Money laundering regulatory risk evaluation using Bitmap Index-based Decision Tree

Jayasree, Vikas; Balan, R.V. Siva

doi:10.1016/j.jaubas.2016.03.001

Cited by 11 publications

(8 citation statements)

References 9 publications

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“…For instance, a DM model is presented in [6] that applied K-means clustering and Association Rule Mining for identifying suspected sequence of money laundering processes. A novel technique named Bitmap Index-based DT was proposed in [32] for evaluating the risk factor of money laundering with Statlog German credit data.…”

Section: Security and Fraud Detectionmentioning

confidence: 99%

“…Security and fraud detection [6, classification (DT, NN, SVM, NB), k-mean clustering, ARM Australia [12], Latin-America [29], Greece [24], Germany [32], Belgium [21], UCI Repository [15][16][17][18] Identifying phishing, fraud, money laundering, credit card fraud, security trend of mobile/online/traditional banking.…”

Section: References Key Techniques Regions Purposesmentioning

confidence: 99%

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Digitalisation and Big Data Mining in Banking

Hassani

Huang

Silva

2018

BDCC

120

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Banking as a data intensive subject has been progressing continuously under the promoting influences of the era of big data. Exploring the advanced big data analytic tools like Data Mining (DM) techniques is key for the banking sector, which aims to reveal valuable information from the overwhelming volume of data and achieve better strategic management and customer satisfaction. In order to provide sound direction for the future research and development, a comprehensive and most up to date review of the current research status of DM in banking will be extremely beneficial.Since existing reviews only cover the applications until 2013, this paper aims to fill this research gap and presents the significant progressions and most recent DM implementations in banking post 2013. By collecting and analyzing the trends of research focus, data resources, technological aids, and data analytical tools, this paper contributes to bringing valuable insights with regard to the future developments of both DM and the banking sector along with a comprehensive one stop reference table. Moreover, we identify the key obstacles and present a summary for all interested parties that are facing the challenges of big data.

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Section: Security and Fraud Detectionmentioning

confidence: 99%

Section: References Key Techniques Regions Purposesmentioning

confidence: 99%

Digitalisation and Big Data Mining in Banking

Hassani

Huang

Silva

2018

BDCC

120

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“…Radial basis function neural network has been used in [20]. On the other hand, the decision tree approach has been applied in [21]- [24]. In another recent work [25], an adaptive neuro-fuzzy inference system is adopted for the AML problem.…”

Section: State Of the Art A Data Science Approachesmentioning

confidence: 99%

“…Among supervised learning works in the AML domain, [19], [20] have adopted transaction features such as sum and frequency of monetary transactions and [21], [24] have developed models with CRM features. However, there is no example combining these two characteristics.…”

Section: State Of the Art A Data Science Approachesmentioning

confidence: 99%

A Time-Frequency Based Suspicious Activity Detection for Anti-Money Laundering

et al. 2021

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Money laundering is the crucial mechanism utilized by criminals to inject proceeds of crime into the financial system. The primary responsibility of the detection of suspicious activity related to money laundering is with the financial institutions. Most of the current systems in these institutions are rule-based and ineffective (over 90 % false positives). The available data science-based anti-money laundering (AML) models to replace the existing rule-based systems work on customer relationship management (CRM) features and time characteristics of transaction behaviour. Due to thousands of possible account features, customer features, and their combinations, it is challenging to perform feature engineering to achieve reasonable accuracy. Aiming to improve the detection performance of suspicious transaction monitoring systems for AML systems, in this article, we introduce a novel feature set based on time-frequency analysis, that uses 2-D representations of financial transactions. Random forest is utilized as a machine learning method, and simulated annealing is adopted for hyperparameter tuning. The designed algorithm is tested on real banking data, proving the results' efficacy in practically relevant environments. It is shown that the timefrequency characteristics are discriminatory features for suspicious and non-suspicious entities. Therefore, these features substantially improve the area under curve results (over 1%) of the existing data science-based transaction monitoring systems. Using time-frequency features alone, a false positive rate of 14.9% has been achieved, with an F-score of 59.05%. When combined with transaction and CRM features, the false positive rate is 11.85%, and the F-Score is improved to 74.06%. INDEX TERMSAnomaly detection, anti-money laundering, compliance, random forest algorithm, timefrequency analysis, transaction monitoring.

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“…Bitmap Index-based DT (BIDT) algorithm was implemented by Jayasree and Balan [13] to evaluate the adaptability risk for money laundering. Results of false positive and true positive rates, alongside the adaptability rate and risk identification time, showed that the proposed approach outperformed other methods.…”

Section: Related Workmentioning

confidence: 99%

Variational Autoencoders and Wasserstein Generative Adversarial Networks for Improving the Anti-Money Laundering Process

2021

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There has been much recent work on fraud and Anti Money Laundering (AML) detection using machine learning techniques. However, most algorithms are based on supervised techniques. Studies show that supervised techniques often have the limitation of not adapting well to new irregular fraud patterns when the dataset is highly imbalanced. Instead, unsupervised learning can have a better capability to find anomalous and irregular patterns in new transaction. Despite this, unsupervised techniques also have the disadvantage of not being able to give state-of-the-art detection results. We propose a suite of unsupervised and deep learning techniques to implement an anti-money laundering and fraud detection system to resolve this limitation. The system leverages three deep learning models: autoencoder (AE), variational autoencoder (VAE), and a generative adversarial network. We preprocess the given dataset to separate the Transaction Date attribute into its base components to capture time-related fraud patterns. Also, Wasserstein Generative Adversarial Network (WGAN) is used to generate fraud transactions, which are then mixed with the base dataset to form a more balanced mixed dataset. These two datasets are used to train the AE and VAE models. We built two versions of the AE model (single-loss and multi-loss) besides a novel method of calculating the anomaly score threshold, called Recall-First Threshold (RFT), which helps enhance the model's performance. Experimental results demonstrated that the False Positive Rate (FPR) drops down to as low as 7% in the proposed multi-loss AE model. In comparison, we achieved an accuracy of 93%, with 100% of the fraud transactions recalled successfully.

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Money laundering regulatory risk evaluation using Bitmap Index-based Decision Tree

Cited by 11 publications

References 9 publications

Digitalisation and Big Data Mining in Banking

Digitalisation and Big Data Mining in Banking

A Time-Frequency Based Suspicious Activity Detection for Anti-Money Laundering

Variational Autoencoders and Wasserstein Generative Adversarial Networks for Improving the Anti-Money Laundering Process

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