Artificial intelligence has been increasingly transforming the industry, the financial industry is one of them. various advanced technology has been applying in banking and financial service institution. The major segments that banking and financial service institutions apply machine learning into our decision making, tailor service and risk management (McKinsey & Company, 2019). Research shows that the use of machine learning brings more than $250 billion in revenue into the banking industry (McKinsey & Company, 2019). previously, the banking data analyst uses a vast of data to manage risk by selecting model, cluster data and analyzing data. However, it is no longer the case with the introduction of machine learning. The algorithms can do these routine jobs much more efficiently. Expert in the field of ai says that machine learning algorithms are able to outperform human in the most situation(Nawijn, 2019). However, does it mean that machine learning is perfect and human interaction in banking risk management is not necessary anymore? I would say no.
Although machine learning could solve most of the problem and have superior performance than human, there are few potential risks and flaws exist. for example, model risk. Model risk occurs when a financial model is used to measure quantitative information, wrong input or the inadequate model could lead to model risk thus results in wrong output (Investopedia, 2015). Conscious of the model risk, many banks proceed cautiously and restrict the use of machine-learning models to low-risk applications, additionally, the bank should employee supervisors or motors to regularly exam the work of machine learning and validate the results. Therefore, the combination of machine learning and traditional framework associated with risk management is crucial for banking.
Besides risk management, banking also employe machine learning to make an investment decision and manage the portfolio. The algorithm enables the bank to generate a more rational decision based on the thousands times of simulation and calculation. However, since the systematic risk is unpredictable and could not be eliminated, it is very subjective to make the decision that relates to the overall market situation. therefore, we still need human interaction in the decision-making process.
-McKinsey & Company. (2019). Derisking machine learning and artificial intelligence. Available at: https://www.mckinsey.com/business-functions/risk/our-insights/derisking-machine-learning-and-artificial-intelligence
-Nawijn, B. (2019). Managing financial risk with Machine Learning. Tjip.com. Available at: https://www.tjip.com/en/publications/managing-financial-risk-with-machine-learning .
-Investopedia. (2015). When Model Risk Occurs in Finance. Available at: https://www.investopedia.com/terms/m/modelrisk.asp.
