Role-Based Programme
RB1682

AI-Powered Credit Risk

Smarter Credit Assessment, Portfolio Monitoring & Risk Decision Support

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Duration
16 Hours
Level
Intermediate
Delivery
Instructor-Led
Format
Capability Training

Programme Objectives

  • Apply AI across Credit Risk activities including borrower assessment, financial analysis, credit appraisal, risk scoring, monitoring, and portfolio review.
  • Use AI-assisted techniques to analyse financial statements, borrower information, repayment behaviour, credit documents, and portfolio data more efficiently.
  • Develop structured workflows for credit assessment, risk identification, limit review, early-warning monitoring, exception management, and reporting.
  • Analyse borrower and portfolio information to identify deterioration signals, concentration risks, overdue exposures, and areas requiring deeper review.
  • Apply responsible AI practices covering financial confidentiality, model limitations, data quality, explainability, regulatory expectations, and human judgement.

Tools covered

Generative AI AssistantsCredit Risk AnalyticsFinancial Statement AnalysisSpreadsheet AnalysisDocument IntelligencePortfolio Risk AnalysisEarly Warning IndicatorsRisk ScoringReporting AssistanceWorkflow Automation

Who should attend

  • Credit Risk Analysts
  • Credit Risk Managers
  • Credit Analysts
  • Risk Management Professionals
  • Underwriting Professionals
  • Portfolio Risk Analysts
  • Credit Officers
  • Corporate Credit Professionals
  • Retail Credit Professionals
  • Risk Analysts
  • Internal Auditors
  • Risk Assurance Professionals
  • Credit Monitoring Professionals
  • Risk & Internal Audit Team Leads

Prerequisites & Participant Readiness

  • Working knowledge of credit, lending, risk, finance, or underwriting activities
  • Familiarity with financial statements, borrower information, credit proposals, or portfolio monitoring is helpful
  • Basic spreadsheet and financial-analysis skills
  • Basic awareness of Generative AI is helpful
  • No programming knowledge required

TOC Modules

Concepts
  • Understanding Generative AI, analytics, predictive techniques, and automation in Credit Risk
  • Identifying AI applications across credit assessment, underwriting support, monitoring, and reporting
  • Understanding AI assistance versus authorised credit judgement and approval
  • Recognising model, bias, data-quality, and explainability risks in AI-assisted credit decisions
Practical activities
  • Mapping the Credit Risk lifecycle to AI-assisted activities
  • Identifying manual analytical and documentation activities suitable for AI support
  • Comparing traditional and AI-assisted credit workflows

Scenarios

Borrower Assessment to Credit Recommendation

Borrower Information → Financial Analysis → AI-Assisted Risk Assessment → Rating Factors → Repayment Capacity → Key Risks → Credit Recommendation

Participants analyse a sample borrower, review financial performance and repayment capacity, identify key risks, and prepare a structured credit recommendation while retaining final credit judgement with authorised decision-makers.

Credit Portfolio to Early-Warning Action Plan

Portfolio Data → AI-Assisted Analysis → Concentrations → Deterioration Signals → High-Risk Accounts → Monitoring Actions → Management Report

Participants analyse sample portfolio information to identify concentrations, emerging deterioration, and accounts requiring closer monitoring, then prepare a management-ready Credit Risk action plan.

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