MAS FEAT and Veritas
Fairness, Ethics, Accountability, and Transparency principles plus the Veritas methodology for assessing AI in financial services. We embed both into your model lifecycle and supply the evidence MAS expects.
Industry
Governance-First AI for Banks and Financial Institutions
MAS FEAT and RBI aligned AI for fraud, credit risk, AML, regulatory reporting, and customer intelligence.
Overview
Banking and Financial Services, the practice in summary
Banks in Singapore, India, and across Asia are operating under regulators that have moved from principles-based guidance to detailed AI expectations. The Monetary Authority of Singapore FEAT principles, the RBI guidance on model risk, the EU AI Act high-risk classification for credit scoring, and emerging Basel-style expectations on AI in capital models all create a defining moment. Magnis AI helps banks deploy AI in fraud, credit, AML, customer intelligence, and operations with the model risk management discipline regulators expect.
Regulatory Context
The frameworks shaping AI in banking and financial services
We translate each into engineering controls and audit evidence. AI in banking and financial services ships faster when the regulator is treated as a stakeholder from day one.
RBI and SEBI
Reserve Bank of India guidance on model risk and SEBI rules on algorithmic trading and robo-advisory. We translate these into board-level policy and engineering controls.
EU AI Act for Banking
Credit scoring is classified as high risk under the EU AI Act. We help banks operating in or selling to the EU prepare conformity assessments and Article 9 to 15 controls.
Basel III and Model Risk
SR 11-7 and Basel guidance on model risk management remain the gold standard. Our framework aligns AI controls to your existing MRM function rather than creating a parallel one.
Use Cases
High-value AI use cases for banking and financial services
Each use case is shipped with the governance, validation, and monitoring needed to clear internal risk review and regulator inspection.
Fraud Detection and Prevention
Real-time payment fraud, account takeover, synthetic identity, and first-party fraud detection using ensemble models with explainability and regulator-defensible feature engineering.
Credit Risk and Underwriting
Credit scoring, early warning, and behavioural risk models with full audit trails, bias testing, and EU AI Act high-risk conformity readiness.
Anti-Money Laundering (AML)
Network analytics, alert prioritisation, and SAR drafting with LLM assistance. Designed to reduce false positives without increasing regulatory risk.
Regulatory Reporting Automation
Intelligent document processing and agentic workflows that compile MAS, RBI, and APRA returns from source systems with audit-grade lineage.
Customer Intelligence and Personalisation
Next-best-action engines, churn prediction, and personalisation with consent, fairness, and explainability built into the serving layer.
Operations and Back-Office Automation
KYC and onboarding automation, dispute handling, reconciliation, and contact centre AI with measurable cost and cycle-time outcomes.
Services we deliver in Banking and Financial Services
The Magnis AI services that fit this sector
Enterprise AI Consulting
Three integrated pillars under one practice: AI Governance and Compliance, Generative AI and LLM Consulting, and Intelligent Process Automation. Designed for banks, insurers, healthcare, government, and large operators that need AI to stand up to regulators and to the board.
ExploreAI Automation
Most SMBs lose 15 to 30 hours per week per role to repetitive work. Magnis AI builds automation that removes the work, not the team, using LLM agents, n8n, Zapier, OpenAI, and bespoke integrations with your stack.
ExploreAI Chatbot Development
Custom chatbots grounded in your own knowledge base, integrated with your CRM and helpdesk, and tuned to your tone of voice. For websites, WhatsApp, Telegram, Instagram, and voice.
ExploreMethodology
How an industry engagement runs
Four phases, each calibrated to the sector. Regulatory diagnostic comes first; deployment follows once the controls envelope is clear.
- 01
Regulatory Diagnostic
MAS FEAT, RBI, and EU AI Act gap assessment against your existing AI portfolio. Output: prioritised remediation roadmap.
- 02
Use Case Sequencing
Identify the two or three highest-value AI use cases that can ship within the regulatory envelope in six months.
- 03
Build and Govern
Iterative delivery with model risk artefacts produced alongside code. Independent validation involved early.
- 04
Scale and Sustain
Industrialise via your CoE or our managed service. Quarterly assurance against MAS and RBI expectations.
Outcomes
Measurable results
Specific, defensible outcomes from typical engagements.
- Reduction in fraud losses with explainable, regulator-defensible models
- Lower false positive rate in AML alerts and faster SAR processing
- Audit-ready model risk evidence for every production AI system
- Reduced cost-to-serve in onboarding, dispute, and reporting workflows
- Defensible position for MAS Veritas, RBI model risk, and EU AI Act conformity
Common questions about AI in banking and financial services
How does Magnis AI align with MAS FEAT and Veritas?
We translate FEAT principles into engineering controls and audit evidence. For Fairness we run protected-attribute bias tests in CI; for Ethics we run a use-case review board; for Accountability we maintain a model owner registry; for Transparency we publish model cards and customer-facing disclosures. Veritas assessments are run on credit, fraud, and customer intelligence models with documented methodology.
Are AI credit scoring models classified as high risk under the EU AI Act?
Yes. The EU AI Act Annex III classifies AI systems used to evaluate creditworthiness or establish credit score as high risk. Banks selling into the EU must implement Article 9 to 15 controls covering risk management, data governance, technical documentation, record keeping, transparency, human oversight, and accuracy. We deliver the conformity assessment package.
Can you work alongside our existing model risk management team?
Yes. We do not replace MRM. We augment it with AI-specific control libraries, evaluation methods, and tooling. Many banks already have strong MRM for traditional models and need a controlled extension to cover AI and LLM systems. We deliver that extension.
What is realistic for generative AI in banking right now?
High-value, lower-risk use cases include internal knowledge assistants, customer service drafting, SAR drafting, regulatory horizon scanning, and code modernisation. Customer-facing generative AI for advice or transactions remains a high-risk area we recommend approaching with strong guardrails, evaluation, and regulator dialogue.
How long until a fraud or AML model can go live?
From kick-off to a first model in production, three to six months. The variable is data access and validation depth. Our approach front-loads independent validation so the model passes the MRM gate the first time.
Talk to a Banking AI Specialist
Book a 45-minute AI Risk Consultation. We will assess your AI portfolio against MAS, RBI, and EU AI Act expectations and outline three priority initiatives.