Predictive Analytics Applications for Risk Mitigation across Industries; A review
Keywords:
Supply chain management, demand forecasting, customer segmentation, personalized marketing, sales forecasting, customer lifetime value, churn prediction, market trend analysis, sentiment analysis, operational efficiency, decision-making, data-driven strategies.Abstract
A potent subset of data analytics called predictive analytics is revolutionizing a number of industries by using historical data, machine learning methods, and statistical algorithms to predict future events and guide strategic choices. The uses and advantages of predictive analytics in the fields of finance, healthcare, manufacturing, energy and utilities, retail, and marketing are highlighted in this thorough overview. Predictive models improve market risk management, fraud detection, and credit risk assessment in the financial sector, promoting stability and confidence. Applications in healthcare include operational efficiency, tailored treatment, and patient risk assessment, all of which improve patient outcomes. Supply chain risk management, quality assurance, and predictive maintenance all help manufacturers maximize efficiency and reduce downtime. Demand forecasting, asset performance management, and regulatory compliance all help the energy and utilities sector by guaranteeing dependable and effective service delivery. Predictive analytics helps retailers satisfy customer requests and keep a competitive edge by assisting with inventory management, customer satisfaction, and competitive analysis. Customer segmentation, personalized marketing, campaign optimization, sales forecasting, churn prediction, customer lifetime value prediction, market trend analysis, and sentiment analysis all greatly improve marketing techniques. All things considered, predictive analytics helps businesses to foresee possible hazards, allocate resources optimally, and take proactive steps that lead to better decision-making and increased corporate performance. Predictive analytics' capabilities will develop as technology advances, securing its place as a vital instrument for contemporary businesses that spurs productivity, creativity, and expansion.
References
Akinosho, T. D., Oyedele, L. O., Bilal, M., Ajayi, A. O., Delgado, M. D., Akinade, O. O., & Ahmed, A. A. (2020). Deep learning in the construction industry: A review of present status and future innovations. Journal of Building Engineering, 32, 101827. https://doi.org/10. 1016/j.jobe.2020.101827
Akter, S., Michael, K., Uddin, M. R., McCarthy, G., & Rahman, M. (2022). Transforming business using digital innovations: The application of AI, blockchain, cloud and data analytics. Annals of Operations Research, 308(1–2), 7–39. https://doi.org/10.1007/ s10479-020-03620-w
Ahmed, N., & Abraham, A. (2015). Modeling cloud computing risk assessment using ensemble methods [Paper presentation]. 4th World Congress on Information and Communication Technologies, WICT 2014.
Amoako, R., Buaba, J., & Brickey, A. (2020). Identifying risk factors from MSHA accidents and injury data using logistic regression. Mining, Metallurgy and Exploration, 38(1), 1–19.
Araz, O. M., Choi, T. M., Olson, D. L., & Salman, F. S. (2020). Role of analytics for operational risk management in the era of big data. Decision Sciences, 51(6), 1320–1346. https://doi.org/10.1111/deci.12451 Aria, M., & Cuccurullo, C. (2017).
bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. Https://doi.org/10.1016/j. joi.2017.08.007
Arumugam, S., Gupta, S., Patra, B., Rajan, S., & Agarwal, S. (2016). Revealing patterns within the drilling reports using text mining techniques for efficient knowledge management [Paper presentation]. 2016 SPE Eastern Regional Meeting, ERM 2016. https://doi.org/10.2118/ 184062
Ashley, M. (2020). Risk heat maps have failed us … now what? https://www.linkedin.com/pulse/risk-heat-mapshave-failed-us-now-what-mike-ashley/ Australian Business Deans Council. (2019). 2019 Australian Business Deans Council (ABDC) journal quality list.
Aven, T. (2016). Risk assessment and risk management: Review of recent advances on their foundation. European Journal of Operational Research, 253(1), 1–13. https://doi.org/10.1016/j.ejor.2015.12.023
Aven, T., & Flage, R. (2020). Foundational challenges for advancing the field and discipline of risk analysis. Risk Analysis: An Official Publication of the Society for Risk Analysis, 40(S1), 2128–2136.
Azar, A., & Mostafaee Dolatabad, K. (2019). A method for modelling operational risk with fuzzy cognitive maps and Bayesian belief networks. Expert Systems with Applications, 115, 607–617. https://doi.org/10. 1016/j.eswa.2018.08.043
Aziz, S., & Dowling, M. (2019). Machine learning and AI for risk management. In T. Lynn, J. G. Mooney, P. Rosati, & M. Cummins (Eds.), Disrupting finance (pp. 33–50). Palgrave Pivot.
Basel Committee on Banking Supervision. (2010). Basel III: A global regulatory framework for more resilient banks and banking systems. Bank for International Settlements.
Basel Committee on Banking Supervision. (2020). Governors and Heads of Supervision announce deferral of Basel III implementation to increase operational capacity of banks and supervisors to respond to Covid-19. Bank for International Settlements.
Bouveret, A. (2019). Estimation of losses due to cyber risk for financial institutions. Journal of Operational Risk, 14(2), 1–20. https://doi.org/10.21314/JOP.2019.224
Bromiley, P., McShane, M., Nair, A., & Rustambekov, E. (2015). Enterprise risk management: review, critique, and research directions. Long Range Planning, 48(4), 265–276. https://doi.org/10.1016/j.lrp.2014.07.005
Bzdok, D., Altman, N., & Krzywinski, M. (2018). Statistics versus machine learning. Nature Methods, 15(4), 233–234
. Cao, L., Dong, X., & Zheng, Z. (2016). E-NSP: Efficient negative sequential pattern mining. Artificial Intelligence, 235, 156–182. https://doi.org/10.1016/j. artint.2016.03.001
Caron, F., Vanthienen, J., & Baesens, B. (2012). Rulebased business process mining: Applications for management [Paper presentation]. 1st International Symposium on Management Intelligent Systems, Salamanca. IS-MiS 2012.
Caron, F., Vanthienen, J., & Baesens, B. (2013). A comprehensive investigation of the applicability of process mining techniques for enterprise risk management. Computers in Industry, 64(4), 464–475. https://doi.org/ 10.1016/j.compind.2013.02.001
Centers for Disease Control and Prevention. (2017). Historical mine disasters. https://www.cdc.gov/niosh/ mining/statistics/minedisasters.html
Chavez-Demoulin, V., Embrechts, P., & Hofert, M. (2016). An extreme value approach for modeling operational risk losses depending on covariates. Journal of Risk and Insurance, 83(3), 735–776. https://doi.org/10. 1111/jori.12059
Choi, T.-M., Chan, H. K., & Yue, X. (2017). Recent development in big data analytics for business operations and risk management. IEEE Transactions on Cybernetics, 47(1), 81–92.
Choi, T.-M., & Lambert, J. H. (2017). Advances in risk analysis with big data. Risk Analysis: An Official Publication of the Society for Risk Analysis, 37(8), 1435–1442.
Chollet, F. (2018). Deep learning with Python. Manning Publications Co. Chu, C. Y., Park, K., & Kremer, G. E. (2020). A global supply chain risk management framework: An application of text-mining to identify region-specific supply chain risks. Advanced Engineering Informatics, 45, 101053. https://doi.org/10.1016/j.aei.2020.101053
COSO. (2017). Enterprise risk management – integrating with strategy and performance. Committee of Sponsoring Organizations of the Treadway Commission.
COSO. (2020). Guidance on enterprise risk management. Committee of Sponsoring Organizations of the Treadway Commission. Retrieved 23 March from https://www.coso.org/Pages/erm.aspx
Middleton, S. E., & Sabeur, Z. A. (2011). Knowledge-based service architecture for multi-risk environmental decision support applications. Environmental Software Systems: Frameworks of Eenvironment. Milana, D., Darena, M. S., Bettio, N., Cerruti, C., Siliprandi, G., Fidanzi, A., Cerioli, P., Silvestri, G., Tarasconi, F., Caserio, M., Botros, M., & Gabrielli, M. L. (2019).
Natural language understanding for safety and risk management in oil and gas plants [Paper presentation]. Abu Dhabi International Petroleum Exhibition and Conference 2019, ADIP 2019. https://doi.org/10.2118/ 197668-MS
Milkau, U., & Bott, J. (2018). Active management of operational risk in the regimes of the “Unknown”: What can machine learning or heuristics deliver? Risks, 6(2), 41.
Misic, V. V., & Perakis, G. (2020). Data analytics in operations management: A review. Manufacturing & Service Operations Management, 22(1), 158–169. https://doi. org/10.1287/msom.2019.0805
Mittnik, S., & Starobinskaya, I. (2010). Modeling Dependencies in Operational Risk with Hybrid Bayesian Networks. Methodology and Computing in Applied Probability, 12(3), 379–390. https://doi.org/10. 1007/s11009-007-9066-y
Monish, H., & Pandey, A. C. (2020). A comparative assessment of data mining algorithms to predict fraudulent firms [Paper presentation]. 10th International Conference on Cloud Computing, Data Science and Engineering, Confluence 2020.
Moura, R., Beer, M., Patelli, E., Lewis, J., & Knoll, F. (2017). Learning from accidents: Interactions between human factors, technology and organisations as a central element to validate risk studies. Safety Science, 99, 196–214. https://doi.org/10.1016/j.ssci.2017.05.001
Nagarajan, R., Scutari, M., & Lebre, S. (2013). Bayesian networks in R with applications in systems biology (1st ed.). Springer. Nagashree, N., Tejasvi, R., & Swathi, K. C. (2018). An early risk detection and management system for the cloud with log parser. Computers in Industry, 97, 24–33.
Nateghi, R., & Aven, T. (2021). Risk analysis in the age of big data: The promises and pitfalls. Risk Analysis: An Official Publication of the Society for Risk Analysis, 41(10), 1751–1758.
Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50(3), 559–569. https://doi.org/10.1016/j.dss.2010. 08.006
Nugent, T., & Leidner, J. L. (2017). Risk mining: Companyrisk identification from unstructured sources [Paper presentation]. 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016.
Nwafor, C. N., Nwafor, O. Z., & Onalo, C. (2019). The use of business intelligence and predictive analytics in detecting and managing occupational fraud in Nigerian banks. Journal of Operational Risk, 14(3), 95–120.
Ocelewicz, L., Lewis, J., Steele, C. (2021). Conduct risk: Delivering an effective framework. https://home.kpmg/ uk/en/home/insights/2017/09/conduct-risk-delivering-aneffective-framework.html
Operational Riskdata eXchange Association. (2020). Annual banking loss report. Operational Riskdata eXchange Association.
O’Shea, N., Pence, J., Mohaghegh, Z., & Kee, E. (2015). Physics of failure, predictive modeling & data analytics for LOCA frequency [Paper presentation].
61st Annual Reliability and Maintainability Symposium, RAMS 2015.
Palshikar, G. K., & Apte, M. (2013). Financial security against money laundering: A survey. In B. Akhgar & H. Arabnia (Eds.), Emerging trends in ICT security (pp. 577–590).
Pence, J., Farshadmanesh, P., Kim, J., Blake, C., & Mohaghegh, Z. (2020). Data-theoretic approach for socio-technical risk analysis: Text mining licensee event reports of U.S. nuclear power plants. Safety Science, 124, 104574. https://doi.org/10.1016/j.ssci.2019.104574
Pence, J., Mohaghegh, Z., Ostroff, C., Kee, E., Yilmaz, F., Grantom, R., & Johnson, D. (2014). Toward monitoring organizational safety indicators by integrating probabilistic risk assessment, socio-technical systems theory, and big data analytics [Paper presentation]. 12th International Probabilistic Safety Assessment and Management Conference, PSAM 2014.
Pence, J., Sakurahara, T., Zhu, X. F., Mohaghegh, Z., Ertem, M., Ostroff, C., & Kee, E. (2019). Data-theoretic methodology and computational platform to quantify organizational factors in socio-technical risk analysis. Reliability Engineering & System Safety, 185, 240–260. https://doi.org/10.1016/j.ress.2018.12.020
Persona, A., Battini, D., Faccio, M., Bevilacqua, M., & Ciarapica, F. E. (2006). Classification of occupational injury cases using the regression tree approach. International Journal of Reliability, Quality and Safety Engineering, 13(02), 171–191. https://doi.org/10.1142/ S0218539306002197