Effect of Financial Technology on Financial Performance of Commercial Banks in Tanzania

Abstract

Like other banking sectors across the globe, there is heightened competition in the Tanzanian banking industry, thanks to the increased adoption of Fintech tools to enhance banking operations. Most banks are increasingly adopting Fintech tools to meet the ever-changing customer needs and gain a competitive edge. However, there is no clarity on how Fintech actually transforms banking operations and financial performance. While previous literature has emphasized the positive aspects of Fintech and how the technology benefits banks and their customers, there is scarce information on its shortcomings, especially in the developing world. Such studies are profoundly insufficient in Tanzania, an issue that requires redress. This research investigated the Fintech impact on the financial performance of commercial banks in Tanzania over a five-year period (2018-2022). The primary objective was to discern the relationships between Fintech usage, asset quality, firm liquidity, firm size, and financial performance in the Tanzanian banking sector. The theoretical foundation draws upon the diffusion of technology theory, technology acceptance model, and information asymmetry theory. The research employs a fixed-effects regression model to analyze a dataset comprising 147 unbalanced panel observations from 34 licensed commercial banks. The dataset is sourced from annual financial reports, offering a comprehensive yet specific perspective on the financial landscape of Tanzanian banks. The fixed-effects model is chosen to account for individual bank-specific effects and provides a robust analytical framework to pursue the impact of Fintech, asset quality, firm liquidity, and firm size on ROA. The regression results revealed that Fintech, while showing a positive coefficient, does not achieve statistical significance, suggesting a nuanced influence on financial performance. Asset quality emerges as a significant negative predictor, reinforcing the detrimental impact of non-performing loans. Conversely, firm liquidity and firm size show significant positive coefficients, indicating a strong positive association with ROA. The overall R-squared of 0.0915 signifies that the model explains about 9.15% of the total variation in ROA. The F-statistic of 3558.75 is highly significant, supporting the overall model's statistical significance. The model coefficients reveal the varying impacts of Fintech, asset quality, firm liquidity, and firm size on ROA. The study concludes that while Fintech's direct impact on financial performance remains inconclusive, asset quality, firm liquidity, and firm size emerge as significant determinants. Policymakers should consider fostering an environment supportive of Fintech innovation, while also emphasizing effective risk management and liquidity practices. Larger institutions may benefit from economies of scale, leading to improved financial performance. Future research could delve deeper into the qualitative aspects of Fintech adoption