Financial Services · Security
Real-Time Fraud Detection
Traditional rule-based systems generate too many false positives and fail to catch sophisticated new fraud patterns.
Applies toFinancial Services
The challenge
What stands in the way
Traditional rule-based systems generate too many false positives and fail to catch sophisticated new fraud patterns.
The solution
How Scrydon solves it
Deploy autonomous agents that learn transaction patterns in real-time, flagging anomalies with high precision without moving data off-premise.
Built on
In practice
How this plays out
Rule-based fraud systems catch what they were explicitly told to catch, which means fraud rings adapt their patterns slightly and slip straight through, while legitimate customers get blocked by rules too blunt to tell the difference.
Autonomous agents built on the Agentic AI Platform learn transaction patterns continuously and flag genuine anomalies with far higher precision, running entirely on infrastructure the bank controls so the behavioural models never have to leave the institution to get smarter.
The result
- Reduction in fraud losses by 40% and false positives by 60%, improving customer trust.
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Financial Services use cases
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- Algorithmic Risk Management
- Automated KYC/AML
- Regulatory Reporting
- Shadow AI Discovery for EU AI Act Readiness
- Decision Intelligence for Credit & Trading Desks
- Persistent Memory for AML Investigations
- Coordinated Human-Agent Loan Underwriting
- Ontology-Based Single Customer View
- Financial Crime Network Investigation