Artificial intelligence is growing in importance and is one of the most discussed technological topics today. The article explains and discusses two approaches and their viability for the utilization of AI in banking use cases: Deep learning and gradient boosting. While artificial intelligence and the deep learning model generate substantial media attention, gradient boosting is not as well-known to the public.
Deep learning is based on complex artificial neural networks, which process data rapidly via a layered network structure until a decision regarding a previously defined problem is made in the last layer. This enables the solution of complex problems but can lead to insufficient transparency and traceability in terms of the decision-making process, as one large decision tree is being followed. The German regulatory authority BaFin already stated that in terms of traceability no algorithms will be accepted, that is no longer comprehensible due to their complexity.
In this regard, the authors argue that gradient boosting might be a viable alternative, as this model bases its decisions on several smaller decision trees, which are easier to interpret and understand. In addition, gradient boosting requires less data than deep learning to become functional.
Statements about the dominance of either of the models are not possible, as both of them provide good results in terms of classifying high-dimensional data. However, considering regulatory requirements, such as transparency in the financial industry, gradient boosting might be a preferable approach to artificial intelligence.