Nedbank is one of South Africa’s largest retail banks. With the fourth largest customer base, it is critically important to monitor customer sentiment at all times. With this in mind, Nedbank approached OLSPS Analytics, to develop a social media monitoring tool built on IBM SPSS Software.
Nedbank uses this tool to collect data from the online interactions between them and their clients. These interactions happen on different social media feeds, including news feeds, blogs, Twitter, Facebook, and other relevant sites, resulting in ‘unstructured’ data.
The OLSPS Sentiment Analysis Solution makes use of text mining to gather insight from the interactions to transform the data into structured data. The data are used to drive customer engagements, and upsell products and services.
Major SA Retailer
We implemented the OLSPS Churn Solution for a large South African-based multibillion dollar retailer which employs close to 32 000 people. They approached OLSPS Analytics to help with extracting value from their customer datasets.
The first solution that we developed was a churn model for their cellular client base. This model predicts when a customer is likely to leave their network and helps them to design targeted marketing campaigns aimed at retaining their high value customers. The model was proven to identify the churners with a high level of accuracy.