AI Agents

Context

Built for banking within the Fraud Investigation Stack and used by Fraud / Investigation teams, this agent tackles one of the most mechanical but time-consuming parts of investigation work: gathering case data from multiple systems and turning it into one coherent file. It is designed for Tier 1–3 banks where investigators routinely lose time pulling fragments from core banking, logs, device tools, and network sources.

What it does

The agent aggregates and correlates data from multiple source systems—core banking, logs, device signals, and network data—into a unified case file. Instead of making investigators open five or more systems and manually reconcile identities, transactions, sessions, and events, it brings those artifacts together into a single structured record. The point is not just collection, but correlation: the right person, the right account, the right device, the right activity, in one place.

Core AI functions

The core capability is multi-source data correlation, entity resolution, and timeline alignment. The agent pulls signals from different systems, resolves which records belong together, and aligns them so the investigation starts from an integrated fact base rather than disconnected evidence scraps.

Problem solved

Investigators manually pull data from five or more systems and spend more than 30 minutes per case on data gathering alone. That is costly, repetitive, and risky, because manual evidence collection is exactly where things get missed.

Business impact

An 80% reduction in data-prep time, a consistent audit trail, and less risk that something important is missed. In operational terms, that means investigators start with a full case file faster, documentation is cleaner, and the bank has a more defensible record of how the case was built.

Integration and adjacent use cases

Integration complexity is medium–high: the agent needs read access across multiple banking and investigative data sources and a place to write the compiled case file back into the case-management environment.

Common combinations in this stack:

• Alert Triage Agent to decide which alerts deserve compilation first;

• False Positive Screener to reduce the number of weak alerts that ever need a compiled file;

• Timeline Generator to transform the compiled evidence into a chronological story;

• Voice Call Transcriber to add searchable call transcripts into the case record;

• Case Narrative Generator to draft the investigation memo from the compiled evidence set; and

• SAR Report Compiler Agent to pull regulatory fields from the same compiled case once reporting becomes necessary.