Role-Based Programme
RB1683

AI-Powered Credit Risk Management

Intelligent Credit Assessment, Portfolio Analytics & Risk Monitoring

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Duration
32 Hours
Level
Advanced
Delivery
Instructor-Led
Format
Capability Development

Programme Objectives

  • Develop advanced capability to apply AI across credit assessment, borrower analysis, portfolio monitoring, early warning, review, and reporting.
  • Use AI to analyse financial statements, credit applications, borrower information, transaction patterns, industry data, collateral information, and credit documentation.
  • Apply AI-assisted analytics to identify credit-risk indicators, portfolio concentrations, deteriorating exposures, covenant concerns, and accounts requiring further review.
  • Build repeatable AI-assisted workflows for credit appraisal, document review, periodic monitoring, exception management, and management reporting.
  • Design responsible AI-enabled Credit Risk workflows with appropriate controls for explainability, fairness, confidentiality, financial accuracy, approvals, auditability, and human oversight.

Tools covered

Generative AI AssistantsAI Search & ResearchDocument AISpreadsheet & Data AnalysisCredit Risk AnalyticsFinancial Statement AnalysisPortfolio Risk AnalyticsEarly Warning AnalyticsReporting & Dashboard ToolsWorkflow Automation

Who should attend

  • Credit Risk Managers
  • Credit Risk Analysts
  • Credit Underwriters
  • Credit Officers
  • Risk Managers
  • Portfolio Risk Analysts
  • Corporate Credit Professionals
  • Commercial Credit Professionals
  • Credit Monitoring Professionals
  • Risk & Control Analysts
  • Internal Auditors
  • Banking Risk Professionals
  • Credit Administration Professionals
  • Credit Assurance Professionals
  • Credit Risk Team Leads

Prerequisites & Participant Readiness

  • Working knowledge of credit risk, lending, underwriting, financial analysis, banking, or risk management
  • Familiarity with credit applications, financial statements, borrower profiles, collateral, covenants, or portfolio monitoring
  • Basic proficiency with spreadsheets, financial data, documents, and workplace productivity applications
  • No previous AI course attendance required
  • No programming background required

TOC Modules

Concepts
  • Understanding Generative AI, analytical AI, Document AI, and credit-risk analytics
  • Mapping AI opportunities across credit assessment, underwriting support, monitoring, and portfolio management
  • Understanding AI assistance versus authorised credit judgement and approval
  • Recognising explainability, bias, data-quality, and model-risk concerns
Practical activities
  • Mapping a typical Credit Risk lifecycle and identifying AI opportunities
  • Comparing manual and AI-assisted credit-risk activities
  • Creating an AI opportunity matrix based on value, risk, and feasibility

Scenarios

Borrower Application to Credit Risk Assessment

Borrower Profile → Financial Analysis → Industry Review → Credit Rating → Collateral Assessment → Covenant Review → Risk Summary → Approval Pack

Participants use AI-assisted methods to analyse a simulated credit application, review borrower and financial information, identify key risk drivers, structure a credit assessment, and prepare an approval-ready summary for authorised decision-makers.

Credit Portfolio to Early Warning & Risk Monitoring Workflow

Portfolio Data + Financial Trends + Payment Behaviour + Covenant Status + External Signals → AI Analysis → Early Warnings → Priority Accounts → Follow-Up → Dashboard → Management Review

Participants design an AI-enabled credit-monitoring workflow that identifies emerging borrower deterioration, portfolio concentrations, overdue reviews, and exceptions while retaining all credit-rating and lending decisions with authorised professionals.

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