Assessing Gaussian Mixture Model applicability in risk analysis and estimation

Machine learning (ML) has transformed the model development lifecycle, revolutionizing traditional decision-making processes, in recent years. ML models enable systems to learn from data, generate new data, identify hidden patterns, make intelligent predictions and forecasts. Despite unparalleled advantages, most, if not all, ML models are black boxes. They pose major challenges in terms of transparency, interpretability, and regulatory compliance. These models often rely on complex non-linear functions and operate in high-dimensional spaces making it difficult for banks to interpret and justify the model results. Consequently, banks repeatedly seek simple time-tested models in their decision-making and risk management processes.

The Gaussian Mixture Model (GMM) is a straightforward and historically established ML model that has existed for several years. It is employed across industries in a range of applications such as clustering/classification, anomaly detection, density estimation, and image/speech recognition.

In simple terms, GMM is a probabilistic mixture model that can be expressed as a weighted sum of two or more Gaussian distributions (components), wherein each component is a multivariate normal distribution defined by its own set of parameters.

We evaluate its applicability in areas such as risk analysis and estimation.

First, we outline a few model-agnostic applications where GMM can be effectively implemented. Next, we delve into GMM and its application as a generative model. Finally, we investigate specific risk models where, we believe, GMM has significant potential as a robust alternative methodology.