Predicting the possibility of fraudulent incidence occurrence through smart solutions.

About the Industry

Improved technology has impacted the insurance industry drastically. Digitization has incredible potential for insurance companies, and that is why many small and mid-sized firms are capitalizing in digital space. With the smart transformations taking place, the focus has mainly shifted from service to customers. Improvements in the risk mitigation model have resulted in cost efficiency, along with better brand architecture.

As per the recent trends, the analyzed data are capable of improving the performance of the operations that run back-end. The transformation from a legacy system to a usage-based insurance model is the best example of system modernization. Security is a priority today, and that is why the industry has extensively deployed cybersecurity measures. The market forecast suggests a better tomorrow for the industry.

About the Client

Our client is a renowned Africa-based insurance firm. The company offers property holding and financial services to major enterprises. The organization also works with asset management and the maintenance of property portfolios.

The client is a leading financial services group with a strong presence in 24 countries across the African continent. Along with major banking services, the investment portfolio of the organization is also robust. As a part of business revamping, they are looking for expansion across major geographical areas.

The Business Challenges

Our client was facing challenges that impacted their daily engagement. Some of the weak areas that we identified are as follows: –

  • The cases of false death claim were taking a toll. These incidents were going unnoticed, as the nominees were unaware of such fraudulent practices.
  • The frequency of insurance claims, even before the death of the claimant, was also rising at an alarming rate.
  • appeared believable and remained unnoticed.
  • the practice of presenting fake documents were rising exponentially.

Our Solution

  • We collected all historical data and did a regression analysis. This analysis classified the data into certain entities and helped identify future trends.
  • We analyzed the situation through a severity matrix. With the use of color coding, we highlighted the case and made the judgment stronger by predicting the occurrence of fraudulent practices in the future.
  • The machine learning models trained the collected data, and with the integrated approach of deep learning, we mitigated fraud incidence with greater accuracy.

Key Results

  • Initially, we were successful in resolving 30% to 40% of the fraud cases. Our analysis of the historical data helped trace the origin point of the issue.
  • With Machine Learning models, we improved the accuracy of our solution. We succeeded in mitigating 60% to 70% of all illegal cases in another phase.

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