AI-driven claims fraud detection will make major advances in 2024

A focus on so-called deepfakes and shallow fakes will be part of the overall push to tackle the claims fraud industry.

The integration of artificial intelligence (AI) in insurance fraud detection marks a significant progression in the industry’s ability to tackle a perennial challenge that costs it billions every year.

AI's primary role in this field is the rapid and precise analysis of large volumes of data. AI algorithms and machine learning can efficiently sift through extensive datasets and identify patterns and anomalies that are indicative of fraud. By learning from historical data, it continuously enhances its ability to identify intricate fraud schemes.

A crucial advancement in AI for fraud detection is its ability to extract data points from various documents. Technologies like optical character recognition (OCR) and natural language processing (NLP) empower AI to analyse documents such as claim forms and medical records, extracting essential information, comparing inconsistencies, and highlighting potential red flags. This capability is vital in detecting fraudulent activities, such as inconsistencies in dates or narrative discrepancies.

In the coming years, it is likely that AI will evolve an increasingly sophisticated ability to detect and counter deepfakes and shallowfakes. These advanced techniques involve creating realistic but fake audio, video, or image content, which can be used to fabricate evidence in fraudulent claims. AI-driven solutions are being developed to identify these fakes by analysing inconsistencies in digital fingerprints, patterns, and other anomalies that are not perceptible to the human eye.

While AI is revolutionising the insurance industry's approach to fraud detection with machine learning, OCR, and NLP, its ultimate effectiveness will depend on continuous evolution and ethical oversight.