Based on Kalogiannidis et al. (2024)
1. How does NLP improve the efficiency and accuracy of risk assessment processes? Natural Language Processing (NLP) fundamentally shifts risk assessment from a manual, labor-intensive process to an automated one capable of handling vast datasets. Kalogiannidis et al. (2024) highlight that over 80% of enterprise data is unstructured (e.g., text reports, social media), which traditional quantitative methods often struggle to process. By automating the analysis of this unstructured data, NLP significantly speeds up risk identification, finding supported by 70.2% of technology specialists. Furthermore, NLP reduces the human bias and error inherent in manual qualitative assessments, with 79.2% of respondents agreeing it improves identification accuracy.
2. In what ways can AI-powered data analytics enhance risk prediction and support business continuity? AI-powered analytics enables a transition from reactive to proactive risk management. Unlike traditional methods that rely on historical data and static risk factors, AI analytics can detect subtle patterns and anomalies in real-time streams. The study found that 71.5% of respondents agreed AI enhances the accuracy of predicting potential risks, rather than just reporting on past ones. Crucially, for business continuity, these tools allow for the rapid identification of “emerging risks” that have not yet materialised, with 93.5% of professionals noting that it supports a proactive approach.
3. Why is it important for businesses to integrate multiple AI technologies, beyond just NLP? While NLP is effective for efficiency, the study’s regression analysis indicates its direct impact on business continuity is only moderate compared to other technologies. In contrast, the integration of AI into Incident Response Planning demonstrated the highest statistical impact on minimising business disruption (coefficient of 0.361). Therefore, a “comprehensive strategy” is required: NLP for data processing, predictive analytics for identifying emerging threats, and AI-driven incident response to enhance resilience during crises. Relying solely on one tool leaves gaps in the Risk Management Process.
References
- Kalogiannidis, S., Kalfas, D., Papaevangelou, O., Giannarakis, G. and Chatzitheodoridis, F. (2024) ‘The Role of Artificial Intelligence Technology in Predictive Risk Assessment for Business Continuity: A Case Study of Greece’, Risks, 12(2), p. 19. Available at: https://doi.org/10.3390/risks12020019
Nikunj Bhalotia 