There is technological advancement in traditional banking and gradually banks are moving towards innovative solutions such as blockchain, cloud, and quantum computing. However, many banks are still reaching the stage of their AI revolution. The human touch is still an important element. For AI, banking is no longer just physical branches, but a brand-new modern banking ecosystem. In this new digital landscape, the machine learning algorithms spectrum plays a significant role and comprises many different analytical methods. Those include regression, classification, and clustering (unsupervised machine learning). Those methods can apply to different classes of statistical problems and can solve problems through supervised machine learning.
ML Regression Problems
Regression problems in banking involve the prediction of quantitative dependent variables, such as the prediction of macroeconomic factors by banking risk management teams of the country’s GDP growth or inflation. Linear learning methods include regression problems of partial least squares and principal component analysis. On the other hand, non-linear learning methods include penalized approaches in which a factor is typically added to reduce the complexity of the model and increase its predictive performance, such as LASSO and Elastic Nets.
ML Classification Problems
Classification problems typically involve the prediction of a qualitative dependent variable, which aims to provide a structured set of “yes or no” questions of leading the team to sort through a wide range of data and therefore produce a precise prediction of a particular outcome. A tailor-made example of data classification is the leverage of sentiment classification data through social media which has opened new avenues and opportunities for banking institutions to improve the quality of their services.
Two main aspects are emerging on the added value of applying machine learning algorithms in the financial services industry. First, analytical capabilities across risk management and compliance areas (such as credit risk modeling and money laundering detection) can significantly be improved by using high granularity and depth of predictive analysis of machine learning methods. Secondly, there is high context dependency on the data quality and availability. This machine learning in banking interdependency can come at the cost of producing model complexity and causing a lack of explanatory model insights.
