Case studies in
AI & risk analytics
Network Link Analytics
Graph-based framework mapping mule account clusters, multi-hop relationships, and suspicious transaction communities across banking data.
CrediSense
Microsoft Copilot Studio assistant grounded in official policy documents — enabling consistent, instant credit policy navigation.
CrediNote
Structured GenAI workflow for credit memo generation — human-in-the-loop controls, source-grounded drafts, tone standardisation.
CrediSignal
Automated pipeline collecting, matching, summarising and risk-tagging adverse news for banking client portfolios in near real-time.
Fraud Detection Models
End-to-end origination and behavioural fraud models with explainability, governance documentation, and false positive management.
Deep domain.
Full stack.
Delivered.
From raw data pipelines and graph models to GenAI assistants and executive dashboards — every solution built for regulated banking environments.
Learn moreFinancial Crime Analytics
Mule account detection, fraud network mapping, AML workflow intelligence, and suspicious community identification.
GenAI Engineering
LLM assistants, RAG architecture, Microsoft Copilot Studio, prompt engineering, and governed enterprise AI deployment.
Fraud Detection Models
Origination risk scoring, behavioural fraud signals, false positive management, and full model governance documentation.
Risk Intelligence Pipelines
Adverse news monitoring, entity matching, LLM summarisation, risk taxonomy, and real-time alerting for banking portfolios.
Data Science Training
Structured programmes in Python, ML, GenAI, and Azure analytics for 200+ banking and enterprise professionals.
Building
capability
in others.
I design and deliver AI and data science training for banking teams — from Python and machine learning to GenAI strategy and enterprise analytics platforms.
Training Programs