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.