Browsing by Author "Mosiane , Boipelo"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Challenges of big data usage for risk management in a South African Bank
    (University of the Witwatersrand, Johannesburg, 2023) Mosiane , Boipelo; Ochara, Nixon
    Risk management is a critical component in the effective operation of corporations, particularly for the purpose of identifying, assessing, and mitigating potential risks associated with running a business. In recent years, the exponential growth of data, along with technological advancements, has opened new opportunities for organizations to enhance their risk management capabilities. Big data is said to be a game-changer that has the potential to completely alter how different industries conduct business. However, there are several difficulties in effectively employing big data in risk management processes. Using a qualitative research approach, this research report highlights the usage of big data in risk management, emphasizing the potential benefits, challenges, and critical considerations for successful adoption. The research findings revealed that big data can enhance risk identification accuracy, proactive risk mitigation, strategic decision-making, and overall organizational resilience. The challenges hindering the adoption of big data in risk management are addressed, including skill gaps, data quality, technology infrastructure, talent acquisition, and bureaucratic barriers. The study highlights issues preventing widespread integration of big data in the risk community, particularly data trust and collaboration barriers between risk and technology teams. The research report recommends that the bank creates a robust talent acquisition strategy for analytics experts and prioritize retaining them to safeguard data resources. It also suggests fostering a learning-friendly environment for big data topics through accessible certifications and learning programs. Additionally, the research report emphasizes the need for addressing data quality issues in risk management, proposing solutions like RPA to improve data capture processes and enhance data accuracy and trust.