THE ROLE OF ARTIFICIAL INTELLIGENCE SUPPORTED IMAGE ANALYSIS IN INSURANCE FRAUD AND RAPID DECISION MAKING PROCESSES: REALITYCHECKER

Özet

Insurance fraud represents a pervasive issue that results in substantial financial losses, particularly for insurance providers. The advent of digital technology has facilitated the utilisation of an array of techniques by fraudsters, enabling them to achieve their objectives with greater ease and security, whilst simultaneously facilitating the detection of such activities. The digital forensic analysis of documents and images necessitates a high degree of expertise, is a laborious process, and is susceptible to human error. Such occurrences can result in financial, temporal, and labour losses, in addition to delays in decision-making processes. This article examines the potential of an artificial intelligencepowered document and image analysis system, designated “RealityChecker,” for the detection of insurance fraud and the facilitation of rapid decision-making processes for insurers. The software developed by the author of this article for the authentication of diverse content offers considerable potential for applications not only in the insurance sector but also in the media and journalism. The RealityChecker software, which has also been released in a GPT version with limited features for rapid access, employs a range of techniques, including forensic image analysis and optical flow, to detect forgery and fraud in the insurance sector. It also facilitates the integration of multiple analysis methods and data types, enabling insurers to make accurate and rapid decisions. This article examines the RealityChecker software, discusses its effectiveness and usefulness in detecting fraud and forgery, and demonstrates its technical approaches by providing sample codes.

Abstract

Insurance fraud represents a pervasive issue that results in substantial financial losses, particularly for insurance providers. The advent of digital technology has facilitated the utilisation of an array of techniques by fraudsters, enabling them to achieve their objectives with greater ease and security, whilst simultaneously facilitating the detection of such activities. The digital forensic analysis of documents and images necessitates a high degree of expertise, is a laborious process, and is susceptible to human error. Such occurrences can result in financial, temporal, and labour losses, in addition to delays in decision-making processes. This article examines the potential of an artificial intelligencepowered document and image analysis system, designated “RealityChecker,” for the detection of insurance fraud and the facilitation of rapid decision-making processes for insurers. The software developed by the author of this article for the authentication of diverse content offers considerable potential for applications not only in the insurance sector but also in the media and journalism. The RealityChecker software, which has also been released in a GPT version with limited features for rapid access, employs a range of techniques, including forensic image analysis and optical flow, to detect forgery and fraud in the insurance sector. It also facilitates the integration of multiple analysis methods and data types, enabling insurers to make accurate and rapid decisions. This article examines the RealityChecker software, discusses its effectiveness and usefulness in detecting fraud and forgery, and demonstrates its technical approaches by providing sample codes.

Yazarlar

Sefer DARICI

Anahtar Kelimeler

Insurance fraud, artificial intelligence, Reality Checker, image analysis, fake accident detection, forensic analysis

JEL Codes

K14, G22

Yayın Bilgileri

Cilt 4, Sayı 2, 2024 · Sayfa 45-63

DOI: 10.52898/ijif.2024.9

Dosyalar