Course Introduction
Risk Data Aggregation and Regulatory Reporting Training Course provides a practical framework for transforming fragmented risk information into accurate, complete, timely, adaptable, and decision-useful regulatory and management reporting. The course is designed around the principles of effective risk data aggregation and risk reporting, with particular emphasis on governance, data architecture, data quality, aggregation processes, reporting controls, regulatory submissions, and management information. The Basel Committee’s current framework identifies risk data aggregation and internal risk reporting as important capabilities for risk management and decision-making, particularly for systemically important banks.
The program develops the capabilities required to collect, integrate, reconcile, validate, aggregate, and report risk information across business lines, legal entities, products, portfolios, counterparties, jurisdictions, and risk types. Participants examine how credit, market, liquidity, operational, counterparty, concentration, capital, and other material risk data can be brought together into consistent analytical structures. The course emphasizes the importance of common data definitions, identifiers, taxonomies, metadata, lineage, and ownership because the Basel Committee specifically highlights integrated data architecture and unified naming conventions as important components of effective risk data aggregation.
A major component of the course is risk data quality management. Participants learn how to assess accuracy, completeness, timeliness, integrity, consistency, reconciliation, and traceability across risk datasets and reporting processes. They develop practical techniques for identifying missing records, duplicate exposures, inconsistent classifications, broken mappings, unexplained movements, stale data, aggregation errors, and differences between risk and finance information. The Basel Committee states that risk data should be accurate and reliable, reconciled to appropriate source systems, and aggregated with controls designed to minimize errors.
The course also examines regulatory reporting processes and the relationship between source data, risk calculations, regulatory templates, validation rules, management reports, and supervisory submissions. Participants learn how to establish controlled reporting pipelines that preserve data lineage from source systems through transformation and aggregation to final reports. They explore regulatory reporting calendars, reporting ownership, submission controls, reconciliations, validation checks, exception management, sign-off procedures, change management, and evidence retention. The Basel Committee's framework links strong aggregation capabilities directly to high-quality risk reporting, emphasizing that reliable reports depend on sound infrastructure and governance.
Particular attention is given to the challenges of implementing risk data aggregation across complex financial institutions. The Basel Committee's January 2026 update identifies ongoing challenges including changing business environments, technological developments, data lineage, cross-border implementation, governance, and the use of emerging technologies. It also notes that some banks are incorporating risk data aggregation into broader enterprise-wide data governance frameworks. Participants therefore learn how to assess current-state capabilities, identify control gaps, establish remediation priorities, and design scalable processes that can accommodate organizational changes, new products, acquisitions, regulatory changes, and evolving reporting requirements.
By the end of the program, participants will be able to design and operate a structured risk data aggregation and regulatory reporting framework that supports reliable risk measurement, regulatory compliance, management decision-making, and supervisory engagement. The course culminates in an integrated capstone in which participants develop a risk-data architecture, establish quality controls, aggregate multiple risk datasets, perform reconciliations, design regulatory reporting outputs, investigate exceptions, and present management-level risk information. The emphasis throughout is on creating transparent, auditable, repeatable, and resilient reporting processes capable of supporting both normal operations and periods of financial stress.
Duration
5 days
Who Should Attend
-
Risk professionals responsible for enterprise risk data aggregation and risk reporting.
-
Regulatory reporting professionals preparing prudential and supervisory submissions.
-
Risk data analysts working with credit, market, liquidity, operational, and enterprise risk datasets.
-
Risk managers responsible for consolidating risk information across business units and legal entities.
-
Data governance professionals responsible for risk data quality, ownership, lineage, and standards.
-
Finance and regulatory finance professionals involved in reconciliations between financial and risk information.
-
Business intelligence and data analytics professionals supporting risk reporting and regulatory reporting platforms.
-
Banking compliance professionals responsible for regulatory reporting governance and reporting controls.
-
Internal auditors reviewing risk data aggregation, reporting processes, and regulatory compliance controls.
-
IT and data architecture professionals designing infrastructure for enterprise risk information management.
-
Model risk and quantitative risk professionals working with risk models, exposure data, and regulatory calculations.
-
Senior finance, risk, compliance, and reporting professionals responsible for management and regulatory risk information.
Course Objectives
-
Develop a comprehensive understanding of risk data aggregation, regulatory reporting, data governance, and risk information management within financial institutions.
-
Apply BCBS 239 principles to evaluate governance, data architecture, accuracy, completeness, timeliness, adaptability, reporting quality, and supervisory readiness.
-
Design integrated risk data structures that connect business lines, legal entities, products, counterparties, portfolios, risk types, and reporting requirements.
-
Establish practical data-quality controls for identifying incomplete, inaccurate, inconsistent, duplicated, stale, or incorrectly classified risk information.
-
Develop end-to-end data lineage that tracks risk information from source systems through transformation, aggregation, calculation, validation, and regulatory reporting.
-
Build reconciliation and validation procedures that compare risk data with source systems, finance information, regulatory calculations, and previously reported results.
-
Design regulatory reporting workflows with clear ownership, reporting calendars, validation checks, exception management, approvals, sign-offs, and evidence-retention procedures.
-
Analyze risk reporting processes for accuracy, completeness, clarity, timeliness, adaptability, and usefulness to senior management and supervisory authorities.
-
Establish governance and remediation frameworks for addressing risk data deficiencies, reporting weaknesses, system limitations, manual processes, and emerging technology risks.
-
Build an integrated risk data aggregation and regulatory reporting solution capable of supporting management reporting, regulatory submissions, stress scenarios, and supervisory information requests.
Comprehensive Course Outline
Module 1: Risk Data Aggregation and Regulatory Reporting Foundations
-
Understanding the role of risk data aggregation in enterprise risk management, regulatory compliance, management decision-making, capital planning, and supervisory reporting.
-
Examining the BCBS 239 framework and its principles covering governance, data architecture, accuracy, completeness, timeliness, adaptability, reporting quality, frequency, distribution, and supervisory review.
-
Mapping risk data flows across credit, market, liquidity, operational, counterparty, concentration, capital, and other material risk categories.
-
Establishing the relationship between source data, risk calculations, aggregated exposures, management information, regulatory templates, validation rules, and supervisory submissions.
Module 2: Risk Data Architecture, Taxonomies and Integration
-
Designing enterprise risk data architectures that connect source systems, risk platforms, data warehouses, analytical environments, reporting applications, and regulatory reporting processes.
-
Developing common data taxonomies, definitions, identifiers, metadata, hierarchies, and naming conventions for customers, counterparties, accounts, products, entities, portfolios, and risk exposures.
-
Integrating data across business lines, legal entities, subsidiaries, branches, jurisdictions, products, and risk systems while maintaining consistent aggregation structures.
-
Addressing fragmented data environments through controlled integration, automated reconciliation, standardized interfaces, and documented data ownership and stewardship responsibilities.
Module 3: Risk Data Quality, Accuracy and Completeness
-
Establishing data-quality dimensions covering accuracy, completeness, consistency, validity, uniqueness, timeliness, integrity, availability, and traceability across risk datasets.
-
Developing automated and manual controls for detecting missing exposures, duplicate records, invalid classifications, stale information, unexpected movements, and broken data mappings.
-
Reconciling risk data with authoritative source systems and accounting information to identify discrepancies and establish reliable control evidence.
-
Creating risk data-quality dashboards and exception-management processes that track defects, ownership, remediation, aging, recurring issues, and unresolved reporting impacts.
Module 4: Risk Data Lineage, Metadata and Governance
-
Building end-to-end data lineage that documents the movement of risk information from source systems through transformations, calculations, aggregation layers, and final reports.
-
Establishing metadata standards that describe data definitions, business meaning, source ownership, transformation logic, calculation methods, reporting use, and retention requirements.
-
Defining governance roles across boards, senior management, risk functions, business owners, IT, data stewards, reporting teams, compliance, and internal audit.
-
Developing governance controls for data changes, new products, system migrations, mergers, acquisitions, reporting modifications, and evolving regulatory requirements.
Module 5: Risk Aggregation and Regulatory Reporting Processes
-
Aggregating risk exposures across legal entities, business lines, products, portfolios, currencies, jurisdictions, counterparties, and risk categories for consolidated reporting.
-
Designing regulatory reporting workflows covering data extraction, transformation, calculation, aggregation, validation, template population, review, approval, submission, and evidence retention.
-
Establishing reporting calendars, responsibility matrices, service levels, escalation procedures, sign-off controls, and submission tracking for recurring regulatory requirements.
-
Managing regulatory reporting exceptions by identifying root causes, assessing materiality, correcting source or transformation issues, documenting decisions, and maintaining an auditable resolution trail.
Module 6: Credit, Market, Liquidity and Enterprise Risk Reporting Analytics
-
Developing aggregated credit-risk reporting covering exposures, counterparties, portfolios, concentrations, collateral, limits, ratings, provisioning information, and material changes in credit risk.
-
Designing market-risk reporting covering positions, sensitivities, market factors, valuation information, limits, concentrations, and risk measures across portfolios and business units.
-
Aggregating liquidity-risk information including cash positions, funding sources, maturity profiles, liquidity buffers, concentrations, stress assumptions, and key liquidity indicators.
-
Connecting individual risk reports into enterprise risk views that allow management to understand material exposures across multiple risk categories and organizational dimensions.
Module 7: Reporting Validation, Reconciliation and Control Frameworks
-
Designing validation rules that test totals, balances, classifications, thresholds, relationships, completeness, period-over-period movements, and regulatory reporting consistency.
-
Performing reconciliations between risk systems, finance systems, general ledgers, subledgers, regulatory templates, management reports, and source transaction populations.
-
Developing controls for manual adjustments, spreadsheet-based processes, end-user computing, overrides, data transformations, and other activities that may introduce reporting errors.
-
Establishing independent validation and assurance procedures to assess whether aggregation and reporting processes operate as intended and remain appropriate for the institution's risk profile.
Module 8: Regulatory Reporting Analytics, Dashboards and Management Information
-
Designing regulatory reporting dashboards that monitor reporting completeness, validation failures, submission status, data-quality issues, reconciliation breaks, and remediation progress.
-
Developing management risk dashboards that present exposures, concentrations, trends, limits, breaches, emerging risks, and material movements across key risk dimensions.
-
Applying drilldown, aggregation, filtering, exception analysis, and trend analysis to investigate risk information from enterprise level through legal entity, business line, product, and counterparty levels.
-
Creating concise senior-management reporting that communicates material risk information clearly, supports timely decisions, and distinguishes routine movements from significant emerging concerns.
Module 9: Stress Reporting, Emerging Technology and Regulatory Change
-
Designing risk aggregation processes capable of supporting stress testing, crisis reporting, ad hoc supervisory requests, and rapid changes in reporting requirements.
-
Assessing how mergers, acquisitions, new products, new jurisdictions, organizational restructuring, system migrations, and changing risk profiles affect aggregation and reporting capabilities.
-
Evaluating the use of automation, advanced analytics, and emerging technologies in risk data aggregation while maintaining data-quality controls, governance, validation, and appropriate compensating controls.
-
Developing regulatory-change management processes that translate new requirements into data definitions, reporting specifications, system changes, analytical controls, testing procedures, and implementation plans.
Module 10: Capstone Risk Data Aggregation and Regulatory Reporting Project
-
Designing an end-to-end risk data aggregation framework covering source systems, data architecture, taxonomies, ownership, lineage, quality controls, aggregation logic, and reporting outputs.
-
Integrating multiple risk datasets and performing data-quality assessments, reconciliations, aggregation procedures, validation checks, exception analysis, and regulatory reporting preparation.
-
Developing management and regulatory dashboards that communicate risk exposures, data-quality indicators, reporting status, material exceptions, concentrations, and emerging risk information.
-
Presenting the final risk reporting framework with documented governance, control procedures, data lineage, remediation priorities, regulatory reporting processes, and recommendations for sustainable implementation.
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.
