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Microsoft Software / SaaS United States

SymphonyAI

Fraud Detection · Risk Management

"Strong risk assessment helps prevent financial crime, reduces losses, and supports a healthy financial ecosystem.

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"We expect that investigations can be completed 60 to 70 percent faster, with 70 percent less effort on the part of the human investigator.
A fraud-free future While Sensa-NetReveal is already a category leader in transaction monitoring, [1] the Sensa Copilot increased the productivity of a financial institution's compliance department by ~60 percent in early testing states.
SymphonyAI has made a $100 million investment commitment over the next three years in generative AI for product development of its vertical applications across industries.
The Sensa Copilot will make this possible-we expect that investigations can be completed 60 to 70 percent faster, with 70 percent less effort on the part of the human investigator.

Reported impact

Business outcome

"We expect that investigations can be completed 60 to 70 percent faster, with 70 percent less effort on the part of the human investigator.

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement
  • Risk Reduction

Solution

SymphonyAI built its Microsoft solution to improve fraud-investigation workflows. "Strong risk assessment helps prevent financial crime, reduces losses, and supports a healthy financial ecosystem. Key components included Azure OpenAI Service ran model inference or heavy processing, and Azure Kubernetes Service coordinated workflows, prompts, or deployment. User prompts and domain content are orchestrated through Azure OpenAI Service, processed by Azure OpenAI Service, and then delivered through Azure Kubernetes Service so SymphonyAI can return context-aware answers or automation in the flow of work. The governed Microsoft stack gave SymphonyAI a reusable foundation for generative ai / llm, ai and machine learning while keeping deployment, security, and day-to-day operations inside the Microsoft estate. "We expect that investigations can be completed 60 to 70 percent faster, with 70 percent less effort on the part of the human investigator.

Data flow

User prompts and domain content are orchestrated through Azure OpenAI Service, processed by Azure OpenAI Service, and then delivered through Azure Kubernetes Service so SymphonyAI can return context-aware answers or automation in the flow of work.

Solution architecture

2 components · 2 layers
  1. Compute
    • Azure OpenAI Service Runs large language models that generate grounded responses, summaries, and copilots.
  2. Orchestration
    • Azure Kubernetes Service Operates containerized application and AI workloads in a managed Kubernetes environment.

Architecture clues

  • The Sensa Copilot for financial crime prevention is built on Azure infrastructure, Azure Kubernetes Service (AKS) , Azure AI solutions, and Azure OpenAI.

Evidence from the source

"Strong risk assessment helps prevent financial crime, reduces losses, and supports a healthy financial ecosystem.
"We expect that investigations can be completed 60 to 70 percent faster, with 70 percent less effort on the part of the human investigator.
A fraud-free future While Sensa-NetReveal is already a category leader in transaction monitoring, [1] the Sensa Copilot increased the productivity of a financial institution's compliance department by ~60 percent in early testing states.
SymphonyAI has made a $100 million investment commitment over the next three years in generative AI for product development of its vertical applications across industries.
The Sensa Copilot will make this possible-we expect that investigations can be completed 60 to 70 percent faster, with 70 percent less effort on the part of the human investigator.
The cost of financial crime is estimated to represent about 5 percent of the global GDP, a significant burden on the economy and an enormous impact on people and organizations.
Transparency Evidence & methodology 90/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value90/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    4 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    6 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Microsoft

    SymphonyAI customer story

    First-party
    https://www.microsoft.com/en/customers/story/1653127518144887835-symphonyai-partner-professional-services-azure
    Source type
    Vendor-published customer story
    Published
    Jul 7, 2023
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Jul 7, 2023 · Microsoft · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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