Concept-Based Explainable AI: Interpreting Deep Learning Models through Human-Readable Concepts in Financial Applications - Akash Vijayrao Chaudhari; Pallavi Ashokrao Charate (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025)
In high-stakes domains like finance, the interpretability of deep learning models is crucial. Concept-Based Explainable AI (XAI) has emerged as a promising approach to bridge the gap between complex neural networks and human understanding by explaining model decisions in terms of human-readable concepts rather than low-level featuresxaiworldconference.com. This paper provides a comprehensive overview of concept-based XAI techniques and their application in finance, including credit risk scoring, fraud detection, and portfolio management. We survey relevant literature – with particular emphasis on recent work by Chaudhari and colleagues – and discuss how concepts (e.g., “credit history quality” or “transaction anomaly patterns”) can be used to interpret deep models. We describe methodologies such as Testing with Concept Activation Vectors (TCAV)arxiv.org and concept bottleneck modelsproceedings.mlr.press, and propose a conceptual framework for integrating domain-specific concepts into financial deep learning models. Experiments drawing on existing studies and hypothetical simulations demonstrate that concept-based explanations can illuminate model reasoning without significantly sacrificing predictive performance. For instance, concept-enhanced fraud detection models maintain high accuracy while providing clear explanations for flagged transactions, thus improving user trust and meeting regulatory requirements for transparencyjrtcse.comarxiv.org. We present a case study with results showing improved fraud detection (F1-score 0.82 vs 0.60) when using enriched data and XAI techniquesjrtcse.com. We also include a conceptual diagram of a concept-based model pipeline and a performance comparison table. Our contribution is a detailed synthesis of concept-based XAI in finance, highlighting theoretical underpinnings, practical implementation considerations, and the potential for these methods to foster more transparent and accountable AI systems in financial services.