A Novel Spatiotemporal Prediction Approach Based on Graph Convolution Neural Networks and Long Short-Term Memory for Money Laundering Fraud

Xia, Pingfan; Ni, Zhiwei; Xiao, Hongwang; Zhu, Xuhui; Peng, Peng

doi:10.1007/s13369-021-06116-2

Cited by 14 publications

(13 citation statements)

References 54 publications

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“…Machine-learning algorithms have found wide applicability in financial world problems 21 , 22 , trading, including portfolio management, risk assessment, and price forecasting 23 . Many studies emphasize predicting futures asset prices, like stocks or Bitcoin 24 – 27 , underpinning that accurate futures price predictions could guide traders’ decisions, increase profits, and hedge against market risks.…”

Section: Related Workmentioning

confidence: 99%

Multi-level deep Q-networks for Bitcoin trading strategies

Otabek,

Choi

2024

Sci Rep

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The Bitcoin market has experienced unprecedented growth, attracting financial traders seeking to capitalize on its potential. As the most widely recognized digital currency, Bitcoin holds a crucial position in the global financial landscape, shaping the overall cryptocurrency ecosystem and driving innovation in financial technology. Despite the use of technical analysis and machine learning, devising successful Bitcoin trading strategies remains a challenge. Recently, deep reinforcement learning algorithms have shown promise in tackling complex problems, including profitable trading strategy development. However, existing studies have not adequately addressed the simultaneous consideration of three critical factors: gaining high profits, lowering the level of risk, and maintaining a high number of active trades. In this study, we propose a multi-level deep Q-network (M-DQN) that leverages historical Bitcoin price data and Twitter sentiment analysis. In addition, an innovative preprocessing pipeline is introduced to extract valuable insights from the data, which are then input into the M-DQN model. A novel reward function is further developed to encourage the M-DQN model to focus on these three factors, thereby filling the gap left by previous studies. By integrating the proposed preprocessing technique with the novel reward function and DQN, we aim to optimize trading decisions in the Bitcoin market. In the experiments, this integration led to a noteworthy 29.93% increase in investment value from the initial amount and a Sharpe Ratio in excess of 2.7 in measuring risk-adjusted return. This performance significantly surpasses that of the state-of-the-art studies aiming to develop an efficient Bitcoin trading strategy. Therefore, the proposed method makes a valuable contribution to the field of Bitcoin trading and financial technology.

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Section: Related Workmentioning

confidence: 99%

Multi-level deep Q-networks for Bitcoin trading strategies

Otabek,

Choi

2024

Sci Rep

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“…Money laundering transactions involve complex currency flow paths, forming specific connection patterns that constitute a graph network structure of money laundering transactions. Graph convolutional neural networks (GCNs) [29][30][31][32][33][34][35][36] are effective methods for capturing node connection relationships by aggregating similar nodes to form spatially meaningful node clusters, aiding in the discovery of potential money laundering patterns. Graph convolution can also explain information transmission patterns between nodes, helping identify potential paths in money laundering activities.…”

Section: Introductionmentioning

confidence: 99%

A Novel Money Laundering Prediction Model Based on a Dynamic Graph Convolutional Neural Network and Long Short-Term Memory

Wan,

2024

Symmetry

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Money laundering is an illicit activity that seeks to conceal the nature and origins of criminal proceeds, posing a substantial threat to the national economy, the political order, and social stability. To scientifically and reasonably predict money laundering risks, this paper focuses on the “layering” stage of the money laundering process in the field of supervised learning for money laundering fraud prediction. A money laundering and fraud prediction model based on deep learning, referred to as MDGC-LSTM, is proposed. The model combines the use of a dynamic graph convolutional network (MDGC) and a long short-term memory (LSTM) network to efficiently identify illegal money laundering activities within financial transactions. MDGC-LSTM constructs dynamic graph snapshots with symmetrical spatiotemporal structures based on transaction information, representing transaction nodes and currency flows as graph nodes and edges, respectively, and effectively captures the relationships between temporal and spatial structures, thus achieving the dynamic prediction of fraudulent transactions. The experimental results demonstrate that compared with traditional algorithms and other deep learning models, MDGC-LSTM achieves significant advantages in comprehensive spatiotemporal feature modeling. Specifically, based on the Elliptic dataset, MDGC-LSTM improves the Macro-F1 score by 0.25 compared to that of the anti-money laundering fraud prediction model currently considered optimal.

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“…Previous financial forecasting works were mainly based on numerical analysis methods on historical data, including risk prediction Qi et al (2014), return prediction Baker et al (2006); Li et al (2013), default prediction Chen and Wu (2014); Duffie et al (2007), stock price prediction Avramov and Chordia (2006); Grinblatt and Moskowitz (2004); Paye (2012), and so forth. Recently, techniques such as machine learning Ghosh et al (2018); Kamruzzaman et al (2022); Song et al (2010), deep learning Alaminos et al (2022); Kim and Ahn (2012); Zhou et al (2022) and graph neural networks Wu et al (2022); Xia et al (2022) have been applied in financial forecasting. Only focusing on historical numerical data may have certain limitations.…”

Section: Introductionmentioning

confidence: 99%

Interpretable deep learning based text regression for financial prediction

Liang

Zhang

2023

Expert Systems

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Text regression is an important task in natural language processing (NLP), which aims to predict continuous numerical values associated with text. Previous work focused on linear text regression requiring manual feature selection for financial prediction. Recently, non-linear text regression through neural network models has become a trend. However, most models rely only on convolutional neural networks (CNN) and suffer from insufficient interpretability. In this paper, we propose a deep neural network model named EM-CBA for text regression and further interpret the model. The proposed model is powered by word EMbedding, CNN, Bidirectional long short-term memory (Bi-LSTM) and Attention mechanism. The proposed EM-CBA takes financial report texts as input and predicts a financial metric named return on assets (ROA). We conduct comprehensive experiments on a dataset about the reports of enterprises. Experimental results show that the proposed model provides more accurate predictions of enterprises' metrics than previous convolutional neural network models and other classical models. The validity of each module of the model is also verified. Finally, we demonstrate a way of performing analysis in words change and results errors to intuitively interpret the effect of different text inputs on the model. The analysis demonstrates that the model is able to use information about sentiment words to analyse their associated contexts to revise the predictions.

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A Novel Spatiotemporal Prediction Approach Based on Graph Convolution Neural Networks and Long Short-Term Memory for Money Laundering Fraud

Cited by 14 publications

References 54 publications

Multi-level deep Q-networks for Bitcoin trading strategies

Multi-level deep Q-networks for Bitcoin trading strategies

A Novel Money Laundering Prediction Model Based on a Dynamic Graph Convolutional Neural Network and Long Short-Term Memory

Interpretable deep learning based text regression for financial prediction

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