Title: Robust and resourceful automobile insurance fraud detection with multi-stacked LSTM network and adaptive synthetic oversampling

Authors: Isaac Kofi Nti; Kwabena Adu; Peter Nimbe; Owusu Nyarko-Boateng; Adebayo Felix Adekoya; Peter Appiahene

Addresses: Department of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani, Ghana; School of Information Technology, University of Cincinnati, OH, USA ' Department of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani, Ghana ' Department of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani, Ghana ' Department of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani, Ghana ' Department of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani, Ghana ' Department of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani, Ghana

Abstract: Insurance companies worldwide are concerned about financial losses due to false claims. Automobile insurance fraud (AIF) has become more sophisticated, causing the yearly loss of trillions of dollars. AIF is tough to establish, and acquiring a thorough knowledge of the problem is complex. Also, AIF investigators have relied on manual claims inspection, proving costly, inefficient, and time-consuming. This paper proposed a robust and resourceful approach to AIF detection with multi-stacked LSTM (MSLSTM) reinforced with the adaptive synthetic (ADASYN) sampling algorithm for imbalanced learning. We experiment with the proposed model with a publicly available AIF dataset from Kaggle. Using accuracy, recall, precision, F1-score, and AUC, we compared the performance of our proposed MSLSTM model with well-known machine learning algorithms and previous AIF detection works. Our results showed a fair performance (accuracy = 95%, precision = 94%, AUC = 97% and F1-score = 92%) of the MSLSTM model than other algorithms and works.

Keywords: automobile insurance fraud; AIF; car fraud detection; stacked LSTM network; adaptive synthetic oversampling.

DOI: 10.1504/IJADS.2024.137007

International Journal of Applied Decision Sciences, 2024 Vol.17 No.2, pp.230 - 249

Received: 18 May 2022
Accepted: 01 Oct 2022
Published online: 01 Mar 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article