Global Journal of Accounting

The Impact of Banks’ Liquidity on Economic Growth in Nigeria

Isaac Azubuike Ogbuji — 2025-05-12

This study investigates the impact of liquidity management indicators on the Gross Growth Rate (GGR) in Nigeria from 1999 to 2022. Utilizing a Generalized Linear Model (GLM) approach, the research explores how some bank liquidity metrics, specifically the liquidity ratio (LR), loan-to-deposit ratio (LTD), cash reserve ratio (CRR), interest rate (INT), and inflation rate (INF) influence economic growth. The findings indicate that liquidity management significantly impact economic growth, with LR, LTD, and CRR having negative impact on GGR. In contrast, INT showed a positive impact, highlighting the importance of lower interest rates in stimulating economic activity in Nigeria. The study also revealed that, when all independent variables are held constant, the GGR is expected to be approximately 4.65%. The study concludes that effective liquidity management and appropriate monetary policies are crucial in fostering economic growth in Nigeria. Recommendations include optimizing liquidity management practices, reviewing cash reserve requirements and promoting lower interest rates. This research contributes to the understanding of liquidity dynamics in developing economies like Nigeria.

Corporate Governance Characteristics and Non-Financial Performance of Listed Financial Institutions in Nigeria

Okwy Peter Okpala — 2025-05-12

This study explores the relationship between corporate governance and the non-financial performance of listed financial institutions in Nigeria. It specifically investigates how corporate governance practices in banks and insurance companies listed on Nigeria’s exchange group influence their non-financial performance, using historical financial data from 2019 to 2023. The effects of board size, board diversity, audit committee size and frequency of board meetings on growth in customers deposit, complaints resolution rate and growth in training and development of listed financial institutions in Nigeria were examined. The study collected data from the annual reports of 30 financial institutions comprising 13 deposit money banks and 17 insurance companies. The study employed a general least squares (GLS) multiple regression analysis to analyse the panel data. The findings reveal no significant correlation between corporate governance and non-financial performance, suggesting that other factors—such as the quality of board interactions or the effectiveness of information-sharing processes—may play a more critical role in driving non-financial performance than the frequency board meetings, board size, audit committee and board diversity. The study recommends that Nigerian financial institutions should continue efforts to improve board diversity, particularly by increasing the representation of women and other underrepresented groups.

Credit Decision Making process in FinTech Services in Nigeria: An Application of Logistic Regression Credit Scoring

Lukman Abolaji Ajijola — 2025-05-12

A Robust Enterprise Risk Management (ERM) framework is very critical for FinTech sustainability and continuity as it helps to manage potential losses from lending activities. To this end, the objective of this study is to identify the factors influencing credit risk and to examine credit scoring process for credit risk decision making in FinTech companies in Nigeria. Logistic regression-based methodology was employed to improve and optimized the traditional approach of credit risk decision making. The study utilizes secondary data from a FinTech company over three-years, focusing on both corporate and individual clients who have closed loans with varying tenors. Python software was used to process and analyzed the retrieved data and the key predictors impacting loan repayment behaviour are identified. Loan amount, frequency of repayment and number of dependents are strong predictors while marital status, net pay after statutory deductions, disbursement turn-around-time (TAT) and years in service show moderate predictive strength. This study contributes to the literature by demonstrating the effectiveness of logistic regression in improving credit risk assessment models for FinTech companies in Nigeria. The findings emphasize the need for FinTech companies to integrate logistic regression models into credit scoring systems to enhance risk assessment accuracy and business value creation.