Comparative Analysis on Classification Algorithms of Auto-Insurance Fraud Detection based on Feature Selection Algorithms

Panigrahi, Sapna; Palkar, Bhakti

doi:10.26438/ijcse/v6i9.7277

Cited by 5 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As the sophistication of these models grows, so does their potential impact on improving risk assessment and fraud detection strategies within the auto insurance domain. The detection of motor insurance fraud was thoroughly studied by Panigrahi et al, utilizing feature selection algorithms and machine learning methods [4]. Utilizing three separate feature selection algorithms, they concentrated on removing fundamental features from data on auto insurance fraud.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on Detecting Auto Insurance Fraud

Quan

2024

HBEM

View full text Add to dashboard Cite

With the expansion of the insurance sector has come an increasing concern about insurance fraud. This issue has grown in prominence over time, necessitating the implementation of appropriate countermeasures. In response, this work conducts a thorough analysis of the available literature, with an emphasis on the incorporation of machine learning approaches for detecting fraudulent actions in the field of automobile insurance. By delving into the intricacies of the vehicle insurance fraud landscape, the research investigates the strategic deployment of expert systems and machine learning models to enhance fraud identification processes. Drawing from the wealth of high-quality auto insurance data spanning the last five years, this work explores the implementation of diverse machine learning algorithms, including Logistic Regression, Decision Tree, and Discriminative Analysis, in order to construct comprehensive predictive models. After the modeling was completed, comprehensive testing and analysis were conducted, and the results showed that the logistic regression model had the highest accuracy among these three models. Finally, the future research direction of automobile insurance fraud detection technology is discussed, and how to reasonably prevent automobile insurance fraud is proposed.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Research on Detecting Auto Insurance Fraud

Quan

2024

HBEM

View full text Add to dashboard Cite

show abstract

“…The findings of this research show that the Decision tree model is more accurate to identify the fraud in auto insurance platforms as compared to Naive Bayes. Panigrahi et al (Panigrahi & Palkar, B., 2018) conducted research for the automobile insurance industry on their claims dataset. It is a binary classification analysis by comparing the various ML models to detect fraud using the feature selection algorithms.…”

Section: Related Workmentioning

confidence: 99%

Fraud Detection in Automotive Insurance Domain Using Machine Learning

2022

Proceedings of the 16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing (CGV

View full text Add to dashboard Cite

The insurance industry is one of the most vital aspects of modern society, the economy, and people's lives. It brings peace and security to people by offsetting the financial risks of damage and loss. Fraud in the Automotive Insurance domain illustrates a scenario in which the client obtains money by providing falsified papers, demonstrating fake accidents or economic claims for early losses. One of the biggest harmful challenges here is an interaction between policyholders and the insurance industry which creates a possible situation for fraud claims. By keeping these in mind, in this research, fraud detection in the automotive insurance domain (AID) is performed using machine learning (ML) techniques. It is a binary classification problem that utilized the recursive feature elimination (RFE) method to uncover the most influential factors for detecting fraud. The significance of each attribute is also determined. Moreover, a comparative analysis of the ML models is performed. The experimental outcomes show that the multilayer perceptron model is quite robust and outperforms the other algorithms in detecting fraud.

show abstract