What challenges did SymphonyAI face before AI implementation?

Banks face increasingly sophisticated financial crimes and money laundering schemes Investigators overwhelmed by massive volumes of alerts and disparate data sources Manual data gathering and analysis delays investigations, leading to slower response times Compliance departments experience high workloads and operational inefficiencies Failure to detect and respond to financial crime can result in regulatory penalties and reputational damage

Source

MicrosoftLive source

SymphonyAI and Microsoft revolutionize financial crime prevention

Use case typeRisk assessmentUpdated Jun 13, 2026

SymphonyAI's Sensa Copilot, developed using Microsoft Azure OpenAI Service, transforms financial crime prevention. This AI solution automates data collection and risk analysis, increasing compliance department efficiency by 60%. The partnership reduces investigator workloads and accelerates fraud investigations.

Organization: SymphonyAI
Industry: Finance
Location: United States
Published: April 2025

Reported outcomes

+60%

productivityProductivity & throughput

Strategic outcomes

Speed & agilityAccelerated fraud investigation and alert resolutionCost efficiencyReduced investigator workloadsNew product / capabilityAutomated suspicious activity report draftingBetter decisions & insightImproved suspicious behavior detection

Catalog median for productivity & throughput deployments: +45% across 225 reported metrics. Compare benchmarks →

Primary read

Use case focus

Showing 3 of 5

1Automated suspicious activity report (SAR) drafting
2Natural language investigation assistant for financial crime teams
3Fraud and money-laundering alert analysis and triage

Banks face increasingly sophisticated financial crimes and money laundering schemes
Investigators overwhelmed by massive volumes of alerts and disparate data sources
Manual data gathering and analysis delays investigations, leading to slower response times
Compliance departments experience high workloads and operational inefficiencies
Failure to detect and respond to financial crime can result in regulatory penalties and reputational damage

Leveraged Azure OpenAI Service to power Sensa Copilot's AI capabilities
Automated data collection, collation, and summarization from financial and third-party sources
Implemented natural language interface for running analyses and drafting reports
Deployed on secure, scalable Azure infrastructure including Azure Kubernetes Service
Integrated with Sensa-NetReveal's machine learning-based anomaly detection

Technologies

Azure OpenAI