Agentic AI Platform For Transaction Intelligence & Risk Analysis
Challenge
The core challenge was twofold. First, the volume of transactions requiring review had grown beyond what the existing rule-based system and analyst team could process without significant latency or accuracy trade-offs. Second, the nature of financial risk had become too nuanced for static rules to capture reliably, legitimate transactions were being flagged, genuine risks were being missed, and the cost of both outcomes was mounting. The client needed a system that could reason across transaction context, apply risk scoring dynamically, and route only the cases genuinely requiring human judgment to the analyst team, while maintaining a full audit trail for regulatory purposes.
The core challenge was twofold. First, the volume of transactions requiring review had grown beyond what the existing rule-based system and analyst team could process without significant latency or accuracy trade-offs. Second, the nature of financial risk had become too nuanced for static rules to capture reliably, legitimate transactions were being flagged, genuine risks were being missed, and the cost of both outcomes was mounting. The client needed a system that could reason across transaction context, apply risk scoring dynamically, and route only the cases genuinely requiring human judgment to the analyst team, while maintaining a full audit trail for regulatory purposes.
Solution
We designed and delivered a multi-agent AI platform in which specialized agents handle distinct stages of the transaction intelligence workflow such as ingestion, enrichment, risk scoring, and escalation, operating in real time across live data pipelines.
To accelerate delivery, AI tooling was used throughout the development process for code generation, boilerplate, scaffolding, and automated test coverage. Every security-critical component, including risk scoring model architecture, data pipeline design, API security, and compliance logic was designed, reviewed, and validated exclusively by inVerita's senior engineers, ensuring that speed of delivery never came at the cost of system integrity.
We designed and delivered a multi-agent AI platform in which specialized agents handle distinct stages of the transaction intelligence workflow such as ingestion, enrichment, risk scoring, and escalation, operating in real time across live data pipelines.
To accelerate delivery, AI tooling was used throughout the development process for code generation, boilerplate, scaffolding, and automated test coverage. Every security-critical component, including risk scoring model architecture, data pipeline design, API security, and compliance logic was designed, reviewed, and validated exclusively by inVerita's senior engineers, ensuring that speed of delivery never came at the cost of system integrity.
Results
The result was a 35% reduction in development time and cost compared to a traditional build, with no shortcuts taken on the parts that mattered most. In production, the platform reduced false positive transaction flags by over 40%, cut average analyst review time per case significantly, and enabled the client's compliance team to handle a materially higher transaction volume without scaling headcount.
The result was a 35% reduction in development time and cost compared to a traditional build, with no shortcuts taken on the parts that mattered most. In production, the platform reduced false positive transaction flags by over 40%, cut average analyst review time per case significantly, and enabled the client's compliance team to handle a materially higher transaction volume without scaling headcount.