Asset-Liability Management Auditing Training Course

Course Introduction

Asset-liability management is a core discipline for managing the balance sheet, financial risks, liquidity, funding, earnings, and capital within banks and other financial institutions. This course provides an audit-focused framework for evaluating ALM governance, balance sheet risk management, interest rate risk, liquidity risk, funding structures, investment portfolios, and the controls supporting sustainable financial performance.

The course examines the complete ALM lifecycle, beginning with balance sheet strategy, risk identification, and risk appetite and continuing through measurement, modeling, limit setting, stress testing, scenario analysis, hedging, monitoring, and management reporting. Participants will develop practical techniques for determining whether ALM frameworks appropriately identify and control material financial risks.

Particular attention is given to structural interest rate risk, repricing risk, yield curve risk, basis risk, optionality, liquidity risk, funding concentration, behavioral assumptions, and balance sheet mismatches. Participants will learn how auditors can assess the reliability of ALM measurements and challenge assumptions concerning deposit behavior, loan prepayments, early withdrawals, non-maturity deposits, funding stability, and other balance sheet characteristics.

The course also examines ALM governance and decision-making structures, including asset-liability committees, treasury functions, risk management, finance, business units, and independent assurance. Participants will assess policies, limits, delegated authorities, management information, escalation arrangements, model governance, and the interaction between ALM strategy and broader risk appetite.

Modern ALM environments increasingly rely on integrated balance sheet systems, risk engines, behavioral models, forecasting tools, market data, scenario analysis, and automated dashboards. The course therefore incorporates data governance, model risk, technology controls, cybersecurity, system interfaces, automation, artificial intelligence, and continuous monitoring considerations relevant to ALM assurance.

Through practical audit scenarios and an integrated case study, participants will learn to assess ALM governance, test balance sheet data, evaluate risk models, analyze stress scenarios, investigate limit breaches, review funding and liquidity controls, and develop evidence-based audit findings. The course strengthens professional skepticism and provides a structured methodology for auditing complex asset-liability management frameworks.

Duration

10 days

Who Should Attend

• Internal auditors responsible for ALM, treasury, liquidity, market risk, or banking audits.

• External auditors involved in asset-liability management and financial risk assurance.

• Asset-liability management professionals responsible for balance sheet risk management.

• Treasury professionals involved in funding, liquidity, investments, and interest rate risk.

• Banking risk professionals responsible for structural balance sheet risk.

• Liquidity risk managers monitoring funding, liquidity buffers, and contingency arrangements.

• Market risk professionals assessing interest rate and balance sheet exposures.

• Financial controllers responsible for balance sheet reporting and financial risk information.

• Finance professionals involved in balance sheet strategy, forecasting, and financial planning.

• Compliance professionals monitoring ALM governance, policies, and regulatory requirements.

• Audit managers and supervisors planning and reviewing ALM assurance engagements.

• Model risk professionals supporting behavioral, interest rate, liquidity, and balance sheet models.

• Technology and data professionals supporting ALM systems and risk analytics.

• Banking executives and risk committee professionals seeking stronger understanding of ALM assurance.

• Professionals seeking advanced practical skills in auditing asset-liability management frameworks.

Course Objectives

• Develop a comprehensive understanding of asset-liability management and its relationship with balance sheet strategy, financial risk, earnings, liquidity, funding, and capital.

• Evaluate ALM governance structures, asset-liability committees, risk appetite, policies, delegated authorities, reporting lines, and management accountability.

• Apply risk-based audit techniques to identify structural interest rate, liquidity, funding, repricing, basis, yield curve, and optionality risks.

• Assess the completeness and accuracy of balance sheet data used for ALM analysis, including asset, liability, maturity, repricing, cash flow, and behavioral information.

• Evaluate ALM measurement methodologies, including gap analysis, duration, sensitivity measures, economic value analysis, earnings sensitivity, and liquidity metrics.

• Examine behavioral assumptions relating to deposits, prepayments, early withdrawals, loan maturities, customer behavior, and other balance sheet characteristics.

• Audit funding structures, liquidity buffers, concentration risks, maturity profiles, contingency funding arrangements, and access to alternative funding sources.

• Evaluate ALM limits, monitoring, breach management, escalation, management reporting, and alignment between risk exposures and approved risk appetite.

• Assess stress testing, scenario analysis, reverse stress testing, and management responses to severe interest rate, liquidity, funding, and balance sheet conditions.

• Evaluate ALM models, assumptions, data inputs, validation, change management, model limitations, and independent challenge from an audit perspective.

• Use data analytics and technology-enabled audit procedures to identify balance sheet mismatches, unusual trends, concentration risks, model anomalies, and control exceptions.

• Develop integrated audit findings and recommendations addressing ALM governance, risk exposure, root causes, financial impacts, control weaknesses, and sustainable remediation.

Comprehensive Course Outline

Module 1: Foundations of Asset-Liability Management Auditing

• Examine the purpose of ALM and its relationship with balance sheet strategy, liquidity, funding, interest rate risk, earnings, and capital management.

• Understand the ALM lifecycle from balance sheet positioning and risk identification through measurement, limits, stress testing, monitoring, and reporting.

• Identify major audit risks associated with asset-liability mismatches, behavioral assumptions, liquidity pressures, funding concentrations, and interest rate exposures.

• Develop a risk-based ALM audit framework covering governance, data, models, risk measurement, controls, reporting, and assurance evidence.

Module 2: ALM Governance, Strategy and Risk Appetite

• Evaluate board and management oversight, asset-liability committees, treasury responsibilities, risk functions, policies, and decision-making structures.

• Assess the alignment between balance sheet strategy, business plans, risk appetite, capital objectives, liquidity requirements, and ALM policies.

• Examine delegated authorities, committee mandates, escalation processes, reporting responsibilities, and segregation of duties across ALM activities.

• Identify governance weaknesses that could result in excessive risk-taking, ineffective challenge, inadequate monitoring, or delayed management action.

Module 3: Balance Sheet Structure and ALM Risk Identification

• Examine the composition of assets, liabilities, capital, off-balance-sheet items, and contingent exposures relevant to ALM risk.

• Evaluate methods for mapping balance sheet items by maturity, repricing date, cash flow characteristics, currency, product, and behavioral profile.

• Assess controls over balance sheet data completeness, classification, aggregation, and reconciliation with financial and operational records.

• Identify structural mismatches and concentrations that may create material interest rate, liquidity, funding, or earnings risks.

Module 4: Interest Rate Risk in the Banking Book and ALM

• Examine repricing risk, yield curve risk, basis risk, optionality, duration, and other structural interest rate exposures.

• Evaluate interest rate gap analysis, earnings sensitivity, economic value measures, duration-based metrics, and other ALM risk indicators.

• Assess assumptions concerning rate resets, non-maturity deposits, prepayments, early withdrawals, and other behavioral characteristics.

• Identify measurement weaknesses that may result in understated interest rate exposure or inappropriate balance sheet risk decisions.

Module 5: Liquidity Risk and Funding Management

• Audit liquidity risk identification, liquidity buffers, cash flow forecasting, funding requirements, and liquidity risk limits.

• Evaluate funding diversification, maturity profiles, concentration risks, stable funding sources, wholesale funding, and contingency arrangements.

• Examine controls over liquidity reporting, cash flow projections, liquidity stress indicators, and escalation of emerging funding pressures.

• Assess whether liquidity information is complete, accurate, timely, independently reviewed, and appropriately integrated into ALM decisions.

Module 6: Deposit, Loan and Customer Behavioral Assumptions

• Examine behavioral models for non-maturity deposits, loan prepayments, early withdrawals, deposit stability, and customer rate sensitivity.

• Evaluate historical data, segmentation, assumptions, model methodologies, validation, and management overlays used in behavioral analysis.

• Assess whether behavioral assumptions are periodically reviewed against actual customer behavior and changing market conditions.

• Identify risks arising from outdated assumptions, insufficient segmentation, model limitations, inappropriate overrides, or weak validation.

Module 7: ALM Funding, Investments and Balance Sheet Optimization

• Examine funding strategies involving deposits, wholesale funding, interbank markets, debt issuance, and other sources of balance sheet funding.

• Evaluate investment portfolio composition, duration, liquidity, concentration, yield, and contribution to overall ALM objectives.

• Assess controls over funding and investment decisions, approval processes, pricing, maturity management, and risk limits.

• Identify risks arising from excessive funding concentrations, maturity mismatches, inefficient liquidity positioning, or inappropriate investment strategies.

Module 8: ALM Limits, Monitoring and Escalation

• Audit ALM limits covering interest rate sensitivity, liquidity gaps, funding concentrations, duration, earnings sensitivity, and other balance sheet risk measures.

• Evaluate automated and manual monitoring processes for limit utilization, breaches, temporary excesses, overrides, and approvals.

• Examine management escalation procedures for material risk movements, repeated breaches, deteriorating liquidity, or unexpected balance sheet changes.

• Assess whether ALM reporting provides management and risk committees with accurate, timely, complete, and decision-useful information.

Module 9: ALM Stress Testing and Scenario Analysis

• Evaluate stress-testing frameworks covering severe interest rate movements, liquidity shocks, funding withdrawals, market disruptions, and balance sheet deterioration.

• Assess historical and hypothetical scenarios, assumptions, sensitivity ranges, management overlays, and scenario governance.

• Examine how stress results influence funding strategies, liquidity buffers, hedging decisions, balance sheet composition, and contingency planning.

• Identify weaknesses in stress-testing data, models, assumptions, management challenge, documentation, and escalation processes.

Module 10: ALM Models, Data and Model Risk Management

• Evaluate ALM models used for interest rate risk, liquidity forecasting, behavioral assumptions, cash flow projections, and balance sheet analysis.

• Assess model inputs, data quality, assumptions, methodology, validation, performance monitoring, change management, and independent review.

• Examine controls over model inventories, model ownership, documentation, approvals, limitations, overrides, and model risk reporting.

• Identify risks arising from model uncertainty, poor data, inappropriate assumptions, implementation errors, outdated models, and inadequate validation.

Module 11: Treasury, Hedging and ALM Risk Mitigation

• Examine the relationship between treasury activities and ALM objectives, including funding, liquidity management, investments, and interest rate hedging.

• Evaluate derivative strategies such as swaps, futures, options, and other instruments used to manage structural balance sheet exposures.

• Assess controls over hedge authorization, execution, valuation, effectiveness, documentation, settlement, accounting, and counterparty exposure.

• Identify risks arising from ineffective hedges, mismatched exposures, unauthorized transactions, valuation errors, and inadequate hedge monitoring.

Module 12: ALM Accounting, Financial Reporting and Reconciliations

• Audit reconciliations between ALM systems, core banking platforms, treasury systems, accounting records, and financial reporting data.

• Examine accounting implications of interest income, funding costs, investment valuations, derivatives, hedge relationships, and balance sheet adjustments.

• Evaluate controls over accruals, valuation adjustments, manual journals, period-end processing, and financial reporting disclosures.

• Identify risks arising from incomplete data, reconciliation breaks, incorrect classifications, delayed adjustments, and inconsistent ALM reporting.

Module 13: ALM Technology, Data Governance and Automated Controls

• Evaluate integrated ALM platforms, balance sheet systems, risk engines, forecasting tools, market data feeds, and automated reporting processes.

• Assess access controls, user privileges, system configurations, interface controls, change management, automated calculations, and exception reporting.

• Examine data lineage from core transactions through ALM calculations, risk reports, management dashboards, accounting, and regulatory information.

• Evaluate risks involving cloud platforms, APIs, automation, artificial intelligence, cybersecurity, system outages, and data integrity.

Module 14: ALM Analytics and Continuous Assurance

• Apply data analytics to identify balance sheet mismatches, maturity concentrations, unusual trends, liquidity movements, repricing exposures, and reporting anomalies.

• Compare ALM risk measures with underlying balance sheet populations, historical trends, forecasts, financial records, and market conditions.

• Develop continuous monitoring approaches using dashboards, automated alerts, key risk indicators, trend analysis, and exception reporting.

• Evaluate how advanced analytics can improve audit coverage, risk assessment, model testing, exception investigation, and early detection of ALM weaknesses.

Module 15: Emerging ALM Risks, Resilience and Advanced Assurance

• Examine emerging ALM risks from digital banking, changing customer behavior, new funding models, real-time payments, artificial intelligence, and automated balance sheet management.

• Evaluate the impact of monetary policy changes, market volatility, geopolitical events, liquidity disruptions, and structural economic changes on ALM risk.

• Assess operational resilience, third-party dependencies, cybersecurity, data risks, model risk, and technology concentration affecting ALM processes.

• Develop advanced audit responses incorporating predictive analytics, scenario testing, continuous assurance, specialized reviews, and enhanced management challenge.

Module 16: Integrated Asset-Liability Management Audit Case Study

• Conduct an end-to-end audit of an ALM framework covering balance sheet data, interest rate risk, liquidity, funding, behavioral assumptions, models, limits, and stress testing.

• Analyze balance sheet populations, repricing schedules, liquidity reports, funding information, behavioral models, stress results, and management explanations.

• Develop integrated findings connecting ALM control weaknesses to root causes, financial and risk impacts, governance deficiencies, and remediation requirements.

• Prepare a comprehensive ALM audit report and management presentation demonstrating professional skepticism, evidence evaluation, issue prioritization, and practical corrective actions.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue 

The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.

Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment:

Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.