Advanced computational forecasting techniques to strengthen risk prediction, pattern recognition, and compliance strategies

Nafisat Temilade Popoola 1, * and Felix Adebayo Bakare 2

1 Applied Statistics and Decision Analytics, Western Illinois University, USA.
2 Haslam College of Business, University of Tennessee, USA.
 
Review
International Journal of Science and Research Archive, 2024, 12(02), 3033-3054.
Article DOI: 10.30574/ijsra.2024.12.2.1412
Publication history: 
Received on 23 June 2024; revised on 22 August 2024; accepted on 26 August 2024
 
Abstract: 
In an era defined by data-driven decision-making, advanced computational forecasting techniques have emerged as powerful tools for strengthening risk prediction, pattern recognition, and compliance strategies. These techniques leverage artificial intelligence (AI), machine learning (ML), and big data analytics to enhance accuracy, efficiency, and reliability in risk assessment across diverse industries. Traditional risk prediction models often rely on historical data and statistical methods, which, while effective, struggle to capture complex, non-linear patterns in evolving datasets. Advanced computational techniques, such as deep learning, ensemble learning, and reinforcement learning, have significantly improved predictive capabilities by identifying intricate correlations and anomalies in vast datasets. Pattern recognition plays a crucial role in cybersecurity, fraud detection, and financial risk management, where real-time anomaly detection enables organizations to preemptively mitigate threats. Predictive analytics models integrated with neural networks and natural language processing (NLP) have further revolutionized compliance strategies, ensuring adherence to regulatory frameworks and minimizing operational risks. In financial institutions, computational forecasting optimizes credit risk assessment and anti-money laundering (AML) monitoring, while in healthcare, it enhances disease outbreak predictions and patient care strategies. Despite these advancements, challenges such as algorithmic biases, data privacy concerns, and interpretability issues remain. Regulatory bodies are increasingly scrutinizing AI-driven decision systems to ensure transparency, fairness, and accountability. This study provides a comprehensive analysis of the latest computational forecasting techniques, their applications in risk management, and the evolving regulatory landscape. By addressing existing challenges and optimizing these techniques, industries can leverage AI-driven forecasting to enhance resilience, mitigate risks, and maintain regulatory compliance in an increasingly complex digital ecosystem.
 
Keywords: 
Computational forecasting; Risk prediction; Pattern recognition; Compliance strategies; Artificial intelligence; Machine learning
 
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