NextGen AML: Distributed Deep Learning based Language Technologies to Augment Anti Money Laundering Investigation

Han, Jian; Barman, Utsab; Hayes, Jeremiah F.; Du, Jinhua; Burgin, Edward; Wan, Dadong

doi:10.18653/v1/p18-4007

Cited by 28 publications

(21 citation statements)

References 14 publications

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“…Money laundering detection solutions are categorized in two groups. First category is to identify the suspicious transactions, example - [23,40] has presented the AutoEncoder and Graph CNN deep learning methods respectively to identify suspicious transactions; and second category is to help investigate the identified suspicious transactions or alerts identified by rule-based systems, which is commonly called as decision support systems, example - [22,39] has presented a multi-channel CNN using NLP and scalable GCN method as a decision support system for investigating the alerts, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…• Multi-channel convolutional neural network Weber et al [39] developed a novel distributed and scalable framework using DL driven natural language processing (NLP) technology to augment AML monitoring and investigation. The proposed framework performs different level of sentiment analysis, entity identification, relationship extraction, and link analysis on different data sources such as news, tweets, social media, etc.…”

Section: B Deep Learningmentioning

confidence: 99%

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Deep Learning and Explainable Artificial Intelligence Techniques Applied for Detecting Money Laundering–A Critical Review

et al. 2021

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Money laundering has been a global issue for decades, which is one of the major threat for economy and society. Government, regulatory and financial institutions are combating it together in their respective capacity, however still billions of dollars in fines by authorities make the headlines in the news. High-speed internet services have enabled financial institutions to deliver better customer experience through multi-channel engagements, which has led to exponential growth in transactions and new avenues for laundering the money for fraudsters. Literature shows the usage of statistical methods, data mining and Machine Learning (ML) techniques for money laundering detection, but limited research on Deep Learning (DL) techniques, primarily due to lack of model interpretability and explainability of the decisions made. Several studies are conducted on application of ML for Anti-Money Laundering (AML), and Explainable Artificial Intelligence (XAI) techniques in general, but lacks the study on usage of DL techniques together with XAI. This paper aims to review the current state-of-the-art literature on DL together with XAI for identifying suspicious money laundering transactions and identify future research areas. Key findings of the review are, researchers have preferred variants of Convolutional Neural Networks, and AutoEncoder; graph deep learning together with natural language processing is emerging as an important technology for AML; XAI use is not seen in AML domain; 51% ML methods used in AML are non-interpretable, 58% studies used sample of old real data; key challenges for researchers are access to recent real transaction data and scarcity of labelled training data; and data being highly imbalanced. Future research directions are, application of XAI techniques to bring-out explainability, graph deep learning using natural language processing (NLP), unsupervised and reinforcement learning to handle lack of labelled data; and joint research programs between research community and industry to benefit from domain knowledge and controlled access to data.

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Section: Discussionmentioning

confidence: 99%

Section: B Deep Learningmentioning

confidence: 99%

Deep Learning and Explainable Artificial Intelligence Techniques Applied for Detecting Money Laundering–A Critical Review

et al. 2021

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“…Methods Application [81] AE Fraud detection in unbalanced datasets [82] Network topology credit card transactions [83] Natural language Processing Anti-money laundering detection [84] AE and RBM architecture Fraud detection in credit cards…”

Section: Referencementioning

confidence: 99%

“…One of the world's largest industries is money laundering, which is the unlawful process of hiding the original source of received money unlawfully by transmitting it through a complicated banking transaction. A recent work, considering anti-money laundering detection, designed a new framework using natural language processing (NLP) technology [83]. Here, the main reason for constructing a deep learning framework is decreasing the cost of human capital and time consumption.…”

Section: Referencementioning

confidence: 99%

Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

et al. 2020

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The popularity of deep reinforcement learning (DRL) applications in economics has increased exponentially. DRL, through a wide range of capabilities from reinforcement learning (RL) to deep learning (DL), offers vast opportunities for handling sophisticated dynamic economics systems. DRL is characterized by scalability with the potential to be applied to high-dimensional problems in conjunction with noisy and nonlinear patterns of economic data. In this paper, we initially consider a brief review of DL, RL, and deep RL methods in diverse applications in economics, providing an in-depth insight into the state-of-the-art. Furthermore, the architecture of DRL applied to economic applications is investigated in order to highlight the complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability. The survey results indicate that DRL can provide better performance and higher efficiency as compared to the traditional algorithms while facing real economic problems in the presence of risk parameters and the ever-increasing uncertainties.

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“…One of the world largest industry called money laundering, which is the unlawful process of hiding the original source of received money unlawfully by transmitting it through a complicated banking transaction. A recent work, considering anti money laundering detection, designed a new framework using natural language processing (NLP) technology [89]. Here, the main reason of constructing deep learning framework is due to diminishing the cost of human capital and consuming time.…”

Section: Deep Learning In Stock Pricingmentioning

confidence: 99%

Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

Mosavi¹,

Ghamisi²,

Faghan³

et al. 2020

Preprint

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The popularity of deep reinforcement learning (DRL) methods in economics have been exponentially increased. DRL through a wide range of capabilities from reinforcement learning (RL) and deep learning (DL) for handling sophisticated dynamic business environments offers vast opportunities. DRL is characterized by scalability with the potential to be applied to high-dimensional problems in conjunction with noisy and nonlinear patterns of economic data. In this work, we first consider a brief review of DL, RL, and deep RL methods in diverse applications in economics providing an in-depth insight into the state of the art. Furthermore, the architecture of DRL applied to economic applications is investigated in order to highlight the complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability. The survey results indicate that DRL can provide better performance and higher accuracy as compared to the traditional algorithms while facing real economic problems at the presence of risk parameters and the ever-increasing uncertainties.

show abstract

NextGen AML: Distributed Deep Learning based Language Technologies to Augment Anti Money Laundering Investigation

Cited by 28 publications

References 14 publications

Deep Learning and Explainable Artificial Intelligence Techniques Applied for Detecting Money Laundering–A Critical Review

Deep Learning and Explainable Artificial Intelligence Techniques Applied for Detecting Money Laundering–A Critical Review

Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

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