Model Online / On-Site
Duration
Modules 5 modules

Course Objective & Scope

Banking and financial services combine classical machine learning—credit scoring, real-time fraud detection, early-warning systems and next-best-action models—with generative AI copilots across branches, contact centres and back-office operations. This programme gives business and risk leaders an end-to-end view of how predictive analytics and LLM/agent architectures drive P&L, risk performance and operational efficiency.

Participants build a balanced AI portfolio map across retail, SME, commercial banking, treasury and operations, along with a model-risk checklist and a business-case template.

Who Should Attend?

  • Business unit, product and channel managers in banks and financial institutions
  • Credit risk, collections, CRM/analytical marketing and treasury teams
  • Digital banking, data/analytics transformation and innovation leads
  • Model risk, compliance, internal audit and technology management professionals

Key Takeaways

  • Combine predictive ML models (scoring, churn, fraud) with generative AI across banking workflows
  • Evaluate AI-assisted decision flows in underwriting, early-warning systems (EWS) and collections
  • Design hyper-personalisation and real-time next-best-action journeys for retail and SME segments
  • Apply explainability (XAI), fairness, drift monitoring and model-risk governance (SR 11-7 / BDDK)
  • Build a business case quantifying P&L impact, cost and risk for a banking AI initiative

Curriculum

AI Architecture in Modern Banking: From Predictive ML to GenAI
  • How scoring/optimisation models and LLM/agent systems complement each other
  • Value drivers across retail, SME, corporate banking and treasury
  • Metrics for revenue uplift, cost-of-risk (CoR) reduction and cost-to-income ratio
Credit Risk, Early Warning (EWS) and Collections Analytics
  • Application and behavioural scoring with machine learning and transaction data
  • Early-warning systems combining financial ratios with sector and news signals
  • Delinquency segmentation and channel/timing optimisation in collections
Customer Value Management (CVM) and Digital Banking CX
  • Real-time Next-Best-Action (NBA), cross-sell and churn prevention
  • Intelligent financial-wellness nudges and conversational banking in mobile apps
  • Unified client insight briefs for branch staff and relationship managers
Fraud Prevention, AML and Back-Office Automation
  • Anomaly and graph approaches to card, instant-payment and digital-channel fraud
  • Reducing false positives in AML/KYC alerts and summarising analyst case files
  • Intelligent document processing across trade finance and back-office operations
Model Risk Management (MRM), Explainability and Roadmap
  • Explainable AI (SHAP/LIME) and fairness checks in credit and risk decisions
  • Model validation, drift monitoring and human-in-the-loop controls
  • Prioritising the bank's AI portfolio and quantifying ROI

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