During the Covid period, insolvencies across Europe plummeted. Governments stepped in with moratoria, guaranteed loans (PGE in France), and massive subsidies. But in 2025, the picture has reversed: business failures are rising sharply.
👉 For banks, insurers, fintechs, brokers and large enterprises, this is a turning point. Relying only on historical data and reactive processes is no longer sustainable. What’s needed is a shift towards predictive, explainable and real-time risk monitoring powered by AI and Open Banking.
This article covers:
The latest figures on business failures in 2025.
Why traditional models are failing.
How AI and Open Banking change the game.
Real-world use cases by sector.
A practical checklist to prepare.
France: +15% increase in insolvencies year-on-year (Banque de France).
Eurozone: insolvencies back to pre-Covid levels, with steeper increases in construction, hospitality, and retail.
SMEs/TPEs: most exposed, representing nearly 75% of failures.
Rising interest rates → heavier debt burden.
Inflation → margin squeeze, especially in energy-intensive sectors.
Supply chain fragility → delayed supplier payments cascade.
End of Covid aid → disappearance of the safety net.
👉 Bottom line: systemic risk is back, with failures potentially triggering chain reactions.
Published with a 12–18 month delay.
Fail to capture weak signals (cash erosion, payment delays).
A company can look healthy on paper while collapsing in reality.
Built on a handful of ratios (liquidity, leverage).
Poor coverage for thin-file SMEs and startups.
Not designed for dynamic, high-frequency data.
Insolvencies often detected too late, once liquidity is already gone.
Leads to heavy provisioning and write-offs for lenders and insurers.
👉 Traditional methods = reactive and costly.
Open Banking APIs give access to company accounts (with consent).
Detect weak signals: declining revenues, growing supplier arrears, abnormal seasonality.
Combine banking, accounting, legal and public datasets.
Learn from historical patterns of deterioration.
Generate dynamic risk scores, updated continuously.
Each alert comes with factor-level explanations.
Example: “Net cash down 35% over three consecutive months.”
Enables targeted, corrective action.
Reliable scoring for SMEs, startups and atypical sectors.
Use of alternative data when limited history is available.
Before: defaults only flagged after 90 days past due.
With AI + Open Banking: alerts triggered on early stress signals → proactive restructuring.
Impact: 25% reduction in losses on SME portfolios.
Before: premiums calculated annually on stale financials.
With AI: premiums adjusted dynamically based on real-time health.
Impact: improved loss ratio and fairer pricing.
Before: heavy manual review for each file.
With RocketFin API: instant pre-scoring and explainability.
Impact: better hit rate, faster time-to-market.
Before: critical supplier insolvencies hit without warning.
With continuous AI scoring: early alerts on deteriorating vendors.
Impact: protected supply chain continuity.
Integrate Open Banking APIs with secure consent flows.
Deploy AI models combining banking, accounting, legal and public data.
Move to real-time, dynamic scoring.
Ensure explainability (factor-level breakdowns, reason codes).
Score inclusively: SMEs and thin-file companies included.
Implement webhooks for proactive alerts.
Stay compliant with GDPR, EU AI Act, PSD3.
The surge in business failures in 2025 is a wake-up call. Institutions must abandon post-mortem analysis and embrace predictive, explainable, real-time monitoring.
👉 By combining AI + Open Banking, financial institutions and corporates can:
Detect early warning signals.
Take proactive action.
Reduce losses and protect their value chains.
With RocketFin, you get real-time, explainable, inclusive scoring, delivered via API and enhanced with continuous monitoring.
Next step: explore the RocketFin demo and test proactive risk detection on your portfolio : https://www.rocketfin.ai/demo