Introduction of AI and Machine Learning in Banking Sector

Banking CIO Outlook | Friday, November 30, 2018

As the amount of data increases, it has become a mammoth task to process the structured and unstructured data manually. Against this backdrop, Artificial Intelligence (AI) and machine learning have emerged as one of the major contenders to solve the data processing dilemma. The banking industry has understood the potential and efficiency of using these technologies to reduce the operational cost of analyzing each terabyte of data while increasing the accuracy of insights.

Banks can leverage machine learning to analyze and learn the patterns from data to improve services. Then AI’s simulated intelligence can be applied to these insights toward solving complex financial problems and improving the success rate of each financial operation. Moreover, AI is fast and precise in its identification capabilities; it can process data, solve equations, and identify discrepancies or a particular trade data within seconds.

As a matter of fact, AI is constantly being used to secure financial data from breaches and malware by leveraging intelligent authentication systems to prevent misuse of information. Furthermore, AI will force banks to remove the inefficiency of manual services and only hire the extremely skilled employees for a job to maintain the IT infrastructure.

Banks are also utilizing machine learning and AI to identify the income generated through illegal actions and fraud in line with the anti-money laundering regulations. Among the myriad of applications, the banking sector can also leverage AI technology to create chatbots that simulate an intelligent and human-like response to improve customer experience as well as reduce expenditure on customer support.

AI’s benefits in the banking sector include a substantial reduction in the time taken for loan and credit approval process by assessing risk and researching the credibility of the banking customer in real-time. Integrating an AI-powered recommendation engine within a bank’s technological infrastructure greatly improves the accuracy of recommendation.

Recently, some banks have introduced smart systems that leverage AI and machine learning to research investment potentials and risks, market conditions, run predictive analysis, and create trade forecasts to help customers make an informed investment decision.

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