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FRISS Fraud Detection Tool

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Client Overview

Tower Insurance is a reputable insurance provider offering an extensive range of insurance products in New Zealand and the Pacific. Along with popular options like home and contents insurance, car insurance, travel insurance, and business insurance, Tower Insurance caters to specific needs with specialized coverage options such as boat insurance, landlord insurance, and cyber insurance.

Despite its strong reputation, Tower Insurance has encountered a growing challenge in the form of fraudulent claims. These fraudulent activities have taken a toll on the company’s financial performance, adversely affecting its bottom line. Fraudulent claims not only lead to financial losses but also erode trust and credibility within the insurance industry. As a result, Tower Insurance has likely been implementing measures to combat fraud, including enhanced claim assessment procedures, advanced analytics, and fraud detection technologies.

To protect its customers and maintain its financial stability, Tower Insurance likely invests in robust fraud prevention strategies and collaborates with law enforcement agencies to identify and prosecute fraudulent claimants. By tackling this issue head-on, Tower Insurance aims to preserve its reputation as a trusted insurance provider and ensure the long-term sustainability of its business operations.


Tower Insurance company’s management has identified fraudulent claims as a significant problem, leading to financial losses, damaged reputation, and potential legal liabilities. The company needs to find a way to detect and prevent fraudulent claims to save the business money.

In general, Insurance companies have always been a lucrative target for fraudulent activities. Fraudulent claims can cost insurance companies a significant amount of money, leading to financial losses, damaged reputation, and potential legal liabilities.

Tower Insurance contracted out with N2N Solutions company to provide a consultant who worked as a Lead Business Analyst for implementing fraud detection tool. N2N consultant fully understood the circumstances for implementing a fraud detection tool is critical to ensure that Tower Insurance can detect and prevent fraudulent claims. In this case study, we will discuss the systematic approach and expertise provided by N2N Solutions’ consultant/ Lead BA for implementation of the FRISS fraud detection tool in Tower Insurance to prevent unjustified claims and save the business money.


After conducting extensive research, Tower Insurance company decided to implement the FRISS fraud detection tool. FRISS is a software solution based in Netherland company that uses machine learning algorithms to analyze data and detect fraudulent activities in insurance claims. The FRISS tool provides real-time detection of fraudulent activities, enabling the company to take proactive measures to prevent fraud. N2N Solutions consultant helped Tower Insurance to implement fraud detection tool by working closely with its team and Vendor’s team to articulate requirements and expectations. 


N2N Solutions helped to implement FRISS fraud detection tool through key steps, including:

  • Requirement gathering: Understood the current systems and their lifecycle system processes. N2N captured current state process maps of claim systems from lodgement to fulfilment stage. N2N also elicited requirements from relevant stakeholders of Tower Insurance and designed future state process maps. Tower Insurance uses ESIS as a Policy Administration system (PAS) to write Quote, Policy and Claim as well. N2N got an agreement on requirements and design aspects from relevant stakeholders of Tower Insurance to proceed with implementation.
  • Data Migration/Mapping/Integration: N2N also worked closely with data platform team to migrate customer and policy data of past 5 years to be picked up by FRISS fraud detection tool. An extensive exercise of data mapping between Tower Insurance data schema and Data model was done. This involved importing historical data into the FRISS tool, allowing it to learn from past cases and improve its detection accuracy.
  • Customization: The FRISS APIs were customized to meet the specific needs of the Tower Insurance existing systems. This activity was done at detailed APIs level that were shared by FRISS vendor with Tower Insurance, which is the primary area of concern for the company.
  • Implementation: N2N helped FRISS tool was implemented and integrated into the company’s claims processing system ESIS. The tool was configured to flag any claims that met specific criteria for potential fraud, allowing the company’s fraud investigation team to investigate further. Flag were categorised in three traffic light systems e.g., RED, ORANGE, YELLOW, GREEN with score indicator.
  • Change management /Training: N2N worked closely with Change management team to record desired changes and cascade changes to train users of lodgement and investigation team. The company’s staff underwent training on how to use the FRISS tool. The training covered the tool’s functionality, how to interpret results, and how to take action in case of suspected fraudulent activity.


The implementation of FRISS has yielded positive results for Tower Insurance. The tool has been successful in detecting and preventing fraudulent claims, even saved the business money approx. 1M NZD within 2 months post implementation. The tool has also helped to improve the Tower’s reputation, as customers are now confident that their claims are being thoroughly investigated for potential fraud.

N2N Implemented a fraud detection tool like FRISS can help insurance companies to detect and prevent fraudulent claims, leading to significant cost savings and improved reputation. By integrating the FRISS tool, the insurance company was able to detect and prevent fraudulent claims, improving its bottom line and protecting itself from potential legal liabilities. As such, insurance companies should consider implementing a fraud detection tool like FRISS to safeguard their business and ensure their long-term success.

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