AI-Powered Financial Intelligence

Detect fraud in real time, predict borrower risk, and automate compliance — with a single AI platform built for financial institutions.

Early Access

Built for banks, credit unions, insurers, and fintech lenders.

The Cost of Acting Too Late

Undetected fraud eroding margins
High loan default rates
Manual compliance overhead

Most financial institutions rely on rule-based systems that cannot keep pace with evolving fraud tactics or the complexity of modern credit risk.

Financial Fraud Detection

Arcus AI analyzes every transaction in real time against a continuously updated model of normal user behavior — catching fraud the moment it deviates, not after the loss has occurred.

Unlike static rule engines, our models adapt to new fraud typologies automatically, reducing the manual rule-maintenance burden on your fraud operations team.

Real-Time Transaction Monitoring

  • Millisecond transaction scoring — every transaction is evaluated in real time using ensemble ML models
  • Advanced AI models — combines gradient boosting and neural networks for high detection accuracy
  • Explainable risk signals — each score includes ranked contributing factors (amount deviation, geolocation mismatch, device anomaly, velocity patterns)
  • Sub-10ms latency at scale — processes millions of events per day without performance bottlenecks
  • Action-ready insights — fraud analysts receive full context for immediate decision-making
  • No infrastructure changes required — integrates seamlessly with existing payment systems

False Positive Reduction

  • Achieves 99.5% fraud detection rate
  • Maintains false positive rate below 0.1%
  • Outperforms industry average false positive rates (0.3–0.5%)
  • Reduces customer friction from incorrect declines
  • Prevents operational overload for fraud teams
  • Uses multi-stage scoring for higher precision
  • Enriches decisions with customer context and behavioral data
  • Continuously improves via analyst feedback loop
  • Enhances model calibration over time
  • Delivers lower operational costs and higher customer satisfaction

AML & KYC Compliance Automation

  • Automates Suspicious Activity Report (SAR) generation
  • Performs customer risk scoring for KYC reviews
  • Enforces transaction monitoring thresholds aligned with regulations
  • Supports compliance with AMLD6, FinCEN, and local frameworks
  • Pre-classifies alerts by fraud typology (structuring, layering, smurfing)
  • Automatically compiles supporting evidence for each case
  • Reduces analyst review time by over 60%
  • Ensures consistent, audit-ready documentation
  • Streamlines end-to-end AML compliance workflows

Behavioral Biometrics & User Profiling

  • Continuous authentication — monitors user behavior patterns (typing rhythm, mouse movement, touch pressure) to verify identity throughout the session
  • Dynamic user profiling — builds a real-time profile of each user’s typical behavior and flags deviations that may indicate account takeover
  • Device fingerprinting — identifies and tracks devices across accounts to detect mule networks and synthetic identities
  • Multi-modal data fusion — combines behavioral signals with transaction data for a holistic fraud risk assessment
  • Adaptive learning — models evolve with changing user behavior and emerging fraud tactics without manual intervention

Network & Graph-Based Pattern Recognition

  • Detects multi-account, coordinated fraud activity — beyond single-account analysis
  • Builds a live relationship graph — across accounts, devices, IPs, phone numbers, and emails
  • Continuously maps hidden connections — between entities in real time
  • Uses graph neural network (GNN) models — to analyze complex relationships
  • Identifies organized fraud clusters — early detection
  • Detects mule account networks and synthetic identity rings
  • Flags bust-out fraud schemes before funds are drained
  • Propagates newly discovered fraud nodes across the network instantly
  • Protects linked accounts through shared intelligence and network effects

Adaptive Model Retraining

  • Adapts to rapidly evolving fraud tactics
  • Runs a continuous learning pipeline for model improvement
  • Ingests confirmed fraud labels from case management systems
  • Retrains production models on a weekly cadence
  • Eliminates need for manual feature engineering
  • Monitors concept drift automatically
  • Detects model performance degradation in real time
  • Triggers automated retraining based on performance thresholds
  • Deploys updated models with zero downtime (blue-green rollout)
  • Ensures consistently high detection accuracy over time

Loan Risk Management

Arcus AI replaces static credit scorecards with dynamic, machine learning-driven risk models that evaluate the full picture of a borrower’s financial health — going far beyond the credit bureau score.

The result is more accurate underwriting, fewer defaults, higher approval rates for creditworthy borrowers, and a defensible, explainable decision for every application.

AI-Driven Credit Scoring

  • Institution-specific credit models — trained on your historical loan performance, not generic benchmarks
  • Multi-source data integration — bureau data, transaction history, income signals, employment stability, and behavioral patterns
  • Probability-of-default scoring — precise risk estimation with calibrated confidence intervals
  • Dynamic score updates — continuously refreshed throughout the loan lifecycle, not just at origination
  • Lifecycle risk monitoring — enables proactive portfolio management and early risk intervention
  • High-fidelity risk insights — tailored to your customers, products, and risk appetite

Default Prediction & Early Warning

  • Early default prediction — estimates probability of default within 30, 60, and 90 days
  • Time-series + survival models — captures evolving borrower risk over time
  • Prioritized watchlists — surfaces high-risk accounts before missed payments occur
  • Behavioral early-warning signals — detects transaction deterioration, rising utilization, payroll disruptions, and spending shifts
  • Proactive intervention enablement — allows outreach before delinquency escalates
  • Lower recovery costs — early action is significantly cheaper than collections
  • Customer relationship preservation — reduces friction while managing risk

Alternative & Open Banking Data

  • Expands beyond traditional credit scoring — captures borrowers excluded by bureau-based models
  • Unlocks underserved segments — self-employed, recent graduates, new-to-country, thin-file consumers
  • Open banking data integration — ingests transaction data via API or file export
  • Reconstructed financial profiles — analyzes income, spending behavior, and stability
  • Improved risk visibility — evaluates applicants with limited or no credit history
  • Market expansion without added risk — grows approval base while maintaining or improving portfolio quality

Automated Underwriting & Decision Workflows

  • Automated underwriting workflows — straight-through processing for low-risk applications
  • Configurable decision thresholds — approve, decline, or route based on risk appetite
  • Human-in-the-loop for complex cases — only high-value decisions require manual review
  • Pre-built decision packs — model score, key risk drivers, comparable cases, and recommended action
  • Explainable recommendations — clear rationale supports every decision
  • Dramatically faster decisions — reduces turnaround time from days to minutes
  • Operational efficiency gains — underwriters focus on complex, high-impact cases

Portfolio Risk Monitoring & Stress Testing

  • Continuous portfolio monitoring — real-time tracking at segment and individual account level
  • Concentration risk detection — flags emerging exposure risks across sectors and segments
  • Automated stress testing — simulates macro scenarios (rate shocks, unemployment spikes, sector downturns)
  • Real-time portfolio health metrics — expected loss, risk-adjusted return, and capital adequacy
  • Faster risk decisions — enables rapid adjustments to pricing, provisioning, and portfolio mix
  • Proactive risk management — identifies issues early before they impact performance

Regulatory Compliance & Explainability

  • Fully explainable credit decisions — aligned with GDPR, ECOA, and SR 11-7 requirements
  • Automated adverse action notices — clearly states key factors behind declines
  • Built-in regulatory compliance — no manual drafting or interpretation needed
  • Model governance framework — tracks versions, validation results, and approvals
  • Performance monitoring & drift detection — ensures models remain accurate over time
  • Complete audit trail — ready for regulatory review and examination

Business Impact

Across retail banking, lending, and fintech deployments, Arcus AI customers report measurable outcomes within the first 90 days of production operation.

↓ 60%
Fraud Losses

Real-time scoring and behavioral analytics intercept the majority of fraudulent transactions before funds leave the institution.

↓ 25%
Loan Default Rate

More accurate risk scoring at origination and proactive early-warning monitoring reduce portfolio defaults significantly.

↑ 35%
Approval Accuracy

Alternative data and AI models approve more creditworthy borrowers who would have been declined by traditional scorecards.

< 0.1%
False Positive Rate

Industry-leading precision means fewer legitimate customers are blocked, reducing churn and fraud ops overhead simultaneously.

↓ 60%
Compliance Review Time

Automated SAR generation and KYC classification reduce the analyst time per compliance case dramatically.

90 days
Time to First Results

Integration, model training, and validation are completed within a structured pilot so your team sees real outcomes before full deployment.

How we quantify impact for your institution

During pilot scoping, Arcus AI works with your risk, fraud, and finance teams to establish a baseline — documenting current fraud loss rates, default rates, and compliance costs per product line. This baseline drives a site-specific ROI projection before the pilot begins and is reviewed at 30, 60, and 90 days to track realized outcomes against the forecast.