About the Course

Banks and financial institutions are expanding the use of AI in credit assessment, fraud detection, customer service, financial-crime monitoring, collections, forecasting and operational decision support. These applications can improve speed and analytical depth, but they also raise significant concerns around model risk, explainability, fairness, customer outcomes, cybersecurity, data governance and third-party dependencies.

This programme provides banking and risk professionals with a practical, non-engineering view of AI applications and controls. Participants examine AI use in credit underwriting, portfolio monitoring, early-warning systems, fraud analytics, AML/KYC support, customer intelligence, financial forecasting, risk reporting and operational automation. The course also addresses model governance, explainability, human decision rights, data quality, third-party risk and regulatory expectations.

Participants work through lending and risk cases to develop an AI opportunity map, model-risk checklist, AI-assisted credit analysis workflow, fraud-risk scenario, control framework and banking AI adoption roadmap. The emphasis is on using AI to improve financial decisions while preserving prudent risk management and defensible human oversight.

Target Participants

This course is designed for bankers, credit analysts, relationship managers, risk managers, internal-control staff, compliance officers, financial-crime teams, finance professionals, treasury staff, internal auditors, data and analytics teams, digital-banking managers and regulators involved in financial-sector AI adoption or oversight.

What You Will Learn

By the end of this course the participants will be able to:

• Evaluate AI use cases across banking, credit and financial-risk functions.
• Apply AI-assisted techniques to credit analysis and portfolio monitoring.
• Assess AI applications in fraud, AML/KYC and operational-risk management.
• Interpret explainability, fairness and model-risk requirements for financial decisions.
• Evaluate data-governance and third-party risks associated with banking AI.
• Design human-review controls for AI-supported financial decisions.
• Develop management reporting for AI performance and risk.
• Prepare a responsible AI adoption roadmap for a financial institution.

Course Duration

• Classroom – 5 Days
• Online – 7 Days

Course Outline

Banking AI Foundations

• AI applications across financial services
• Generative versus predictive AI
• Banking value drivers
• Financial-sector risk considerations
• Human accountability in regulated decisions

Credit Analysis

• AI-supported borrower information review
• Financial-statement interpretation support
• Qualitative risk-factor extraction
• Credit memo drafting assistance
• Verification of AI-supported conclusions

Credit Scoring

• Predictive scoring concepts
• Feature selection considerations
• Alternative data opportunities
• Bias and fairness risks
• Explainability for credit decisions

Portfolio Monitoring

• Early-warning indicators
• Risk migration analysis
• Portfolio segmentation
• Concentration monitoring
• AI-supported watchlist prioritization

Fraud Intelligence

• Anomaly-detection concepts
• Transaction-pattern analysis
• Fraud typology support
• Alert prioritization
• Human investigation controls

Financial Crime

• KYC information synthesis
• Sanctions-screening support
• AML alert triage
• Adverse-media review
• False-positive and explainability considerations

Financial Forecasting

• Forecast-support use cases
• Scenario generation
• Variance explanation
• Liquidity and cash-flow insight
• Limits of AI-generated forecasts

Model Risk

• Model inventory and classification
• Validation concepts
• Performance monitoring
• Change control
• Documentation and challenge processes

AI Controls

• Data quality and lineage
• Third-party AI risk
• Cybersecurity and privacy
• Human override and escalation
• Incident and contingency planning

Banking AI Roadmap

• Use-case prioritization
• Risk-based implementation gates
• Management reporting
• Capability and governance needs
• Phased adoption plan

Practical Outputs

• Banking AI opportunity map
• AI-assisted credit review template
• Credit-model governance checklist
• Fraud/AML AI control matrix
• Human-review decision protocol
• AI risk reporting template
• Banking AI adoption roadmap

Training Approach

The course uses banking cases, sample financial statements, credit scenarios, fraud typologies, model-governance exercises and risk-control workshops. Participants compare AI-assisted analysis with established credit and risk disciplines, with emphasis on explainability, evidence, customer outcomes and escalation of uncertain results.

Organizational Benefits

• Faster analysis of credit and risk information
• Improved portfolio and fraud-risk insight
• Better governance of AI-supported financial decisions
• Stronger attention to explainability and fairness
• Improved oversight of third-party AI providers
• Clearer management reporting on AI opportunities and risks

Certification

Participants who successfully complete this course will receive a Certificate of Course Completion from Devimpact Institute, recognizing the knowledge, practical skills and competencies developed through the programme.

Tailor-Made Course

This course can be customized and delivered as an in-house programme to address the specific policies, systems, sector context and capacity-development priorities of an organization. The course duration, modules, case studies and practical exercises may be adjusted following a training needs assessment.