Title: A novel prediction model based on long short-term memory optimised by dynamic evolutionary glowworm swarm optimisation for money laundering risk

Authors: Pingfan Xia; Zhiwei Ni; Xuhui Zhu; Qizhi He; Qian Chen

Addresses: School of Management, Hefei University of Technology, Hefei, Anhui, China ' School of Management, Hefei University of Technology, Hefei, Anhui, China ' School of Management, Hefei University of Technology, Hefei, Anhui, China ' School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, Zhejiang, China ' School of Management, Hefei University of Technology, Hefei, Anhui, China

Abstract: The accurate prediction of money laundering risk is conducive to the risk prevention, and its task implies the characteristics of time series. Long short-term memory (LSTM) is widely developed in time series problems, but its parameters need to be optimised for delivering good predictive capacity. In this work, a novel bio-inspired algorithm, named dynamic evolutionary glowworm swarm optimisation (DEGSO), is designed. DEGSO employs adaptive step-size strategy, dynamic evolutionary mechanism, and directional mutation mechanism for improving the search performance. The money laundering risk is identified via a combination of DEGSO and LSTM (DEGSO-LSTM). DEGSO is wielded to optimise the main parameters of LSTM. Numerical results on four test functions indicate that DEGSO delivers better performance. Performance test results demonstrate that DEGSO-LSTM performs better than other state-of-the-art approaches. DEGSO-LSTM also attains satisfactory results in money laundering risk prediction, and its potential as a solution to financial fraud risk prediction.

Keywords: glowworm swarm optimisation; dynamic evolutionary mechanism; long short-term memory; LSTM; money laundering risk prediction.

DOI: 10.1504/IJBIC.2022.121233

International Journal of Bio-Inspired Computation, 2022 Vol.19 No.2, pp.77 - 86

Received: 16 Mar 2021
Accepted: 21 Aug 2021
Published online: 01 Mar 2022 *

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