Speaker
Description
Despite the advantages of ML models, their adoption in banking institutions is often limited due to regulatory restrictions. These regulations aim to ensure transparency and accountability in decision-making processes and tend to prioritize traditional models where interpretability and model stability are well established. This project studies the banking institution's existing workflow in terms of model deployment and monitoring and highlights the benefits of the usage of ML models. The objective is to study the necessary changes when transitioning from traditional models to ML models. Additionally, we study the existing approach for the analysis of the stability and predictive power of the models and propose a series of improvements on the cases where the current methodologies may have been outdated by newer advances or are no longer valid in the ML context. By shedding light on the benefits and considerations associated with incorporating ML models into the finance industry, this project contributes to the ongoing application of statistics, data analysis, and ML in the industrial sector.
| Classification | Mainly application |
|---|---|
| Keywords | monitoring; finances; ml-models |