Digital Exhaust as an Early-Warning System: Auditors, Fractal Signals, and Real-Time Run Risk

Sapiri (2024) emphasizes auditors’ role in risk detection but relies on periodic reporting. Ivchenko et al. (2023) show that fractal analysis (e.g., Hurst exponent) combined with neural networks can forecast bank time series, and Mubina et al. (2025) illustrate extracting structured insight from user reviews. This idea synthesizes these by building a real-time “pre-run” dashboard that ingests digital exhaust—mobile banking outage complaints, sentiment shifts in customer reviews, payment rail congestion, even VC/crypto chatter—and computes early-warning indicators (e.g., rising persistence/volatility via Hurst metrics, sentiment regime breaks). The novelty is twofold: treating consumer-tech signals as leading indicators of liquidity stress and embedding them in auditors’ continuous risk assessment pipelines, rather than siloed market surveillance. Cross-validating against episodes like SVB (Allen 2023) could demonstrate that digital precursors lead reported outflows by days. If effective, regulators and auditors could shift from post-hoc analyses to preemptive interventions, especially as app-based banking accelerates the speed of runs.

References:

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2. USE OF INTERNET TECHNOLOGIES FOR DIAGNOSIS AND FORECASTING OF BUSINESS DECISION-MAKING PROCESSES. I. Ivchenko, O. Ivchenko, I. Radkevich (2023). Black Sea Economic Studies.
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