Assessing the effectiveness of artificial intelligence in financial analysis At Stanbic Bank Uganda

Abstract

The purpose of the study was to explore the effectiveness of AI as used by Stanbic Bank in financial analysis. The study was guided by three objectives including to evaluate the effectiveness of AI in financial analysis in Stanbic Bank, to establish the factors that affect effectiveness of IA for financial analysis in Stanbic Bank and to suggest a suggest measures to improve the effectiveness of AI in financial analysis at Stanbic Bank. This study used exploratory research design to gain insights into the effectiveness of AI in financial analysis at Stanbic Bank was employed to collect primary data using self-administered questionnaires. Data was gathered using a structured questionnaire from 25 staff members. Interviews were also used to collect qualitative data from key informants. Quantitative data was analysed using SPSS while qualitative data was reported verbatim. On the effectiveness of AI, it was found out that AI has helped to enhance effectiveness in financial analysis by way of improving the efficiency and accuracy of financial analysis. The factors that were found to affect the effectiveness of AI in financial analysis included adequate training on how to use AI in financial analysis which reflects the bank's commitment to ensuring its employees are well-equipped with the necessary skills though aspects like poor data quality, lack of necessary expertise and the tight bank policies affect the use of AI in financial analysis. On the measures, it was found out that almost all except a few are very familiar with the current use of AI in financial Analysis in the bank, however, in its use, they encounter various challenges which can be managed through interventions like rigorous training of staff and regular systems up date of the systems to ensure that they move with the technological trends in the industry. It was concluded that the use of AI in financial analysis has significantly contributed to the overall performance of the bank. The study recommended that management should encourage collaboration between AI systems and human analysts to leverage the strengths of both as this can lead to a more comprehensive and accurate analysis. The Bank should invest in robust data quality assurance processes and tools as this will help address the challenge of poor data quality which is a significant factor affecting the effectiveness of AI in financial analysis.