SOX & GLBA Compliant AI • On-Premise Financial Intelligence

AI for Financial Services —
Compliant. Private. Audit-Ready.

Deploy AI on your financial institution’s infrastructure — fraud detection, regulatory compliance, risk analysis, and customer service powered by private models. No client financial data flows to third-party AI providers. Full audit trail for every AI interaction.

SOX • GLBA • PCI DSS • FINRA • SEC Compliant Deployments

0
Client Data Breaches
Among Compliant Clients
100%
On-Premise
Data Processing
23+
Years Financial
IT Security
SOX
Audit-Ready
Deployments
The Challenge

Why Financial Services Need Private AI

Financial institutions face the most complex regulatory environment of any industry — with multiple overlapping frameworks governing how customer data can be processed, stored, and shared.

Regulatory Scrutiny of AI

The SEC, FINRA, OCC, and CFPB are actively scrutinizing how financial institutions use AI. Sending customer financial data to cloud AI providers without proper controls creates compliance exposure under GLBA, SOX, and emerging AI governance regulations.

Fraud Losses Accelerating

Financial fraud losses exceeded $10 billion in 2023. AI-powered fraud detection can identify suspicious patterns in real-time, but the transaction data required for training is among the most sensitive data a financial institution holds — it cannot flow to third-party cloud AI.

Model Explainability Requirements

Regulators require explainability for AI-driven decisions in lending, credit scoring, and fraud detection. Cloud AI black boxes don’t provide the transparency auditors need. Private deployment gives you full visibility into model behavior, training data, and decision logic.

Our Solution

Private AI for Finance — Audit-Ready from Day One

Financial AI Use Cases

Fraud Detection & Prevention

AI analyzes transaction patterns in real-time to identify fraudulent activity, synthetic identity fraud, account takeover attempts, and money laundering signals. Models trained on your institution’s transaction history for far fewer false positives than generic solutions.

Regulatory Compliance Monitoring

AI continuously monitors transactions, communications, and activities against regulatory requirements — BSA/AML, KYC, SOX controls, and FINRA rules. Automatically flags potential violations and generates compliance reports.

Credit Risk Analysis

AI evaluates credit applications using alternative data sources and complex risk models while maintaining full explainability for adverse action notices and fair lending compliance. All applicant data stays on your infrastructure.

Document Processing & Analysis

AI extracts data from financial documents (tax returns, financial statements, loan applications), validates information, and populates downstream systems. Handles handwritten documents, multi-format inputs, and complex financial tables.

Customer Service & Advisor Support

AI-powered customer support that answers account questions, explains products, assists with onboarding, and routes complex issues to human advisors — all without exposing customer financial data to external AI providers.

Financial Compliance Framework
  • SOX (Sarbanes-Oxley): Full audit trail for every AI interaction affecting financial reporting. Internal controls documentation for AI-assisted processes. CEO/CFO certification support.
  • GLBA (Gramm-Leach-Bliley): Customer financial information stays within your institution’s security boundary. Safeguards Rule compliance with encryption, access controls, and monitoring.
  • PCI DSS: Cardholder data processing by AI occurs within your PCI-scoped environment. No card data is transmitted to external AI services.
  • BSA/AML: AI-powered suspicious activity monitoring with full audit trail for FinCEN reporting. Transaction monitoring models trained on your institution’s patterns.
  • FINRA/SEC: AI-assisted supervisory procedures, communications monitoring, and suitability analysis with documented decision logic for regulatory examination.
  • Fair Lending (ECOA/HMDA): Model explainability tools ensure AI credit decisions can be documented and defended against disparate impact claims.
How We Deploy AI for Financial Institutions
Financial IT & Compliance Assessment
We audit your core banking systems, data warehouse, compliance infrastructure, and existing AI/ML initiatives. You receive a prioritized roadmap of AI opportunities ranked by regulatory risk reduction and ROI.
Model Selection & Validation
We evaluate and benchmark AI models against your financial data, validate for bias and fairness, and document model risk management per OCC SR 11-7 guidance.
Secure Infrastructure & Integration
AI servers deployed within your data center, integrated with core banking, data warehouse, and compliance systems via secure internal APIs. Full SOX audit logging from day one.
Model Risk Management
Complete model documentation, validation reports, ongoing monitoring metrics, and model governance framework aligned with regulatory expectations for AI in financial services.
Managed Operations & Compliance
Ongoing model monitoring, performance tracking, drift detection, and compliance reporting. We handle the technology so your team can focus on serving clients.
Financial AI Technology Stack
Financial LLMs
Models fine-tuned on financial regulations, products, and industry terminology
Fraud Detection AI
Real-time transaction scoring with anomaly detection and pattern recognition
vLLM / Ollama
High-throughput inference for multi-branch concurrent access
Model Explainability
SHAP/LIME integration for transparent, defensible AI decisions
NVIDIA Enterprise GPUs
Right-sized hardware for your transaction volume and user count
RAG + Regulation Search
AI connected to regulatory databases, policy manuals, and compliance documentation
FAQ

Financial AI — Frequently Asked Questions

How does AI help with SOX compliance?
Private AI enhances SOX compliance by automating internal control testing, monitoring financial reporting processes for anomalies, and maintaining comprehensive audit trails. Every AI interaction is logged with timestamps, user identity, and decision rationale — providing the documentation auditors require under Section 404.
Can AI reduce false positives in fraud detection?
Yes. Generic fraud detection systems generate 95%+ false positive rates. AI models trained on your institution’s specific transaction patterns typically reduce false positives by 50–70% while improving true fraud catch rates. This saves investigation staff time and reduces customer friction from legitimate transactions being declined.
How does private AI handle fair lending requirements?
We build model explainability into every credit-related AI deployment using SHAP values and LIME analysis. This allows you to provide specific, individualized adverse action reasons as required by ECOA, document the factors driving each decision for HMDA reporting, and demonstrate non-discriminatory lending practices to regulators.
What model risk management framework do you follow?
We align with OCC SR 11-7 / Federal Reserve SR 11-7 model risk management guidance. This includes independent model validation, comprehensive documentation, ongoing performance monitoring, annual model reviews, and defined escalation procedures for model degradation or unexpected behavior.
How quickly can we deploy fraud detection AI?
A basic fraud detection deployment with a pre-trained model can be operational in 3–4 weeks. Custom models trained on your transaction history take 6–10 weeks including data preparation, training, validation, and compliance documentation. The AI runs in shadow mode (monitoring without blocking) initially to validate accuracy before going live.

Related Services

Ready to Deploy Audit-Ready AI?

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No obligation • SOX audit-ready • Results in one week