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Re-engineering anti-fraud processes using AI & big data

Re-engineering anti-fraud processes using AI & big data

Modular framework to investigate suspicious items and detect fraud

Combining AI and advanced analytics to introduce dynamic fraud detection in the European insurance market. GFT re-engineered anti-fraud processes for an Italian insurance company and increased the rate of fraud detection by 30% and the performance in daily real-time reporting by 40%.

Close-up of hands typing on a laptop keyboard in a dimly lit environment, symbolising the use of AI and big data to combat fraud. — Learn how GFT is transforming anti-fraud processes through the power of artificial intelligence and big data. This image captures the essence of advanced technology being employed to detect and prevent fraudulent activities in real-time, enhancing security and trust.

Operational efficiency

Significant increase of fraud detection since launch

Real-time technology

Instant analysis and a swift response

Improved scoring effectiveness

Heuristic rules and predictive models minimize false positives and discover false negatives

Partner

Rulex

Founded in the U.S., Rulex is a leader in no-code software for data preparation, machine learning, and optimization. Adopting a unique, logic-based approach to predictive analytics enables business and process experts to create and deploy AI applications without programming.
Rulex offers a new way of making business decisions and has been adopted in many sectors, including automotive, financial, supply chain, education, energy and healthcare.

Challenge

Arresting fraud in the European insurance market

Fraud is a widespread problem within the European insurance market. Fraudulent motor claims are particularly serious – Italy has a nationwide average of 19.3% per year, which increases the cost of business for the entire industry. Faced with a growing fraud challenge, this leading Italian general insurer wanted a smart technical solution that would increase prevention and detection. Specific issues included:

Inconsistent data across markets hindered fraud detection
Static fraud detection models were slow and becoming ineffective
As fraud became more sophisticated, there was a need for a smarter solution that could learn heuristically from behavior patterns

Engagement

Implement a solution combining real-time data, rules and predictive modeling, as well as real-time scoring

GFT provided the insurance company with an accelerator to rapidly implement an enterprise-wide anti-fraud solution that harnessed the power of artificial intelligence and big data. The focus of the solution was online fraud prevention, so it was necessary to combine and analyze structured and unstructured from internal and external sources. The anti-fraud solution implemented included:

Real-time technology to enable instant analysis and a swift response
Improved scoring effectiveness by using heuristic rules and predictive models to minimize false positives and discovery of false negatives to improve effectiveness
Extended data and search capabilities across structured and unstructured sources
User-friendly investigation instruments that allow the antifraud team to access and query structured and unstructured documents and perform social network analysis
Clear reporting that presents actionable information that simplifies the underlying complex data and analysis

Because of the broad scope of the project, GFT developed a modular framework comprising several specialized components, each of which performs a unique role in detecting fraud and investigating suspicious items.

Benefit

In only a few months, this general insurance company improved the accuracy of suspicious claims

GFT delivered the assignment on time and on budget. Although highly technical, the solution can be used without the involvement of the client’s IT department or GFT specialists. In only a few months, the solution delivered quantifiable benefits:

30% increase in fraud detection and 7% decrease in false positives
40% increase in daily real-time reporting
With real-time detection, the anti-fraud team can act immediately, extract relevant information and examine suspicious positions without the involvement of the IT department
The antifraud team can insert and amend heuristic rules and predictive models manually and evaluate the impact of modifications on historical datasets, eliminating the need for expensive simulations or extractions

The solution can automatically detect claims with a high probability of fraud. This has empowered d the company to focus on fraud prevention, which is more cost-effective than ex-post fraud checking. The company has ambitious plans to increase the scope of the solution by extending information sources, with an emphasis on digital documents and data from disparate sources.

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Learn how GFT is transforming anti-fraud processes through the power of artificial intelligence and big data. This image captures the essence of advanced technology being employed to detect and prevent fraudulent activities in real-time, enhancing security and trust.

Success story

Re-engineering anti-fraud processes using AI & big data

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