AI for Financial Services · SOC 2 in progress

Finance AI built for regulators - not just dashboards.

For banks, insurers, and fintech. Real-time fraud anomaly detection, risk forecasting, 24/7 voice agents on every line, and an on-prem deployment option for data-residency-sensitive workloads.

  • Fraud detection
  • Risk forecasting
  • Voice agents
  • AML monitoring
  • On-prem deployment

01 · The Finance-AI Gap

Why Most Finance-AI Deployments Fail Audit.

  • Rule-based fraud monitoring is a treadmill

    Every novel fraud pattern requires a new rule. By the time the rule ships, the pattern has moved.

  • Forecasts that can't be backtested

    Black-box AI forecasts that can't be explained to a regulator are worse than no forecast at all.

  • Data residency is a hard constraint

    EU client work under GDPR, India under DPDP, sovereign-cloud requirements - the AI has to deploy on-prem when needed.

02 · What We Build for Financial Services

Use Cases Live in Production.

  • Real-time fraud anomaly detection

    ML-learned baselines flag materially unusual transactions in under 3 seconds, with reasoning attached.

    ↓ 71% false positives
  • 24/7 customer voice agents

    Sub-second voice agents on every client line, in 14+ languages. CSAT goes up, not down.

    +18 CSAT pts
  • Risk & demand forecasting

    ML forecasts with confidence intervals, scenario modeling, and full backtest reporting for regulators.

    ↓ 31% forecast variance
  • AML monitoring augmentation

    Pattern detection across customer behavior, transaction flow, and counterparty risk - analyst-explainable.

    +58% novel-pattern catch
  • KYC document review

    Identity-document parsing, sanctions screening, and risk-tier assignment with citations preserved.

    ↓ 60% onboarding time
  • Claims triage (insurance)

    Claim intake, fraud-likelihood scoring, and routing to adjusters with explanation.

    ↓ 40% cycle time

03 · Your Financial Services Stack

Agent 01 · Anomaly Detection

Real-time fraud monitoring

ML-learned baselines that flag materially unusual transactions and behaviors, with reasoning attached.

  • Under 3-second alert latency from event to routed alert
  • False-positive rate < 10% (vs. 30–50% for rule-based)
  • Novel patterns caught on first occurrence
Explore Anomaly Agents

Agent 02 · Voice Agents

24/7 voice on every line

Sub-second voice agents on customer lines - multi-language, compliance-aware, on-prem option available.

  • Sub-second turn-taking, 14+ languages live
  • Full call recording + compliance review log
  • Deploys behind your existing PBX or contact-center
Explore Voice Agents

04 · Measured Outcomes

Production Results from Cited Financial Customers.

False-positive rate

↓ 71%

Vs. rule-based monitoring; analysts spend time on real issues, not noise.

CSAT improvement

+18pts

From the Voice Agents financial-services deployment cited on the homepage.

Forecast variance

↓ 31%

Quarterly variance cut by a third within 2 quarters of deployment.

Our rule-based fraud system threw thousands of alerts a day; we ignored most of them. Anomaly Agents send us under twenty - and they're the ones we actually act on.
Head of Trust & SafetyMarketplace · 80M+ usersVerified

Built for regulated industries

Secure AI. Deployed Fast.
Built for Your Workflows.

Your data stays yours

Private cloud and on-prem deployment available. Your data never trains our models. Ever.

Live in 2–4 weeks

Pre-built modules deploy fast - no months-long IT projects, no consultancy bloat.

Plugs into existing tools

Salesforce, Dynamics, ServiceNow, SAP, custom DMS/ERP. We integrate. We don't replace.

Compliance-ready

Built for regulated industries - finance, healthcare, legal, manufacturing, public sector.

Compliance postureSOC 2 · in progressISO 27001 · in progressHIPAA · alignedGDPR · DPA availableIndia DPDP 2023
Pilot on One Signal

Send Us Your Noisiest Alert Stream.
We'll Show You Signal over Noise.

30 minutes with a solutions architect. Walk out with a pilot scope and a regulator-ready architecture diagram.