A cross-sectional study on the relationship between fraud detection technologies and the profitability of commercial banks in Kampala Central Business District.

Abstract

Background

This study examined the relationship between fraud detection technologies and the profitability of commercial banks in Kampala Central Business District.

 Methods

A descriptive, correlational, cross-sectional survey design with a mixed-methods approach was employed. The target population comprised 290 employees from 12 commercial banks, including risk managers, operations managers, finance managers, internal auditors, and other staff. A sample of 165 respondents was selected using purposive sampling for managerial staff and stratified sampling for other employees. Data were collected via self-administered questionnaires, semi-structured interviews, and documentary reviews of financial reports and risk management policies. Quantitative data were analyzed using descriptive statistics and correlation/regression analysis, while qualitative data underwent thematic analysis.

 Results

The study achieved a 90.9% response rate. Respondents were predominantly male (56.7%), aged 31–40 years (60.7%), with 56% holding bachelor’s degrees. OTP verification and SMS alerts were the most widely adopted fraud detection tools (Mean = 3.20, SD = 1.02; Mean = 3.05, SD = 0.98), whereas facial/voice recognition and AI-based monitoring recorded low uptake (Mean ≤ 2.20). Profitability indicators were weak: ROA (Mean = 2.65, SD = 0.88), ROE (Mean = 2.72, SD = 0.91), net interest margin (Mean = 2.56, SD = 0.95), and dividend payout (Mean = 2.44, SD = 0.89). Correlation analysis showed a strong positive association between fraud detection technologies and profitability (r = 0.836, p = 0.001). Regression analysis confirmed fraud detection technologies as a significant predictor of profitability (β = 0.568, t = 9.42, p < 0.001), explaining 73.5% of variance (R² = 0.735).

 Conclusion

Effective fraud detection technologies are crucial for enhancing profitability in banks operating in fraud-prone environments.

 Recommendation

Commercial banks should invest in advanced fraud detection systems, integrate AI and real-time monitoring, and continuously train staff while updating algorithms to counter evolving fraud risks.