Augmenting Human Minds: Artificial Intelligence and Big Data in Financial Risk Assessment

Lilian Mboya
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Lilian Mboya: University of South Africa-PhD Scholar

International Journal of Research and Innovation in Social Science, 2021, vol. 05, issue 09, 307-311

Abstract: The study sought to explore on the role of AI and Big Data on risk assessment in financial institutions. The study adopted a systematic review of literature and secondary data sources to present a qualitative analysis of the key elements of AI and big data and their application in financial risk assessment and management. Peer reviewed journal articles were used to provide essential and relevant information on AI and big data on risk assessment. The study established that machine learning tools were used in predictive analytics and based on big data extracted from databases, the risks managers were able to use regression, classification, clustering, and anomaly detection to carry out fraud detection, portfolio optimization, volatility forecasting and sensitivity analysis. Machine learning was the basic form of AI used in risk assessment in financial institutions in conjunction with big data. Market risks are assessed through portfolio optimization, sensitivity analysis, and volatility forecasting while credit risks are assessed through credit scoring and defaulting prediction. Insurance risks are measured by claims modelling, reserve losses, mortality forecasting, and fraud detection. The study recommended that financial sector should invest in research and development for a specialized AI machines and software to meet the rising needs of cyberspace in the banking systems and mobile banking transactions.

Date: 2021
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