Figure 7: Feature Relevance
Discussion and Conclusion
The popularity of mobile money transfer services has grown rapidly in recent years, driven by the increasing penetration of mobile devices and the widespread adoption of mobile banking services. However, using mobile money transfer services has also created new opportunities for criminals to launder money. To provide more insights into this problem, this study employs ML classifiers to predict mobile money transfer laundering transactions 32,40. While all the classifiers were very useful in predicting suspicion transactions, the random forest model was the most consistent and best-performing model across all the classifiers 41. The random forest model is a powerful ML technique for fraud detection because it can effectively learn from data with many features and can be tuned to achieve high precision (.96), recall (.80), and F1-score (.87). The findings from this study provide useful intelligence for mobile money service providers and law enforcement agencies to fight against money laundering.
Given the current global landscape, it is not surprising that the use of mobile money has increased dramatically in the past few years53. For consumers and businesses alike, the convenience and flexibility of mobile wallets have made them a popular choice 16. However, the rise of mobile money has also attracted the attention of criminals looking for new ways to launder their ill-gotten gains 1,18,32. In many cases, mobile money services are being used to facilitate money laundering by allowing criminals to quickly and easily move large amounts of cash without raising suspicion. For example, recent studies found that a significant proportion of mobile money users have been involved in a transaction that could be considered suspicious 8,14,44,45. Considering these findings, more must be done to prevent mobile money from being used for illegal purposes. While mobile money has brought many benefits to the global economy, it is important to remember that those with criminal intent can also exploit its services.
Financial institutions have constantly been pressured to prevent money laundering and terrorist financing by implementing stringent compliance measures 12,53. The mobile money ecosystem has only added to this pressure, as financial institutions must now also contend with the challenges posed by digital transactions. In response, there has been a shift in emphasis from traditional, deterministic rules-based methodologies toward more sophisticated computational techniques. This shift is primarily because the sheer volume of transaction data makes it difficult to flag and detect suspicious activities using rules-based approaches. Computational techniques in the form of ML offer a more effective way to monitor suspicious behaviour, as they can consider a broader range of factors and larger datasets. Adopting ML techniques for money laundering detection is a promising development in the fight against illicit activities. These techniques could make catching people trying to launder money through criminal networks much more accessible. They could also help mobile money service providers and law enforcement agencies stay one step ahead of individuals trying to launder illicit funds.
Limitations and Future Research
While ML is a valuable tool for fraud detection, several limitations must be considered when conducting research in this area. First, the data sets used to train the algorithms may not represent the population of interest, leading to inaccurate predictions. Second, ML models can be biased based on previous studies that used flawed methodology. Third, it is important to remember that ML is only one tool for detecting fraud; other methods, such as human intelligence and expert analysis, may be more effective in certain situations. Despite these limitations, ML is a powerful tool that significantly impacts fraud research. With continued advances in this area, even more, progress will likely be made in using ML algorithms to fight against fraud. Future research can employ ML to identify behaviour patterns that may indicate fraudulent activity, while human intelligence can provide context and insights that an automated system may miss. By combining these two approaches, future research can significantly improve the fight against fraud.