The Role of Data Analytics in Strengthening Financial Risk Assessment and Strategic Decision-Making

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@article{1704359,
      author = {Oluwafunmike O. Elumilade, Ibidapo Abiodun Ogundeji, Godwin Ozoemenam Achumie, Hope Ehiaghe Omokhoa, Bamidele Michael Omowole},
      title = {The Role of Data Analytics in Strengthening Financial Risk Assessment and Strategic Decision-Making},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {10},
      pages = {1051-1063},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704359.pdf},
      abstract = {The increasing complexity of financial markets and regulatory environments has amplified the need for robust risk assessment and strategic decision-making. Data analytics has emerged as a transformative tool, enabling financial institutions to enhance risk management processes and optimize decision-making frameworks. By leveraging big data, machine learning, and predictive modeling, organizations can identify, assess, and mitigate financial risks with greater accuracy and efficiency, This review explores the role of data analytics in strengthening financial risk assessment and strategic decision-making. It examines how advanced analytics techniques, including descriptive, predictive, and prescriptive analytics, improve risk identification, fraud detection, and regulatory compliance. Real-time data processing and algorithm-driven risk assessment models enable financial professionals to anticipate market fluctuations, assess creditworthiness, and optimize investment portfolios. Additionally, scenario analysis and stress testing, powered by artificial intelligence, contribute to proactive risk mitigation and financial stability. Beyond risk assessment, data analytics enhances strategic decision-making by providing deeper insights into financial performance, market trends, and investment opportunities. Organizations can develop data-driven strategies for capital allocation, pricing models, and portfolio diversification, leading to improved financial efficiency. However, the implementation of data analytics in finance also presents challenges, including data privacy concerns, integration with legacy systems, and the need for specialized expertise. This highlights emerging trends such as the adoption of blockchain for secure transactions, AI-driven financial forecasting, and regulatory advancements in data governance. By embracing advanced analytics, financial institutions can strengthen risk resilience, enhance compliance, and drive informed decision-making in an increasingly volatile financial landscape. The findings underscore the growing importance of data-driven approaches in modern finance, positioning analytics as a cornerstone of sustainable risk management and strategic growth.},
      keywords = {Data analytics, financial risk, Assessment, Strategic decision-making},
      month = {April},
  }