Latent Space Interpretability for Autoencoders in Financial Crime Detection
E. Rossi (TU Delft - Electrical Engineering, Mathematics and Computer Science)
N. Parolya – Mentor (TU Delft - Statistics)
A. Papapantoleon – Graduation committee member (TU Delft - Applied Probability)
E. Haasdijk – Mentor (Deloitte)
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Abstract
Financial crime is growing in scale and complexity, increasing the need for robust monitoring. Variational Autoencoders offer compact representations of transaction behavior that can support anomaly detection and related use cases in this domain, yet their adoption remains limited due to the lack of interpretability of their latent spaces.
To address this challenge, we formalize interpretability by quantifying the relationship between latent dimensions and aspects of transaction behavior, using explicitness, modularity, and compactness as complementary metrics. Based on these measures, we develop a framework and apply it in controlled experiments that vary regularization strength and latent dimensionality, in order to understand their influence on the structure and interpretability of latent spaces.
We find that weak regularization preserves detail but compromises modularity and compactness, intermediate values progressively improve these properties, until strong regularization forces the latent space to collapse. Latent dimensionality further shapes both the level of detail preserved and the conditions under which meaningful structure emerges. The results show that there is no universal optimum, but rather parameter regimes suited to different priorities. The framework offers a systematic way to align model design with these priorities, providing both empirical insights and a general tool to support the adoption of VAEs in financial crime detection.