Financial Services

Banks and financial institutions process millions of transactions daily, with reconciliation and fraud detection among the most resource-intensive and error-sensitive operations. Atomeus deploys Fast Forward for reconciliation cycles and AI agents for real-time fraud detection, ensuring accuracy, speed, and continuous vigilance.

BankingAI AgentAdaptive Fraud Monitoring

Live deployment

Case 1

Automated Banking Reconciliation

Banking · Multi-System Transaction Matching

The Challenge

Finance teams were spending significant analyst time each period manually matching transactions across multiple accounts and systems. The process was slow, susceptible to human error, and left discrepancies undetected, creating downstream problems in reporting and compliance.

The Solution

FastForward runs the full reconciliation cycle overnight, every night. The intelligent agent matches transactions across all source systems, identifies discrepancies, and produces a complete reconciliation report, ready for the finance team at the start of each day. Flagged items include all context needed for immediate review.

Added Value

Reconciliation becomes a background process rather than a manual effort. Finance teams start each day with a clean, reviewed position rather than hours of matching work ahead of them. Audit trails are complete and consistent.

Key Outcomes

Reconciliation quality

Complete audit trail on every transaction

Discrepancy handling

Automatically flagged with full context

Execution

Overnight, results ready at start of day, no more manual labor

Analyst time freed

98% time saved

Case 2

Real-Time AI Fraud Detection

AI Agent · Adaptive Fraud Monitoring

The Challenge

Existing rules-based fraud detection systems struggled to keep pace with evolving fraud patterns. False positives were high, novel fraud vectors went undetected, and the volume of alerts requiring manual review placed significant pressure on fraud operations teams.

The Solution

FastForward AI agents monitor live transaction streams continuously, identifying anomalous patterns indicative of fraudulent activity before funds leave the institution. Unlike static rules, the models adapt continuously to new fraud vectors, learning from outcomes to improve detection accuracy over time. Suspicious transactions trigger immediate alerts and optional automated holds.

Added Value

Fraud is detected faster and more accurately than with rules-based systems. False positives are reduced, allowing fraud operations teams to focus on genuine threats. The institution gains a detection capability that improves the longer it runs.

Key Outcomes

Detection quality

Adaptive models, continuously improving accuracy

Alert handling

Automated holds on high-confidence detections

Detection speed

Real-time

False positive rate

>99,9%

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