Assessing the Effectiveness of Fraud Management Strategies in a Kenyan Financial Institution: A Case Study of Equity Bank Kenya Limited

Equity Bank Kenya Limited, a key player in the Kenyan banking industry, has implemented various strategies to detect, prevent, and respond to fraud. Despite these measures, the recurrence of fraud cases necessitates a thorough evaluation of the bank's fraud management framework. This study sought to address this gap by examining the fraud detection, prevention, and response mechanisms employed by Equity Bank and exploring potential advancements to strengthen its fraud management strategies. The objectives of the study were to evaluate the fraud detection strategies used by the bank, analyze its fraud prevention measures, assess the effectiveness of its fraud response mechanisms, and identify possible advancements to enhance its overall fraud management framework. Guided by the Fraud Triangle Theory, developed by Donald Cressey in 1953, and Routine Activity Theory, developed by Cohen & Felson in 1979, the study adopted a mixed-methods approach, incorporating both quantitative and qualitative methodologies. A descriptive research design was used to systematically assess the bank’s fraud management strategies and data was collected through structured questionnaires and semi-structured interviews with staff from key departments, including Internal Audit, Compliance, Risk Management, and IT Security. A stratified sample of 333 respondents and a purposive sample of 10 interviewees provided insights into the strengths and gaps within existing fraud mitigation practices. The Krejcie and Morgan formula was used to determine the sample size, with stratified sampling ensuring adequate representation across departments. Content validity was used to assess the accuracy of research instruments, while Cronbach’s alpha measured their reliability. Descriptive statistics were used to analyze quantitative data, while thematic analysis was applied to qualitative responses. The findings revealed that, while whistleblowing mechanisms exist, their effectiveness is undermined by employee reluctance due to fear of retaliation, with 22.2% and 6.6% of respondents expressing uncertainty about whistleblower protection. AI-driven fraud detection systems have contributed to a 33% reduction in fraud-related losses, yet concerns persist regarding false-positive rates, with majority convinced that AI and ML are ineffective in fraud detection at Equity Bank. Fraud prevention measures, including staff training and internal controls, are in place; however, 54.4% of employees believe that fraud prevention training is inadequate. While 72.4% of respondents agreed that fraud containment is effective, key challenges include delays in fraud case resolution and unsatisfactory financial loss recovery rates, with 45.9% strongly disagreeing that recovery rates are adequate. To enhance fraud management, the study recommends strengthening whistleblower protection, refining AI-based fraud detection models, expanding training programs, and integrating blockchain technology for secure transactions. Additionally, predictive analytics and machine learning models are proposed as part of technological enhancements that Equity Bank should leverage to enhance its fraud management mechanism.