Introduction
The programme “The Role of Artificial Intelligence in Banking Risk Analysis” develops professional practice in Risk and Credit. It connects the subject with workplace challenges by examining existing evidence and practices and choosing methods appropriate to the organization and its available resources.
The learning progresses from credit analysis and banking risk to financial statement and report analysis, with practical work on artificial intelligence applications and controls, banking service and operations management, risk assessment, controls, and response. Participants prepare outputs to support workplace application and review the quality and results of implementation.
Learning objectives
- Prepare a credit analysis and risk scenario showing repayment capacity and controls in an application related to “The Role of Artificial Intelligence in Banking Risk Analysis”.
- Design an AI use case with verification criteria and human review.
- Analyze a banking process and connect service quality to operational risks and controls.
- Prepare a risk register connecting causes, consequences, controls, and treatment responsibilities.
- Interpret performance, financial position, and cash flows using documented analysis with clear limitations.
Learning areas
1. Credit Analysis and Banking Risk
- Analyze the financing purpose, repayment sources, and nature of the business, and review the financial and nonfinancial data needed to assess the customer.
- Assess cash flows, debt servicing capacity, and collateral, distinguishing the primary repayment source from supplementary protections.
- Classify relevant credit, market, liquidity, and operational risks, considering the applicable regulatory framework and internal policies.
- Test the effects of changes in revenues, prices, and maturities on repayment capacity, and define conditions and monitoring requirements addressing the identified risks.
- Prepare a documented recommendation covering limits, assumptions, information gaps, and warning indicators without turning the training case into an actual financing decision. The application examines a case related to “The Role of Artificial Intelligence in Banking Risk Analysis”.
2. Artificial Intelligence Applications and Controls
- Identify a task that AI can support, and separate inputs and human decisions from outputs produced by the tool.
- Prepare suitable test data and examples while respecting confidentiality, privacy, and permissions, and use de-identified training exercise data.
- Write clear instructions covering the task, context, constraints, and output format, and compare multiple formulations using a fixed set of examples.
- Check outputs for errors, bias, and unsupported information, and identify cases that require specialist review.
- Assess the feasibility of use in terms of quality, time, cost, and risk, and document controls for approval, monitoring, and stopping when failures occur.
3. Banking Service and Operations Management
- Define the customer journey, banking product, and service delivery points, distinguishing front-office service, processing, and control.
- Analyze relationship opening, verification, documentation, and authority requirements, referring to applicable local regulatory instructions.
- Review the risks of errors, delays, fraud, and service disruption, linking them to preventive and detective controls and clear responsibilities.
- Design an operational improvement balancing service speed, customer data protection, financial control, and compliance.
- Select indicators for time, accuracy, complaints, and continuity, and prepare a scenario for addressing a failure and evaluating the responses of the parties involved.
4. Risk Assessment, Controls, and Response
- Define the work context, objectives, and assessment boundaries, and distinguish the risk source, potential event, and effects on results.
- Gather risks from processes, data, past incidents, and interviews with responsible staff, and document causes and existing controls.
- Estimate likelihood and impact using stated criteria, and review control effectiveness to determine residual risk levels.
- Select a response proportionate to the risks, resources, and authority, and specify the owner, deadline, and early warning indicator.
- Test a scenario involving changing circumstances, update the risk register, and present material risks and required decisions to the appropriate authority.
5. Financial Statement and Report Analysis
- Read the relationships between the statements of financial position, performance, and cash flows and the notes, identifying the effects of accounting policies and estimates.
- Perform horizontal and vertical analysis and compare profitability, liquidity, efficiency, and leverage indicators, considering the nature and size of the business.
- Examine earnings quality, cash flows, and nonrecurring items, and identify differences between accounting results and actual liquidity.
- Test the sensitivity of results to changes in sales, costs, collections, and financing, stating the assumptions and constraints in each scenario.
- Write a financial report linking indicators to management's question and available alternatives, presenting risks and additional data needed for the decision.
Practical outputs
- A credit memo, risk register, and early warning indicators.
- A use case brief and a test template for the quality of AI outputs.
- A banking process map, control matrix, and service quality indicators.
- A risk register, control matrix, and prioritized treatment plan.
- A comparative financial analysis report and a memo of findings and assumptions.
Who should attend
- Practitioners, specialists and supervisors whose work relates to the programme subject.
- Managers and teams working in risk and credit, according to their practical responsibilities.
- Professionals responsible for applying the outputs of “The Role of Artificial Intelligence in Banking Risk Analysis” within their organizations.
Prerequisites
- Working familiarity with the programme field and the ability to discuss a professional case or challenge.
- Confirm the suitability of your experience and any specialist prerequisites with the Union before participation.
Conclusion
The programme “The Role of Artificial Intelligence in Banking Risk Analysis” brings together concepts, case review and a practical application. Participants prepare steps that can be adapted to their responsibilities and organizational resources, with an indicator to follow application and review results after returning to work.
Participation arrangements and any special requests are specified in the confirmation issued by the Union.