AI-Powered Fraud Detection in the Insurance Industry

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Maximilian Schneider Avatar

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The implementation of AI for fraud detection in insurance has shown immense potential, particularly in the case of a German private health insurance (PKV) project. This project utilized AI to automate the detection of fraudulent patterns in physiotherapy treatment claims. With AI’s help, a successful system for identifying fraud cases was established, addressing a significant issue in the insurance sector.

Insurance fraud inflicts substantial financial damage, with German insurers reporting billions of euros in losses annually (€14 billion in 2018). A majority of PKV and nearly half of all statutory health insurance (GKV) companies report annual fraud-related losses exceeding €500,000 within their organizations.

Insurers face the challenge of early detection of suspicious fraud cases to prevent complications. The extent of investment in fraud detection amidst digitalization initiatives by insurance companies is also a critical area of focus.

Despite the potential benefits of digital fraud detection, many German insurers have yet to fully embrace these technologies. Only 37% of GKV companies use advanced data analytics for fraud detection. The COVID-19 pandemic has increased the necessity and opportunity for digital upgrades in this area due to heightened digital capabilities and financial pressures on fraudsters.

According to the German Insurance Association (GDV), potential savings from billing, some of which are fraudulent, could be over 10% of the total damage payments in Germany. However, experts estimate the actual figure to be much higher.

The challenge lies in balancing the time invested in searching for fraud against the potential savings, considering the probability of success. Often, the search costs for smaller individual claims exceed the potential savings.

New Machine Learning technologies, such as Natural Language Processing (NLP) and Computer Vision, are proving effective in detecting insurance fraud. These technologies provide a better initial sorting for human review of reported damages, reducing the number of falsely examined claims. Bavarian insurers report over 30% savings using AI compared to rule-based systems.

AI in document processing saves time by automatically classifying or extracting information from documents. This technology combines traditional Optical Character Recognition (OCR) software capabilities with AI, recognizing handwriting and extracting data from documents with unknown layouts or unusual phrasing.

In fraud detection, AI software is particularly useful for automatically structuring data, which is typically the most time-consuming step in the process. These data can be easily integrated into an Integrated Development Environment (IDE) for analysis using neural networks. Experts can label data beforehand, or companies can use APIs and Python SDKs to train and adapt their models.

Both PKV and GKV can realize substantial savings through AI and improve customer satisfaction. Traditional methods like rule-based systems have limitations, often leading to lengthy and complex processes. Additionally, medical personnel typically review bills, incurring significant costs, as rule-based systems flag more bills than necessary. These flagged bills require individual examination by experts.

AI can drastically shorten these processes by identifying non-viable cases much earlier. This reduces the workload on staff, allowing them to focus on their core activities.

For insurance companies looking to automate their fraud detection without significant effort, such as coding or external consulting, a few example documents are all that’s needed. Customers have seen impressive automation results using a small number of documents. Support teams can assist with initial steps and training the first AI model. The provided infrastructure enables companies to become document AI experts, developing their models independently. Data scientists can seamlessly integrate and customize services using APIs and Python SDKs, going beyond document extraction and classification. Using the provided SDKs, data experts can implement their scoring models for detecting fraud cases tailored to their specific use cases.

Glossary API = Application Programming Interface GDV = German Insurance Association GKV = Statutory Health Insurance IDE = Integrated Development Environment AI = Artificial Intelligence NLP = Natural Language Processing OCR = Optical Character Recognition PKV = Private Health Insurance SDK = Software Development Kit

Sources on Billing Fraud in Healthcare and the Insurance Industry [1] “Bayern sagt Betrug im Gesundheitswesen den Kampf an,” Deutsches Ärzteblatt, Mar. 27, 2018. [2] “Abrechnungsbetrug im Gesundheitswesen,” PwC Deutschland, Feb. 2021. [3] “PwC-Umfrage: Mehr Abrechnungsbetrug im Gesundheitswesen,” AssCompact, Feb. 1, 2021. [4] “Sorge der Versicherer: Corona gibt Betrügern Auftrieb,” GDVde News, Aug. 27, 2020. [5] “Künstliche Intelligenz: Use Cases in der Assekuranz (Teil 1),” msg life, Feb. 9, 2021.


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