Predicting Fraud in Mobile Money Transfer Using Case-Based Reasoning

Andoh

et al. 2019

JMLC

Purpose Fraud is a global economic menace which threatens the survival of individuals, firms, industries and economies, and the mobile money service is no exception. This paper aims to explore the main causes of fraud in the mobile money services in Ghana and the measures to combat the menace by the key stakeholders connected to the mobile money services. The paper is motivated by recent reports of numerous fraudulent transactions on the mobile money platform, and the need to clamp down these nefarious transactions with effective and practical measures to sustain the service. Design/methodology/approach A thorough review of existing studies on fraud risk relating to mobile money services was done revealing a paucity of literature on the subject. Primary data were gathered using an interview guide to explore the magnitude of the problem based on the views of employees of mobile money operators, mobile money agents, banking supervisors from Bank of Ghana, employees of partnering banks, employees of National Communications Authority and mobile money subscribers. Findings The study revealed that fraud in mobile money services is caused by weak internal controls and systems, lack of sophisticated information technology tools to detect the menace, inadequate education and training and the poor remuneration of employees. These factors disrupt the growth, and the smooth-running of the services. To curb this menace, a detailed legal code and internal fraud policy should be developed and used by mobile money operators and partner banks. Adequate training for mobile money agents should be encouraged coupled with public awareness campaigns to educate stakeholders especially the mobile money subscribers on the tricks of the fraudsters. Research limitations/implications With the chosen research methodology and limited sample size, the findings may not reflect the views of all the stakeholders connected to the mobile money services. Therefore, future studies on this subject are entreated to use research methods which embrace larger samples to get more details about this menace. Practical implications The study will assist in tackling the mobile money fraud to sustain the service in the foreseeable future. Originality/value This paper contributes to scanty literature on fraud relating to the mobile money services by drawing lessons from a middle-income country.

Section: Literature Reviewmentioning

confidence: 99%

Control of fraud on mobile money services in Ghana: an exploratory study

Andoh

et al. 2019

JMLC

“…The researcher concluded by reporting a success rate of 92.74% using C4.5 decision tree algorithm. Adeyinka et al [20] provided a great insight into predicting fraud in mobile money transfer. Their work was based on Case-Based Reasoning (CBR) which is an alternative to standard machine learning methods.…”

Section: Related Workmentioning

confidence: 99%

“…However, some problems arise from this regulatory regime by Central Banks. Central Banks recognize these telecommunication companies as merely providing telecommunication infrastructure for payment system [20] thereby playing only passive roles. Works are ongoing for proposals to develop a legislative framework to properly streamline the operations of MMTs.…”

Section: Regulating Mmts the Role Of Central Banks And Telecommunicamentioning

confidence: 99%

See 1 more Smart Citation

Mobile Money Fraud Prediction—A Cross-Case Analysis on the Efficiency of Support Vector Machines, Gradient Boosted Decision Trees, and Naïve Bayes Algorithms

2020

The onset of COVID-19 has re-emphasized the importance of FinTech especially in developing countries as the major powers of the world are already enjoying the advantages that come with the adoption of FinTech. Handling of physical cash has been established as a means of transmitting the novel corona virus. Again, research has established that, been unbanked raises the potential of sinking one into abject poverty. Over the years, developing countries have been piloting the various forms of FinTech, but the very one that has come to stay is the Mobile Money Transactions (MMT). As mobile money transactions attempt to gain a foothold, it faces several problems, the most important of them is mobile money fraud. This paper seeks to provide a solution to this problem by looking at machine learning algorithms based on support vector machines (kernel-based), gradient boosted decision tree (tree-based) and Naïve Bayes (probabilistic based) algorithms, taking into consideration the imbalanced nature of the dataset. Our experiments showed that the use of gradient boosted decision tree holds a great potential in combating the problem of mobile money fraud as it was able to produce near perfect results.

Mathematical Problems in Engineering

“…is is commonly referred to as overfitting in machine learning. e ideal binary classification solutions should have low variability, by producing consistent predictions across different datasets [10]. e goal of this work was to conduct a thorough investigation of the impact of employing a hybrid data-point strategy to handle the misclassification problem in credit card datasets that were imbalanced.…”

Section: Introductionmentioning

confidence: 99%

Solving Misclassification of the Credit Card Imbalance Problem Using Near Miss

Mqadi

Naicker

Adeliyi

2021

In ordinary credit card datasets, there are far fewer fraudulent transactions than ordinary transactions. In dealing with the credit card imbalance problem, the ideal solution must have low bias and low variance. The paper aims to provide an in-depth experimental investigation of the effect of using a hybrid data-point approach to resolve the class misclassification problem in imbalanced credit card datasets. The goal of the research was to use a novel technique to manage unbalanced datasets to improve the effectiveness of machine learning algorithms in detecting fraud or anomalous patterns in huge volumes of financial transaction records where the class distribution was imbalanced. The paper proposed using random forest and a hybrid data-point approach combining feature selection with Near Miss-based undersampling technique. We assessed the proposed method on two imbalanced credit card datasets, namely, the European Credit Card dataset and the UCI Credit Card dataset. The experimental results were reported using performance matrices. We compared the classification results of logistic regression, support vector machine, decision tree, and random forest before and after using our approach. The findings showed that the proposed approach improved the predictive accuracy of the logistic regression, support vector machine, decision tree, and random forest algorithms in credit card datasets. Furthermore, we found that, out of the four algorithms, the random forest produced the best results.