Data Analytics and Risk Management in U.S. Community Banking: Strengthening Financial Stability Through Data-Driven Decision Making

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@article{1720343,
      author = {Abdoulie K Darboe},
      title = {Data Analytics and Risk Management in U.S. Community Banking: Strengthening Financial Stability Through Data-Driven Decision Making},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {2},
      pages = {348-367},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1720343.pdf},
      abstract = {The U.S. banking sector, especially community banks, faces growing risks from changing regulations, advanced cyber threats, and competition from big banks and fintech companies. Defined here as institutions with assets under $10 billion, community banks play a crucial role in local economies by offering personalized services to small businesses, farms, and consumers in underserved markets. However, many of these banks still lack the technological systems and data analysis skills necessary to thrive in a more data-driven financial world. Although data analytics has proven benefits in enterprise risk management, many U.S. community banks still depend on traditional, reactive methods that overlook the predictive potential of modern tools. This disconnect between technology availability and actual use introduces vulnerabilities in areas like credit risk assessment, fraud detection, regulatory compliance, and operational efficiency. Such gaps are critical because enterprise risk management significantly influences a financial institution's competitive edge and decision-making, even though its impact on overall performance is primarily reflected in the quality of those decisions (Tewu et al., 2024). This research explores how data analytics can enhance risk management practices in U.S. community banks, thereby supporting financial stability and operational resilience. It introduces an original, data-driven risk management framework designed specifically for community banking, considering the unique challenges and opportunities faced by smaller institutions. Using an applied qualitative approach, the study combines evidence from peer-reviewed academic journals, government publications, banking regulations, and industry reports. This synthesis involves a systematic literature review and a thematic analysis of regulatory frameworks from the Federal Reserve System, FDIC, and Basel Committee on Banking Supervision. Evidence indicates that integrating data analytics into governance structures significantly enhances banking performance. Financial innovation consistently boosts long-term bank performance, especially in concentrated markets where effective risk management is crucial (Sadraoui, 2025). Additionally, advanced predictive analytics now deliver remarkable results, with fraud detection accuracy reaching up to 98.5 percent, while efficiently managing high data volumes with minimal delays (Sathupadi et al., 2025). Based on these findings, community banks should invest strategically in scalable data analytics capability, build robust data governance frameworks, strengthen workforce training in analytical skills, harden their cybersecurity infrastructure, and pursue partnerships with technology providers to accelerate their digital transformation.},
      keywords = {Data Analytics, Enterprise Risk Management, Community Banking, Predictive Analytics, Credit Risk, Fraud Detection, Regulatory Compliance, Cybersecurity Risk, Financial Stability, Machine Learning.},
      month = {August},
      doi = {https://doi.org/10.64388/IREV10I2-1720343}
  }