Predicting State-Level Financial Distress with Privacy-Preserving AI

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Author: Denis Avetisyan

A new approach leverages federated learning and explainable AI to identify financial risks across U.S. states without sharing sensitive data.

A federated learning model’s performance in predicting financial distress varies significantly across U.S. states, as evidenced by differing precision, recall, $F1$-score, and AUC values when each state operates as an independent client, revealing substantial regional heterogeneity in model effectiveness.
A federated learning model’s performance in predicting financial distress varies significantly across U.S. states, as evidenced by differing precision, recall, $F1$-score, and AUC values when each state operates as an independent client, revealing substantial regional heterogeneity in model effectiveness.

This study demonstrates the feasibility of cross-silo federated learning with SHAP values for accurate and interpretable financial distress modeling at the state level.

Predicting financial vulnerability often requires centralized data collection, creating privacy risks and regulatory hurdles. This is addressed in ‘Explainable Federated Learning for U.S. State-Level Financial Distress Modeling’, which pioneers a cross-silo federated learning framework to model financial hardship across all 50 U.S. states without sharing sensitive individual data. By integrating explainable AI techniques, this work not only predicts financial distress but also identifies key state-specific and nationwide predictors, such as debt collection contact. Could this approach unlock a new era of responsible AI in consumer finance, enabling targeted interventions and promoting financial inclusion while safeguarding data privacy?

Forecasting Vulnerability: The Signal in Silence

Identifying individuals at risk of financial hardship—signaled by debt collection contact—is crucial for timely intervention. Early detection can mitigate negative consequences. Traditional prediction methods struggle with complex data and achieving robust accuracy, often relying on limited datasets and yielding high false positive rates. Protecting consumer privacy is paramount; innovative approaches like federated learning and differential privacy are increasingly explored to balance prediction with ethical data handling.

Data Privacy Through Collaboration

Federated Learning offers a solution to data privacy challenges in financial modeling. It enables model training across distributed data sources—like banks—without central data exchange, preserving institutional control. Applying this to the National Financial Crime Survey (NFCS) Dataset improved predictive capabilities, achieving an F1-Score of 42.2%—a significant advancement over isolated datasets. Scalability is further enhanced through Cross-Silo Federated Learning, adapting the core principle for geographically dispersed data and large-scale deployments.

Refining the Predictive Engine

To improve efficiency, Partial Participation was employed, limiting each training round to a subset of clients, reducing computational costs. Client selection was randomized to ensure data distribution representativeness. Addressing class imbalance within the NFCS Dataset, Class Weighting was utilized during training, incentivizing robust representations for under-represented classes. An 8-layer Highway Network, designed to mitigate vanishing gradients, achieved an Area Under the Curve (AUC) of 71.4% in predicting financial distress.

Illuminating the Path to Intervention

The predictive model incorporates Explainable AI (XAI) to improve transparency and build trust. This allows for detailed analysis of the factors driving each prediction, moving beyond ‘black box’ approaches, crucial for responsible financial deployment. Feature Importance Analysis, utilizing SHAP and Owen Values, identified key predictors.

The average contribution of each input feature to the model’s predictions is summarized using SHAP values.
The average contribution of each input feature to the model’s predictions is summarized using SHAP values.

Variables related to debt-to-income ratio and credit utilization are consistently strong predictors. These insights facilitate proactive measures, reducing communication costs by 60% and enabling efficient, targeted support. The capacity to foresee vulnerability is not merely prediction, but a form of digital empathy.

The pursuit of predictive modeling, as demonstrated in this work on federated learning for financial distress, often obscures the underlying mechanisms at play. This study, however, prioritizes not merely prediction, but understanding – a revealing of risk factors through explainable AI techniques like SHAP values. It echoes the sentiment of Georg Wilhelm Friedrich Hegel: “We do not know truth, we only know its expressions.” The application of federated learning, preserving data privacy across states, allows for a more nuanced expression of financial vulnerability. By focusing on localized risk assessment, the research doesn’t attempt to impose a singular ‘truth’, but rather illuminates the distinct expressions of distress within each state’s financial landscape.

Where to Next?

The successful application of federated learning to financial distress prediction, as demonstrated, is not a destination but a narrowing of the problem. The architecture functioned, data remained localized – yet the underlying fragility remains exposed. Current methods, even with explainable AI like SHAP values, treat indicators as discrete levers, failing to account for the systemic interdependencies that cause distress, not merely signal it. Future work must move beyond feature importance to model causal pathways – a far more ambitious, and likely less precise, undertaking.

The promise of financial inclusion through localized risk assessment is hampered by a fundamental tension. Increased granularity demands increased data resolution, subtly eroding the very privacy protections federated learning seeks to establish. A continued focus on differential privacy, and exploring genuinely privacy-preserving synthetic data generation, is not merely desirable, but essential if this approach is to scale beyond carefully curated datasets.

Ultimately, the pursuit of explainability in complex systems feels like a Sisyphean task. Each layer of interpretation introduces further abstraction, distancing the model from the lived reality it attempts to represent. The true challenge lies not in illuminating the ‘black box,’ but in acknowledging its inherent opacity and designing systems robust enough to function despite incomplete understanding.

Original article: https://arxiv.org/pdf/2511.08588.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-11-13 09:45