Skip to content
Research Article Open access CC BY 4.0

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

Hameed Adesina Raji, Mustapha Gabriel Ademola

Asian Journal of Economics, Business and Accounting · pp. 55–72 · Published 30 Jul 2024

10.9734/ajeba/2024/v24i81441

Abstract

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.

Predictive financial analysis technological advancements Nigeria Banks

References (36)

  1. 1 Big data analytics and firm performance: Effects of dynamic capabilities [DOI]
  2. 2 Machine Learning in Banking Risk Management: A Literature Review [DOI]
  3. 3 Fintech: what’s old, what’s new? [DOI]
  4. 4 YAFS: A Simulator for IoT Scenarios in Fog Computing [DOI]
  5. 5 Predictive analytics: The power to predict who will click, buy, lie, or die [DOI]
  6. 6 Disruptions and Digital Banking Trends
  7. 7 Organizational capabilities that enable big data and predictive analytics diffusion and organizational performance [DOI]
  8. 8 Investigating customer churn in banking: a machine learning approach and visualization app for data science and management [DOI]
  9. 9 Big Data Analytics for Improved Risk Management and Customer Segregation in Banking Applications [DOI]
  10. 10 The Effect of Business Intelligence on Bank Operational Efficiency and Perceptions of Profitability [DOI]
  11. 11 Advancements in predictive analytics: A philosophical and practical overview [DOI]
  12. 12 Predictive analytics in credit risk management for banks: A comprehensive review [DOI]
  13. 13 Harnessing Predictive Analytics for Strategic Foresight: A Comprehensive Review of Techniques and Applications in Transforming Raw Data to Actionable Insights [DOI]
  14. 14 DIGITAL TRANSFORMATION IN BANKING: A REVIEW OF NIGERIA'S JOURNEY TO ECONOMIC PROSPERITY [DOI]
  15. 15 Computational approaches and data analytics in financial services: A literature review [DOI]
  16. 16 Big data and predictive analytics in ERP systems for automating decision making process [DOI]
  17. 17 Opportunities and Challenges of Implementing Predictive Analytics for Competitive Advantage [DOI]
  18. 18 The impact of microfinance on financial inclusion in Nigeria [DOI]
  19. 19 Analysis of Impacts of Operational Risk Management Practices on Banks’ Financial Performance: Study of Selected Commercial Banks in Nigeria [DOI]
  20. 20 Innovative Approaches in Business Development Strategies Through Artificial Intelligence Technology [DOI]
  21. 21 Challenges of the Nigerian Banking Sector and the Way Forward [DOI]
  22. 22 REVIEW ON THE EVOLUTION AND IMPACT OF IOT-DRIVEN PREDICTIVE MAINTENANCE: ASSESSING ADVANCEMENTS, THEIR ROLE IN ENHANCING SYSTEM LONGEVITY, AND SUSTAINABLE OPERATIONS IN BOTH MECHANICAL AND ELECTRICAL REALMS [DOI]
  23. 23 RISK GOVERNANCE AND FINANCIAL PERFORMANCE: AN EMPIRICAL ANALYSIS [DOI]
  24. 24 A comparative study of predictive algorithms for business analytics and decision support systems: Finance as a case study [DOI]
  25. 25 Big Data Predictive and Prescriptive Analytics [DOI]
  26. 26 Empirical Research Study on the Determinants of Market Indicators for 41 Financial Institutions [DOI]
  27. 27 Effects of Corporate Governance on Financial Performance of Commercial Banks in Nigeria [DOI]
  28. 28 Predictive analytics for loan default in banking sector using machine learning techniques [DOI]
  29. 29 Towards a unified ontology for IoT fabric with SDDC [DOI]
  30. 30 Artificial Intelligence: the Application of Machine Learning and Predictive Analytics in Credit Risk [DOI]
  31. 31 Credit Risk Evaluation in Banking and Lending Sectors Using Neural Network Model [DOI]
  32. 32 How Do Business Analytics and Business Intelligence Contribute to Improving Care Efficiency? [DOI]
  33. 33 IMPLEMENTATION OF PRUDENTIAL PRINCIPLES AND RISK MANAGEMENT IN SHARIA BANK FINANCIAL MANAGEMENT (CASE STUDY OF INDONESIAN SHARIA BANK) [DOI]
  34. 34 Does Financial Deepening Cause Liquidity Problems for Banks? Evidence from Nigeria [DOI]
  35. 35 Effect of Risks Management on the Financial Performance of Listed Deposit Money Banks in Nigeria [DOI]
  36. 36 Formulating A Strategic Plan Based On Statistical Analyses And Applications For Financial Companies Through A Real-World Use Case [DOI]

Cited by 6

Predictive analytics in accounting: Using regression models to forecast financial outcomes

I. Elbatal, Ehab M. Almetwally, Mohammad Zaid Alaskar · Alexandria Engineering Journal · 2025

Predictive Analytics and Strategic Banking Performance: The Critical Impact of Organizational Readiness

Bekti Utomo, Nurul Jannatul Wahidah, Tri Wisudawati · Novatio : Journal of Management Technology and Innovation · 2025

Showing 2 of 6 known citations — external sources report more than can currently be individually listed.

Related research

Article metrics

Real usage data collected on this platform.

54

Page views

0

PDF downloads

4

Outbound clicks

6

Citations

Views over time

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

Traffic sources

Referring site, by host.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.