Technical Evidence for
Fintech AI Risk Decisions
We help fintech companies, banks, and financial services providers evaluate AI systems used in lending, fraud detection, customer interaction, and decision automation before production deployment, audits, or enterprise due diligence.
Why Fintech AI Needs Independent Technical Evidence
Financial services AI systems influence credit decisions, fraud outcomes, and customer experiences at scale. A failure is not just a technical incident — it is a regulatory event, a consumer harm issue, and a board-level risk.
CTOs, CROs, compliance officers, and boards need defensible technical evidence demonstrating that AI systems do not produce biased outcomes, leak customer data, or operate without proper audit trails — before they face regulatory examination or enterprise due diligence.
Fintech-Specific Risk Coverage
Focused assessment areas for financial services and fintech AI systems
Credit & Lending Decision Exposure
AI models making or influencing credit decisions without documented fairness testing, bias monitoring, or explainability for adverse action notices.
Regulatory Explainability Gaps
Inability to explain AI-driven decisions to regulators, auditors, or affected customers as required by fair lending and consumer protection rules.
Customer Data Leakage
AI systems revealing customer financial records, transaction histories, or PII through response generation, RAG retrieval, or logging pipelines.
Missing Audit & Decision Trails
AI-influenced financial decisions and data access events not logged with sufficient detail for regulatory examination or dispute resolution.
Access Control & Segregation Failures
Customer-facing AI assistants or internal tools accessing financial data beyond authorized scope, or missing segregation between client accounts.
Fraud Model Evasion
Adversarial inputs or model drift causing fraud detection systems to miss patterns, leading to financial loss and compliance failures.
Cloud Infrastructure & API Exposure
Misconfigured IAM, storage, or API gateways allowing unauthorized access to AI models, customer data stores, or transaction processing systems.
Credit & Lending Decision Exposure
AI models making or influencing credit decisions without documented fairness testing, bias monitoring, or explainability for adverse action notices.
Regulatory Explainability Gaps
Inability to explain AI-driven decisions to regulators, auditors, or affected customers as required by fair lending and consumer protection rules.
Customer Data Leakage
AI systems revealing customer financial records, transaction histories, or PII through response generation, RAG retrieval, or logging pipelines.
Missing Audit & Decision Trails
AI-influenced financial decisions and data access events not logged with sufficient detail for regulatory examination or dispute resolution.
Access Control & Segregation Failures
Customer-facing AI assistants or internal tools accessing financial data beyond authorized scope, or missing segregation between client accounts.
Fraud Model Evasion
Adversarial inputs or model drift causing fraud detection systems to miss patterns, leading to financial loss and compliance failures.
Cloud Infrastructure & API Exposure
Misconfigured IAM, storage, or API gateways allowing unauthorized access to AI models, customer data stores, or transaction processing systems.
Who This Is For
Lending Platforms
Companies using AI for credit scoring, underwriting, or loan decision support
Neobanks & Payment Providers
Digital banks and payment companies deploying AI for customer service, fraud detection, or risk
Wealth & Investment Tech
Platforms using AI for portfolio recommendations, market analysis, or customer advisory
RegTech & Compliance
Companies building AI-powered compliance monitoring, AML screening, or transaction surveillance
Regulatory & Framework Alignment
Evidence mapped to the frameworks that matter to financial services
EU AI Act
High-risk AI system requirements for creditworthiness assessment and financial decision automation
DORA
Digital Operational Resilience Act ICT risk management and third-party AI service oversight
Fair Lending / ECOA
Equal Credit Opportunity Act requirements for explainability and non-discrimination in AI lending
SR 11-7 / OCC Guidance
Model risk management expectations for AI systems used in banking and financial services
PCI DSS & SOC 2
Data security and access control requirements for AI systems handling payment and account data
GDPR & Data Protection
Automated decision-making rights, data minimization, and purpose limitation for AI processing
Ready to assess your fintech AI risks?
Start with a 30-minute triage call to scope your assessment and understand your system-specific risks.
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Ready to understand your AI system risks? Let us help you generate the technical evidence you need for confident decision-making.
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Telbi provides technical evidence and remediation recommendations. We do not provide legal advice, conformity assessments, certifications, or guaranteed compliance.