An Evaluation of AI-Powered Loan Risk Assessment Models in Financial Institutions: A Case Study of Banks in Plateau State
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- NGN 5000
Background of the Study
In recent years, artificial intelligence (AI) has emerged as a transformative tool in the financial services industry, offering solutions for improving operational efficiency and reducing risk. One of the most significant applications of AI in the banking sector is in loan risk assessment. AI-powered models enable financial institutions to evaluate the creditworthiness of borrowers more accurately by analyzing vast amounts of data from various sources, including historical financial records, transaction data, and even social media activity. These models use machine learning algorithms to detect patterns and predict the likelihood of loan repayment (Ogunleye & Adeleke, 2024).
In Plateau State, banks are beginning to adopt AI technologies to enhance their loan assessment processes. Traditional loan assessment models, which heavily rely on human judgment and static criteria such as credit scores, have been criticized for their limitations in assessing the full financial profile of an individual or business. AI-powered models, however, provide a more comprehensive, data-driven approach that can result in better-informed decisions and more accurate predictions of loan repayment behavior. Despite the promising benefits of AI, the adoption of AI models in loan risk assessment in Plateau State’s banks is still in its early stages. This study examines the effectiveness of AI-powered loan risk assessment models and their impact on credit risk management in Plateau State's banking sector.
Statement of the Problem
While AI-powered loan risk assessment models promise to improve the accuracy and efficiency of credit risk evaluations, the actual effectiveness of these systems in Plateau State’s banks has yet to be critically assessed. Many banks in the region face challenges such as data quality issues, integration with existing banking systems, and resistance to adopting new technologies. This study seeks to evaluate how AI is being utilized in loan risk assessment models in banks in Plateau State and to identify the barriers to successful implementation.
Objectives of the Study
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To assess the effectiveness of AI-powered loan risk assessment models in banks in Plateau State.
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To identify the challenges faced by banks in Plateau State when implementing AI-powered loan risk assessment models.
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To recommend strategies for improving the adoption and effectiveness of AI-powered loan risk assessment models in Plateau State’s financial institutions.
Research Questions
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How effective are AI-powered loan risk assessment models in improving the accuracy of loan approvals in banks in Plateau State?
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What challenges do banks in Plateau State face in adopting AI-powered loan risk assessment models?
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What strategies can be employed by banks in Plateau State to enhance the implementation of AI-powered loan risk assessment models?
Research Hypotheses
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AI-powered loan risk assessment models do not significantly improve the accuracy of loan approval decisions in Plateau State’s banks.
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Challenges such as data quality and integration difficulties do not significantly hinder the adoption of AI-powered loan risk assessment models in Plateau State’s banks.
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Strategies aimed at enhancing AI implementation will not significantly improve the effectiveness of loan risk assessments in Plateau State’s financial institutions.
Scope and Limitations of the Study
This study focuses on banks in Plateau State that have begun using AI-powered models for loan risk assessment. The study is limited by the availability of data on loan approval outcomes and the potential reluctance of financial institutions to share proprietary information.
Definitions of Terms
AI-Powered Loan Risk Assessment Models: Machine learning algorithms and other AI technologies used to assess the risk of lending to individuals or businesses based on a variety of data points.
Loan Risk Assessment: The process by which financial institutions evaluate the likelihood that a borrower will repay a loan.
Financial Institutions: Banks and other financial entities that provide services such as loans, savings accounts, and financial advice.
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