AI Agent

Transaction Monitoring AI Agent

Catch suspicious transactions before they become compliance failures

From 500 daily alerts to 50 that matter. Cut false positives by 90% while catching more true suspicious activity.

Benefits

Focus on Real Risks, Not False Alarms

AML transaction monitoring generates thousands of alerts. 95% are false positives consuming analyst time. Meanwhile, sophisticated patterns slip through. The Transaction Monitoring Agent separates signal from noise.

90%

False positive reduction

35%

More true positives caught

70%

Faster investigation

Agentic Flows

Complete AML Monitoring Workflow

Automated monitoring workflow: real-time transaction screening, rule-based and ML pattern detection, alert scoring and prioritization, automated false positive disposition, investigation support with evidence gathering, and SAR preparation assistance.

How it works

Intelligent Alert Analysis

The agent analyzes transaction patterns against customer profiles, scores alerts by true risk indicators, auto-closes obvious false positives with documentation, and surfaces high-risk activity for analyst review.

Self-learning agents

The agent improves with every task, adapting to outcomes, applying feedback, and self-correcting using Constitutional AI.

Upto 98% accuracy

As a result of constant feedback loops, Beam AI Agents refine their approach with every cycle, leading to 98% accuracy across flows.

Smart model switching

We call it ModelMesh. Each agent selects the right model for the task, balancing speed, accuracy, and cost in real time.

Extract order number, seller information, delivery information, and payment details from the uploaded order forms.

Completed

ID-0E48

Extract order number, seller information, delivery information, and payment details from the uploaded order forms.

Completed

ID-0E48

Extract order number, seller information, delivery information, and payment details from the uploaded order forms.

Completed

ID-0E48

Self-learning agents

The agent improves with every task, adapting to outcomes, applying feedback, and self-correcting using Constitutional AI.

Upto 98% accuracy

As a result of constant feedback loops, Beam AI Agents refine their approach with every cycle, leading to 98% accuracy across flows.

Smart model switching

We call it ModelMesh. Each agent selects the right model for the task, balancing speed, accuracy, and cost in real time.

Extract order number, seller information, delivery information, and payment details from the uploaded order forms.

Completed

ID-0E48

Extract order number, seller information, delivery information, and payment details from the uploaded order forms.

Completed

ID-0E48

Extract order number, seller information, delivery information, and payment details from the uploaded order forms.

Completed

ID-0E48

Integrations

Works with Your AML Stack

Direct integration with Actimize, SAS, Oracle FCCM, and Temenos FCM. Connects to core banking for transaction feeds. Case management via your existing platform or standalone deployment.

Implementation

Better Monitoring in 14 Days

Connect your transaction monitoring system and configure scoring rules. Train on historical alert dispositions. Most teams see false positive reduction within the first month of operation.

Step 1

Agent discovery

Conduct workshop(s) with relevant stakeholders to identify and prioritize use cases and map the requirements

Step 1

Agent discovery

Conduct workshop(s) with relevant stakeholders to identify and prioritize use cases and map the requirements

Step 2

Agent setup

Develop and launch your first agent with basic logic and integration (process scoping and recording, test dataset of 30-50 examples, baseline agent running and testing with target output mapping)

Step 2

Agent setup

Develop and launch your first agent with basic logic and integration (process scoping and recording, test dataset of 30-50 examples, baseline agent running and testing with target output mapping)

Step 3

Agent training

Test performance and gather feedback from process users (agent optimization up to 80% against expected output, variable-level accuracy measurement and optimization, integrations setup, and feedback API interfacing)

Step 3

Agent training

Test performance and gather feedback from process users (agent optimization up to 80% against expected output, variable-level accuracy measurement and optimization, integrations setup, and feedback API interfacing)

Step 4

Agent live

Extend to more workflows and client teams (live and continuous monitoring, human-in-the loop interfacing, node auto-tuning for automated prompt enhancements, >90% accuracy improvement, weekly business logic improvements)

Step 4

Agent live

Extend to more workflows and client teams (live and continuous monitoring, human-in-the loop interfacing, node auto-tuning for automated prompt enhancements, >90% accuracy improvement, weekly business logic improvements)

Key Features

Advanced Monitoring Technology

Behavioral Analytics

Learns normal patterns for each customer segment. Flags true anomalies vs. expected variations.

Alert Scoring

ML-based risk scoring prioritizes analyst queues. Focus effort on highest-risk alerts first.

Auto-Disposition

Closes known false positive patterns automatically. Full documentation for audit and regulatory review.

Demo

See Transaction Monitoring in Action

Watch the agent process a day's transaction alerts, scoring by risk, auto-disposing false positives, and queuing true suspicious activity with investigation-ready evidence packages.