An Anti-fraud System for Car Insurance Claim Based on Visual Evidence

TL;DR

This paper presents a novel anti-fraud system for car insurance claims that uses deep learning to analyze damage images, aiming to prevent fraudulent claims efficiently and accurately.

Contribution

It introduces a new dataset of car damage images and a deep learning pipeline combining YOLO and VGG models for damage detection and feature extraction.

Findings

Effective in preventing fraudulent claims

Speeds up insurance claim processing

Robust damage detection performance

Abstract

Automatically scene understanding using machine learning algorithms has been widely applied to different industries to reduce the cost of manual labor. Nowadays, insurance companies launch express vehicle insurance claim and settlement by allowing customers uploading pictures taken by mobile devices. This kind of insurance claim is treated as small claim and can be processed either manually or automatically in a quick fashion. However, due to the increasing amount of claims every day, system or people are likely to be fooled by repeated claims for identical case leading to big lost to insurance companies.Thus, an anti-fraud checking before processing the claim is necessary. We create the first data set of car damage images collected from internet and local parking lots. In addition, we proposed an approach to generate robust deep features by locating the damages accurately and efficiently in the images. The state-of-the-art real-time object detector YOLO \cite{redmon2016you}is modified to train and discover damage region as an important part of the pipeline. Both local and global deep features are extracted using VGG model\cite{Simonyan14c}, which are fused later for more robust system performance. Experiments show our approach is effective in preventing fraud claims as well as meet the requirement to speed up the insurance claim prepossessing.