SOBE: A Fraud Detection Platform in Insurance Industry

Year 2022, Volume: 5 Issue: ICOLES2021 Special Issue, 25 - 31, 30.11.2022

H. Onur Özcan İsmail Çolak Selin Erımhan Vedat Güneş Fatih Abut , Fatih Akay

https://doi.org/10.34088/kojose.1019125

Abstract

Fraud detection identifies suspicious activities, false pretenses, wrongful or criminal deception intended to result in financial gain. Fraud is rare, well thought, effortful, and deceiving throughout claims. Detecting fraudulent claims is essential for the insurance industry. Therefore, most insurance companies must devote time and budget to fraud detection. Fraud detection can be divided into two categories; the main and most common type of fraud is individual fraud. Individual frauds can appear in many kinds of forms. For example, damage to an asset might be occurred before issuing a policy and be reported after. The second category is organized fraud which is much rarer and harder to detect than individual fraud. Especially motor insurance fraud is commonly attempted by organized crime rings. Counterparties involved in fraudulent claims change frequently, and changes make fraud detection difficult. According to Insurance Information and Monitoring Center findings, the fraudulent claim payment ratio is 10 to 30 %, and the detection success rate for an individual is at 1.4 to 5%. At the same time, the annual fraud cost is at 200 to 300 $ million. This study proposes a fraud detection platform called SOBE, which assists fraud departments’ claim inquiry more easily and shorter than manual investigation made by employees. At its core, SOBE uses a rule engine approach. In order to support the rule engine, there is also a machine learning algorithm for fraud detection. In addition, the SNA module detects interconnected fraud counterparts among claim files. Consequently, the SOBE fraud detection platform allows Anadolu Sigorta to prevent improper payments from claiming participants. SOBE platform, the central fraud detection platform at Anadolu Sigorta, was developed in-house using different technologies and methods, including KNIME Analytics Platform, Python, graph methods, and web service methodologies.

Keywords

Fraud Detection, Machine Learning, Social Network Analysis, KNIME

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