Money Laundering Alert Prioritisation Using Machine Learning and Benford’s Law
type: CreativeWork
description: TymeBank’s traditional rule-based system for Anti-Money Laundering generates a high volume of false-positive alerts. From an industrial engineering perspective, this creates a significant process inefficiency, resulting in a costly and unscalable investigative bottleneck that consumes analyst resources and diverts focus from genuine financial crime. This research addresses the problem by developing a machine learning risk-scoring system. The system pursues multiple objectives, aiming to enhance fraud detection by identifying genuine mule accounts while simultaneously enabling risk-based prioritisation to reduce wasted investigative effort. Using TymeBank’s anonymised data, a controlled experiment compared baseline models (Model A), trained on the full dataset, against specialised models (Model B). These specialised configurations were trained only on high-transaction profiles and incorporated additional features derived from Benford’s Law. A range of algorithms, including Logistic Regression, Na¨ıve Bayes, Decision Trees, K-Nearest Neighbours, and Random Forests, was systematically evaluated. To assess models against the multiple objectives, a bespoke evaluation approach with a businessoriented focus was developed. Standard statistical metrics, such as accuracy, are often misleading in the context of imbalanced data, while precision and recall present inherent trade-offs. To address this, a Total Cost function was introduced to determine the balance between effective fraud detection and the high cost of unnecessary investigations, with the latter being directly quantified through a Resource Efficiency metric. The results suggest that a Random Forest, Model A provides the most cost-effective solution, achieving the lowest Total Cost and the highest Resource Efficiency among all tested configurations. In contrast, the specialised Model B exhibited a pronounced trade-off. Although it achieved an increase in the Fraud Capture Rate, this was accompanied by a decrease in …
abstract: TymeBank’s traditional rule-based system for Anti-Money Laundering generates a high volume of false-positive alerts. From an industrial engineering perspective, this creates a significant process inefficiency, resulting in a costly and unscalable investigative bottleneck that consumes analyst resources and diverts focus from genuine financial crime. This research addresses the problem by developing a machine learning risk-scoring system. The system pursues multiple objectives, aiming to enhance fraud detection by identifying genuine mule accounts while simultaneously enabling risk-based prioritisation to reduce wasted investigative effort. Using TymeBank’s anonymised data, a controlled experiment compared baseline models (Model A), trained on the full dataset, against specialised models (Model B). These specialised configurations were trained only on high-transaction profiles and incorporated additional features derived from Benford’s Law. A range of algorithms, including Logistic Regression, Na¨ıve Bayes, Decision Trees, K-Nearest Neighbours, and Random Forests, was systematically evaluated. To assess models against the multiple objectives, a bespoke evaluation approach with a businessoriented focus was developed. Standard statistical metrics, such as accuracy, are often misleading in the context of imbalanced data, while precision and recall present inherent trade-offs. To address this, a Total Cost function was introduced to determine the balance between effective fraud detection and the high cost of unnecessary investigations, with the latter being directly quantified through a Resource Efficiency metric. The results suggest that a Random Forest, Model A provides the most cost-effective solution, achieving the lowest Total Cost and the highest Resource Efficiency among all tested configurations. In contrast, the specialised Model B exhibited a pronounced trade-off. Although it achieved an increase in the Fraud Capture Rate, this was accompanied by a decrease in …
author: Spies, Luka
language: en
date published: 2026-03
date modified: 2026-07-24T04:40:09Z
provider: Stellenbosch University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Stellenbosch : Stellenbosch University