Europe’s €1 Trillion Late Payment Problem Is Getting Worse - Cleavr Thinks AI Can Fix It

2026-03-25 · Source: The French Tech Journal · Field: Finance & Economics — FinTech & Digital Financial Services, Corporate Finance & Treasury · Depth: Novice, medium

Summary

Paris-based startup Cleavr, founded in 2025, recently secured €1 million in funding from investors including Kima Ventures and Better Angle to address Europe's escalating late payment problem, which currently amounts to €1 trillion. Despite a majority of SMEs paying within 60 days, average payment delays in France climbed to 13.6 days in late 2024, exceeding the European average of 12.1 days. Cleavr's AI-driven platform connects to a company's ERP, automating up to 80% of the accounts receivable process by identifying overdue invoices, initiating multi-channel follow-ups (email, SMS, phone), and handling responses. The system, which leverages existing LLMs from OpenAI, Anthropic, and Mistral, adapts communication strategies to specific sectors and countries, aiming to guarantee follow-up on 100% of invoices. Early results show clients achieving a 30% to 40% reduction in payment delays and a 37% decrease in Days Sales Outstanding (DSO).

Key takeaway

For finance executives grappling with rising late payments and operational inefficiencies, Cleavr's AI-driven solution offers a concrete path to significant improvement. You should evaluate how automating up to 80% of your accounts receivable workflow could reduce payment delays by 30-40% and free up finance teams from manual follow-ups, ultimately improving cash flow and client relationships.

Key insights

AI can automate complex accounts receivable processes, significantly reducing late payment delays across diverse European markets.

Principles

• Late payments are often a process problem, not a behavioral one.
• Systematic, intelligent follow-up improves invoice payment rates.
• AI can adapt communication strategies to sector and country specifics.

Method

Cleavr's AI platform integrates with ERPs, uses LLMs to automate multi-channel follow-ups, handles disputes, identifies new contacts, and adapts communication based on sector and country.

In practice

• Automate accounts receivable beyond initial reminders.
• Tailor collection communication to industry and geography.
• Utilize AI to identify alternative contacts for overdue invoices.

Topics

• AI-driven Collections
• Accounts Receivable Automation
• Fintech Funding
• Late Payment Problem
• Large Language Models

Best for: Executive, Investor, Entrepreneur, Operations Professional

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Editorial summary, takeaway, and curation by AIssential. Original article published by The French Tech Journal.