Dynamic Bayesian Networks for Early-Warning Signals of Systemic Risk in FinTech Ecosystems

FinTech ecosystems increasingly exhibit systemic fragility due to tight coupling among platforms, partner banks, payment rails, cloud infrastructure, and user behavior. This study proposes a Dynamic Bayesian Network (DBN) early-warning framework that models systemic risk as a latent temporal regime inferred from multi-channel evidence, including operational reliability (transaction failures, settlement delay), liquidity and funding conditions (buffer drawdowns, spreads), infrastructure incidents, and network exposure proxies.  Backtesting under strict time-ordered evaluation demonstrates that the DBN yields decision-grade probabilistic monitoring performance and operationally usable alert behavior. The model achieves ROC-AUC = 0.873 and PR-AUC = 0.417, indicating strong discrimination under rare-event conditions. Probability quality remains stable with a Brier Score = 0.094, supporting threshold-based governance without overconfident miscalibration. In early-warning utility, the system provides an average lead time of 9.6 days before labeled stress episodes, while maintaining a manageable operational burden of 1.7 false alarms per 30 days. Alert behavior is consistent over time, reflected by an Alert Stability Index = 0.931, and stress episode coverage remains high with recall = 0.842. Temporal inspection of the EWS shows sustained elevation during stress windows rather than short-lived spikes, with posterior risk increasing most sharply when operational degradation co-occurs with liquidity/funding pressure—consistent with systemic coupling mechanisms rather than isolated incidents. Overall, the results indicate that DBNs provide a unified, explainable, and deployable mechanism for systemic-risk early warning in FinTech ecosystems, enabling probabilistic escalation policies, evidence-based attribution, and scenario-driven fragility mapping.