AI Strategy and Requirements Engineering for Fintech
From fraud detection systems to KYC automation — Vikrant Chauhan delivers AI strategy and precise requirements documentation for regulated financial products.
6+
Years in regulated industry requirements
CBAP®
Gold-standard BA certification
2–4 weeks
AI Readiness Audit for fintech
Fintech AI projects fail not because of the technology, but because requirements are unclear and regulatory obligations are underspecified. Vikrant Chauhan brings CBAP® certified business analysis to fintech teams — ensuring AI initiatives are grounded in accurate process documentation, compliant requirements, and validated use cases before engineering begins.
Key Challenges in Fintech AI
Regulatory documentation requirements
FCA, PRA, GDPR, AML, and PSD2 regulations require AI systems to be explainable, auditable, and documented to a specific standard. Requirements documents must hold up to regulatory scrutiny.
Data quality and model risk
Fintech AI models depend on high-quality transactional data. AI readiness assessment must include honest evaluation of data lineage, completeness, and model risk management frameworks.
Legacy system integration
Most fintech AI initiatives require integration with core banking systems, card processors, or third-party data APIs. Requirements must specify integration contracts precisely.
Explainability requirements
Regulatory guidance increasingly demands that AI credit decisions, fraud flags, and risk scores are explainable. Requirements must define explainability criteria upfront.
Top AI Use Cases for Fintech
Fraud Detection Systems
Use Case 1Real-time transaction monitoring and anomaly detection. Business analysis scopes the rules engine, ML model inputs, threshold logic, and case management workflow.
KYC Automation
Use Case 2AI-assisted identity verification, document processing, and risk screening. Requirements cover OCR, biometric matching, sanctions screening, and AML workflows.
Credit Risk Modelling
Use Case 3Alternative data credit scoring and risk stratification. Business analysis defines the scoring model inputs, decision thresholds, and fair lending compliance requirements.
Regulatory Reporting Automation
Use Case 4Automate FCA/PRA reporting data extraction, transformation, and submission. Requirements cover data mapping, validation rules, and submission format specifications.
How We Work in Fintech
Regulatory Context Review
Map applicable regulations (FCA, GDPR, AML) to the specific AI use case before any requirements are written.
Data and Model Risk Assessment
Evaluate data quality, lineage, and model risk management readiness — identifying gaps that would block regulatory approval.
Use Case Prioritisation
Score AI opportunities by regulatory complexity, data readiness, business impact, and implementation effort using RICE framework.
Compliant Requirements Documentation
Produce BRD/FRS with explainability requirements, audit trail specifications, and regulatory compliance criteria integrated into acceptance criteria.
Related Resources
Frequently Asked Questions
Ready to start?
Book a free Fintech AI discovery call
30 minutes. Vikrant will assess your fintech AI readiness and recommend the right starting point — no pitch, no obligation.