This study is on enhancing risk management in financial institutions through big data analytics. It delves into the impact of big data analytics on risk management practices within financial institutions, focusing on how these advanced techniques contribute to improving both the accuracy and timeliness of risk prediction models. By employing a comprehensive survey methodology, the research collected insights from 217 industry professionals to assess the effectiveness of big data analytics in enhancing risk management. The study reveals that a substantial proportion of respondents recognize big data analytics as a powerful tool for refining the precision of risk prediction models. Among the tools assessed, Apache Hadoop and Apache Spark are highlighted as particularly effective in processing and analyzing large datasets. Despite the overall positive assessment, the study identifies several persistent challenges, including issues related to data quality, integration complexities, and the high costs of implementation. To address these challenges, the study recommends several strategies: enhancing data quality and integration processes, investing significantly in employee training and development, and implementing robust data security measures to protect sensitive information. These findings offer actionable insights for financial institutions aiming to leverage big data analytics to optimize their risk management practices. They underscore both the significant benefits of these technologies and the critical strategies required for overcoming implementation challenges and achieving successful outcomes in risk management.
Ngozi E. Okonkwo (2026) studied this question.