The financial industry demands compliance teams to be ever more efficient. But it imposes on them a fragmented ecosystem of tools that don't communicate with each other.
The problem: data scattered across more than 10 systems
Each department has its reference tool. Sales have their CRM. Finance navigates in its accounting software. Support manages its requests in a ticketing platform.
And compliance? It juggles daily between:
- •The CRM to access customer information and KYC
- •The business back-office to consult transaction history
- •The case management tool to process alerts
- •Excel spreadsheets and Notion databases to document processes
- •The Internet to verify merchant sites, company registries, sanctions lists...
This fragmentation creates a major problem: the decision-making context doesn't exist anywhere in one place.
For example, to analyze an AML alert, a compliance analyst must:
- Open the alert in case management
- Switch to the CRM to retrieve customer identification information
- Consult the back-office to verify suspicious transactions
- Search on Google/LinkedIn to understand their real activity
- Check public registries (INPI, Companies House...)
- Compile everything in Excel or Word for documentation
Result: 15-20 minutes per alert, 80% of which is navigation between systems.
Why all 'Compliance OS' have failed
Faced with this observation, the idea of a centralized 'Compliance OS' is appealing: a single system that would bring together all compliance data and workflows.
Except that this approach has systematically failed. And for good reasons:
1. Technical stack heterogeneity
Each fintech has its tech history: legacy banking systems for some, cloud-native architecture for others. Impossible to standardize.
2. Regulatory fragmentation
Compliance obligations vary by country, licenses, types of activity. A 'universal OS' would need to handle thousands of special cases.
3. Budget constraints
Mid-size fintechs don't have the means or desire for a multi-year IT overhaul project.
4. Business workflow specificity
Each organization has developed its own processes, its own business rules, its own special cases. A standardized tool cannot cover everything.
The real solution: connect silos where they are
The good news? No need for a centralized OS anymore.
Modern technologies now allow connecting existing systems without replacing them:
Browser automation
Tools like Playwright or Selenium allow automating web interactions, even on legacy applications without APIs.
API orchestration
When APIs exist, they can be orchestrated to create smooth cross-system workflows.
Model Context Protocol (MCP)
Large language models can now directly access data sources via standardized connectors.
Intelligent normalization
AI models can extract and structure information, regardless of the source format.
The result: pragmatic automation
This approach doesn't create a perfect system. But it creates something that works.
Provided you master three pillars:
1. Business workflows:
Deeply understand how analysts actually work
2. Business rules:
Document decision criteria, thresholds, exceptions
3. Edge cases:
Identify and handle the 20% of complex cases that break automation
Concrete example: automated AML alert processing
Instead of replacing all tools, it's possible to create an AI agent that:
- Reads the alert in case management (via API or browser automation)
- Automatically retrieves customer data (CRM), transactional data (back-office), web data (search APIs)
- Compiles the context into a single structured document
- Proposes an analysis based on documented business rules
- Submits to human validation for complex cases
Measured impact: 80% reduction in information gathering time, allowing analysts to focus on analysis and decision-making.
Tangible benefits
This approach delivers three immediate gains:
Less copy-paste
Information flows automatically between systems. No more transcription errors.
Less navigation between tabs
Context is automatically aggregated. The analyst stays focused on their analysis.
More time for what matters
80% of freed time is reinvested in deep analysis, judgment, decision-making.
And you, how many tools in your compliance stack?
The average we observe with our clients: 8 to 12 different systems used daily by compliance teams. Some have more than 15.
Open question: is this fragmentation a technical problem... or an opportunity for intelligent automation?
About Kolar
We help European fintechs automate their compliance operations (AML/KYC/KYB) via AI agents that connect to their existing stack. Without system migration, without IT overhaul.
Want to see how it works concretely on your stack?
Let's talk for 30 minutes