Artificial intelligence and fraud detection
Fraud is defined as any dishonest act intended to obtain a material or moral advantage. This practice concerns all economic sectors and activities, with different forms and types. These include accounting fraud, cybercrime, invoice fraud, tax fraud, bank fraud and insurance fraud.
Indeed, the insurance industry is not immune to the scourge of fraud, which is by definition complex to control. Fraud represents an obstacle to the growth of insurance companies, as it increases the cost of claims, leading to a deterioration in the combined ratio and, consequently, to significant losses in terms of profitability in the various lines of insurance, particularly property. Moreover, there were around 13 billion euros of fraudulent claims in Europe in 2017, of which 2.5 billion euros of fraudulent claims were detected, but 80% of fraud goes undetected, according to Insurance Europe. For the year 2020, we can roughly estimate the amount of fraudulent 14.5 billion euros in fraudulent claims in Europe. According to ALFA (l’Agence de Lutte contre la Fraude à l’assurance), fraud in the property-casualty sector in France accounted for almost half a billion euros in 2018.
With a view to limiting the negative financial impact of fraud, detecting fraud attempts on an ongoing basis is becoming a strategic issue for insurance companies. However, the traditional techniques used are showing their inadequacies, such as the analysis of files by claims managers, accompanied by operational risks and time constraints. Hence the need to develop more sophisticated and effective fraud detection systems, using new technologies such as Artificial Intelligence (AI).
Indeed, insurers can protect themselves against financial loss by exploiting the inescapable potential of Machine Learning (ML) to detect anomalies automatically with a high degree of accuracy.
The diversity of fraud categories makes it difficult to control fraudulent acts from both a legal and technical point of view.
