Evaluating Predictive Financial Analysis Techniques, Impacts, and Challenges in Nigerian Banking Institutions

This study examined the techniques, impacts, and challenges of predictive financial analysis in Nigeria Banking Institutions. The Nigerian Banking Institutions is the life wire of the nation's economy. They play a vital and massive role in the nation's economic development and financial system. The role of these institutions remains a catalyst for a nation's growth and development. Despite the importance of predictive financial analysis in improving financial performance, accurately forecasting financial outcomes, mitigating the impact of financial crises, and making informed decisions, many Nigerian banks still need help to adopt and use this technique effectively. As a result, this study aims to examine the predictive financial analysis, evaluate the techniques, impact, and challenges, and recommend possible solutions that can help address the challenges faced by the banking sector.

Methodology: The study adopted a descriptive survey research design. A simple random sampling technique was used to select 7 respondents representing five banks (Access Bank, Fidelity Bank, First Bank, Wema Bank, and Zenith Bank). A self-designed online questionnaire (SDOQ) was used for data collection. The data collected were analyzed using descriptive statistics (percentage).

Results: Banks: 2 of the respondents, representing 28.6%, work at Access Bank; 1 of the respondents, representing 14.3%, works at Fidelity Bank; 2 of the respondents representing 28.6%, works at First Bank,1 respondent representing 14.3%, works at Wema Bank and the remaining 1 representing 14.3% works at Zenith bank.

Role of Respondents: AHOP, Compliance Officer, Data Administrator, Data Analyst/Analytics, Relationship Manager and Team Lead.

Analysis techniques: 3 banks representing 42.9% rely on the time series analysis technique, 2 banks representing 28.6% rely on the Regression analysis technique and 2 banks representing 28.6% rely on the machine learning algorithms technique.

Satisfaction Level: 3 respondents representing 42.9% are very satisfied with current predictive analytics capabilities, while 4 respondents representing 57.1% are moderately satisfied with current predictive analytics capabilities.

Discussion: The study's findings revealed that the primary challenges faced by banks in implementing predictive financial analysis techniques include data quality issues, regulatory constraints, lack of expertise, and integration with existing systems. Similarly, most banks rely on time series analysis to forecast future financial trends.

Conclusion and Recommendations: Based on the findings in this study, it was concluded that the primary challenges faced by banks in implementing predictive financial analysis techniques include data quality issues, regulatory constraints, lack of expertise, integration with existing systems, and others. Similarly, most banks rely on the time series analysis technique to forecast future financial trends. The findings in this study also revealed that respondents perceive the impact of technological advancements to improve accuracy significantly. It was therefore recommended that a robust data management system practice be established to ensure the accuracy and consistency of data. Also, beyond time series analysis, banks should explore other predictive analytics techniques to enhance forecasting accuracy.