Course Introduction
Data Regulatory Reporting Controls Training Course provides a structured understanding of how organizations establish, operate, and continuously improve controls that support accurate, complete, timely, consistent, and traceable regulatory reporting. The course examines how data is collected, transformed, validated, approved, reconciled, and submitted to regulators while maintaining strong governance and accountability throughout the reporting lifecycle.
The course explores the relationship between regulatory requirements, data governance, reporting processes, internal controls, technology platforms, and organizational accountability. Participants learn how regulatory reporting controls help reduce data errors, inconsistent definitions, incomplete submissions, unsupported figures, reporting delays, and weaknesses in evidence or documentation. The training also considers how regulatory expectations increasingly emphasize data lineage, transparency, explainability, and demonstrable control effectiveness.
Participants will examine practical approaches for identifying regulatory reporting data sources, defining critical reporting data elements, establishing validation rules, documenting data transformations, and maintaining reliable reporting workflows. The course covers preventive, detective, and corrective controls and explains how organizations can combine automated and manual controls to address risks across data collection, aggregation, calculation, reconciliation, review, approval, and submission activities.
The training also addresses control assessment and testing techniques used to determine whether regulatory reporting controls are properly designed and operating effectively. Participants learn how to develop control objectives, define control owners, establish evidence requirements, evaluate exceptions, perform reconciliations, document testing results, and manage remediation activities. Emphasis is placed on creating an auditable control environment that can demonstrate how reported information was produced and validated.
Modern regulatory reporting environments increasingly depend on cloud platforms, distributed data architectures, APIs, data warehouses, automated reporting solutions, artificial intelligence, and third-party service providers. The course therefore examines technology-related risks that may affect regulatory reporting controls, including data integration failures, system changes, model risks, automation errors, access weaknesses, cybersecurity incidents, and insufficient oversight of external data or technology providers.
The course concludes by examining advanced approaches for strengthening regulatory reporting control environments through continuous monitoring, risk-based testing, automated validation, control analytics, exception management, data lineage monitoring, and emerging regulatory technology. Participants gain practical knowledge for building sustainable control frameworks that support reliable regulatory reporting while adapting to changing requirements, increasingly complex data environments, and greater expectations for transparency and accountability.
Duration
10 days
Who Should Attend
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Regulatory reporting professionals responsible for preparing or submitting regulatory returns
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Data governance professionals supporting regulatory reporting processes
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Risk management professionals involved in data and reporting risk
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Compliance officers responsible for regulatory obligations and controls
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Internal auditors assessing regulatory reporting processes and controls
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Finance professionals involved in regulatory and statutory reporting
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Data quality managers responsible for reporting data accuracy and completeness
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Business analysts supporting regulatory reporting requirements
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Data stewards responsible for critical regulatory data elements
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IT professionals supporting regulatory reporting platforms and systems
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Information security professionals addressing reporting data protection risks
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Internal control specialists designing and testing reporting controls
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Banking, insurance, investment, and financial services professionals
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Regulatory affairs and regulatory change management teams
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Reporting managers and data operations specialists
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Professionals seeking practical knowledge of regulatory reporting control frameworks
Course Objectives
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Understand the principles, structures, and operating models used to establish effective controls over regulatory reporting data and processes.
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Identify regulatory reporting risks arising from inaccurate, incomplete, inconsistent, untimely, or poorly governed data throughout the reporting lifecycle.
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Develop clear control objectives that align regulatory obligations, business requirements, data risks, reporting processes, and organizational accountability.
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Assess regulatory reporting data sources and determine whether critical information is sufficiently controlled from collection through final submission.
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Design preventive, detective, and corrective controls that address data quality, validation, reconciliation, transformation, calculation, approval, and submission risks.
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Apply data lineage and traceability techniques to demonstrate how regulatory reporting figures originate, change, aggregate, and reach final reports.
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Evaluate the design and operating effectiveness of regulatory reporting controls using structured testing, evidence review, sampling, and exception analysis.
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Establish effective reconciliation controls for identifying differences between source systems, reporting databases, calculations, general ledgers, and submitted regulatory reports.
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Assess technology, access, automation, cloud, third-party, and system-change risks that may affect the reliability of regulatory reporting controls.
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Develop practical approaches for documenting control evidence, managing exceptions, escalating deficiencies, and tracking remediation activities.
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Apply monitoring, reporting, and control performance techniques to identify emerging weaknesses before they affect regulatory submissions.
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Strengthen regulatory reporting control environments through continuous improvement, automation, analytics, and risk-based control assessment.
Comprehensive Course Outline
Module 1: Foundations Of Regulatory Reporting Controls
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Purpose, scope, and principles of regulatory reporting control frameworks
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Regulatory reporting lifecycle from data sourcing through submission and retention
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Roles, responsibilities, accountability, and control ownership
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Relationship between regulatory reporting, governance, risk, compliance, and assurance
Module 2: Regulatory Reporting Requirements And Control Obligations
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Translating regulatory requirements into operational reporting controls
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Identifying reporting obligations, frequencies, deadlines, thresholds, and data requirements
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Regulatory interpretations, reporting instructions, definitions, and business rules
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Managing regulatory change and its impact on reporting controls
Module 3: Regulatory Reporting Data Governance
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Data ownership, stewardship, accountability, and governance structures
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Critical data elements and regulatory reporting data inventories
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Data definitions, business glossaries, reference data, and reporting taxonomies
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Governance forums, escalation structures, and decision-making processes
Module 4: Regulatory Reporting Risk Identification And Assessment
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Identifying risks across data sourcing, processing, calculation, aggregation, and submission
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Assessing data accuracy, completeness, consistency, timeliness, and validity risks
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Risk assessment methodologies and risk prioritization techniques
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Connecting reporting risks with business impact, regulatory impact, and control requirements
Module 5: Regulatory Data Quality Controls
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Data validation rules and automated quality checks for regulatory reporting
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Completeness, accuracy, consistency, uniqueness, validity, and timeliness controls
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Data quality thresholds, tolerances, exceptions, and remediation workflows
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Monitoring recurring data quality problems and root causes
Module 6: Data Lineage And Reporting Traceability Controls
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Mapping regulatory reports to source systems, datasets, transformations, and calculations
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End-to-end data lineage documentation and traceability requirements
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Transformation logic, aggregation rules, calculation methodologies, and dependencies
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Using lineage evidence to support regulatory reviews, audits, and investigations
Module 7: Reconciliation And Validation Controls
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Source-to-report reconciliation and cross-system data validation techniques
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Financial, operational, and regulatory reporting reconciliation frameworks
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Investigating variances, breaks, exceptions, and unexplained differences
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Automated reconciliation, tolerance management, and exception escalation
Module 8: Regulatory Reporting Process And Workflow Controls
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Control points across data collection, preparation, review, approval, and submission
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Segregation of duties and independent review requirements
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Reporting calendars, workflow approvals, deadlines, and submission controls
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Managing manual interventions, overrides, adjustments, and late changes
Module 9: Control Testing And Effectiveness Assessment
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Designing control testing procedures for regulatory reporting environments
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Assessing control design effectiveness and operating effectiveness
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Evidence collection, sampling, walkthroughs, reperformance, and exception testing
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Documenting deficiencies, conclusions, findings, and remediation requirements
Module 10: Technology And Automated Reporting Controls
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Application controls supporting regulatory reporting calculations and data processing
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System interfaces, APIs, ETL processes, databases, and reporting platforms
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Change management, release controls, configuration management, and system validation
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Risks and controls associated with automated reporting and artificial intelligence
Module 11: Access, Security And Third-Party Reporting Controls
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Access controls protecting regulatory reporting data and reporting applications
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Privileged access, segregation of duties, authentication, and authorization controls
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Third-party data providers, outsourced reporting services, and vendor oversight
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Cybersecurity incidents, data breaches, and operational disruptions affecting reporting
Module 12: Regulatory Reporting Evidence And Documentation
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Control documentation standards and regulatory reporting evidence requirements
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Maintaining audit trails, approvals, reconciliations, validation results, and submission records
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Evidence retention, version control, documentation integrity, and retrieval
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Preparing control evidence for internal audit, external audit, and regulatory examination
Module 13: Exceptions, Deficiencies And Remediation Management
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Identifying and classifying regulatory reporting control deficiencies
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Root cause analysis for recurring reporting errors and control failures
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Corrective action planning, ownership, deadlines, and remediation tracking
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Escalation frameworks for material reporting issues and regulatory concerns
Module 14: Regulatory Reporting Monitoring And Assurance
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Key risk indicators, key control indicators, and reporting control dashboards
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Continuous monitoring of reporting quality, control performance, and exceptions
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Management information, control attestations, and assurance reporting
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Independent assurance, internal audit, regulatory examinations, and control reviews
Module 15: Emerging Regulatory Reporting And Data Control Risks
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Regulatory reporting risks associated with cloud, distributed data, and modern architectures
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Artificial intelligence, machine learning, generative AI, and automated decision-support risks
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Real-time reporting, regulatory technology, advanced analytics, and continuous controls
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Emerging expectations for explainability, transparency, resilience, and data accountability
Module 16: Advanced Regulatory Reporting Control Frameworks And Continuous Improvement
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Building integrated regulatory reporting control frameworks across business and technology
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Risk-based optimization of control coverage, testing frequency, and monitoring
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Automation, control analytics, continuous assurance, and intelligent exception detection
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Developing sustainable improvement programs for evolving regulatory reporting environments
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.
