Augmenting Human Minds: Artificial Intelligence and Big Data in Financial Risk Assessment
- October 5, 2021
- Posted by: RSIS
- Category: IJRISS
International Journal of Research and Innovation in Social Science (IJRISS) | Volume V, Issue IX, September 2021 | ISSN 2454–6186
University of South Africa-PhD Scholar
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.
Keywords: Artificial Intelligence, Big Data, Machine Learning, Risk Management, Risk Assessment, Financial Risk Assessment
Artificial Intelligence (AI) has taken the center stage of most operations in the financial world. The advancement of technology has contributed to the use of AI changing the structures of financial operations that include the use of machine learning, natural language processing, and business automation (Cerchiello & Giudici, 2016). The computing power has played a crucial role in integrating AI in the era of big data. AI is the process of using smart machines that can perform tasks that require human intelligence. This process involves computer systems that are capable of recognizing speech, have machine vision, and can process natural language. Most financial services have been designed to utilize AI to ensure there is increased accuracy, precision, and short task turnaround (Cerchiello & Giudici, 2016) s. AI systems in finance work through the use of big data which are fed into the computers to be processed into meaningful output.