Background of the Study
Machine learning (ML) has emerged as a powerful tool in automated risk assessment, particularly within fintech firms that deal with large amounts of financial data. By leveraging algorithms that learn from data and improve over time, fintech companies can assess risks more accurately and make faster, data-driven decisions (Adeoye & Aliyu, 2023). In Yobe State, where fintech is increasingly being adopted to foster financial inclusion, the potential of machine learning to transform risk assessment in this sector is significant.
Fintech firms face numerous challenges, including fraud, credit risk, and regulatory compliance. Traditional risk assessment methods often struggle to keep up with the rapid growth of digital financial services, leading to inefficiencies and inaccuracies. Machine learning can automate this process, making it faster, more reliable, and cost-effective by identifying patterns and anomalies in vast datasets. However, the integration of ML into risk assessment practices in fintech firms in Yobe State is still in its early stages, and this study aims to evaluate how machine learning can improve these processes.
Statement of the Problem
Despite the potential benefits of machine learning in automating risk assessment, many fintech firms in Yobe State have not fully embraced these technologies. The lack of adequate infrastructure, skilled personnel, and awareness about ML capabilities hinders the implementation of effective risk management solutions. This study will explore how fintech firms can leverage machine learning to enhance risk assessment practices and what factors are limiting its adoption.
Objectives of the Study
Research Questions
Research Hypotheses
Scope and Limitations of the Study
This study focuses on fintech firms operating in Yobe State and evaluates the role of machine learning in their risk assessment practices. The main limitation is the potential lack of data on the effectiveness of machine learning models used by these firms, as well as the challenges associated with data privacy.
Definitions of Terms
Machine Learning (ML): A subset of artificial intelligence that uses algorithms to analyze data, identify patterns, and make predictions or decisions without explicit programming.
Risk Assessment: The process of identifying, evaluating, and prioritizing risks to minimize their impact on an organization.
Fintech Firms: Financial technology companies that offer digital financial services using innovative technology solutions.