The Issues, Challenges and Impacts of Implementing Machine Learning in the Financial Services Sector

Abstract

Machine learning (ML) is another branch of technology deemed valuable in the financial sector because of its ability to assist organisations in identifying fraudulent transactions and predicting the ability of customers to repay their bank-issued loans. However, like any type of technology, the adoption of ML introduces changes that impact the processes and operations of the financial service sector. Research on the merits of implementing ML is well captured; however, research on such developments' challenges, issues, and impact is scant. To address this gap, a systematic literature review was undertaken to contribute to the research discourse by investigating the issues, challenges and impacts of implementing ML in the financial business sector. The ScienceDirect, EBSCOhost and ProQuest databases were used to search for the relevant scholarly sources published from 2013-2022. The literature was reviewed based on the PRISMA flow diagram and a thematic analysis of the 35 articles that met the inclusion criteria. The outcome of the review revealed that more complex models, such as artificial neural networks, were implemented in all the identified financial services sectors, followed by support vector machines. This review concludes that the larger the quantity and complexity of financial data, the less the data quality, which significantly reduces the prediction performance, efficiency, and accuracy of the model, which can significantly impact the operations, financial aspects, and the overall reputation of the firms. Future research must explore the impact of ML on the operational, adoption and skills shortages in the financial sector.