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Risk Management
Risk Data Aggregation and Data Quality Controls Training Course
Introduction
The modern financial landscape is driven by data, making robust Risk Data Aggregation (RDA) and impeccable Data Quality (DQ) non-negotiable for regulatory compliance and strategic decision-making. The increasing velocity and volume of data, compounded by stringent global mandates like BCBS 239, demand an enterprise-wide shift from fragmented systems to a unified data governance framework. Failure to achieve high-quality, aggregated risk data results in inaccurate risk exposures, flawed capital calculations, and an inability to respond swiftly during crisis scenarios, leading to significant financial losses and supervisory penalties. Risk Data Aggregation and Data Quality Controls Training Course is designed to empower professionals to operationalize these critical principles, transforming their data infrastructure from a vulnerability into a competitive advantage.
This specialized training delivers the practical skills necessary to design, implement, and maintain effective DQ controls across the entire data lifecycle, directly supporting the core pillars of effective risk management. Participants will learn how to build automated data lineage and reporting capabilities that ensure Accuracy, Completeness, and Timeliness of risk reports, moving beyond compliance checkbox exercises to genuine organizational resilience. By mastering techniques in data profiling, validation, and modern data architecture, attendees will be equipped to dismantle data silos and establish the foundational trust required for advanced analytics, stress testing, and proactive identification of emerging risks, thereby fortifying the institution’s overall Enterprise Risk Management (ERM) posture.
Programme Curriculum
Risk Data Aggregation and Data Quality Controls Training Course
Introduction
The modern financial landscape is driven by data, making robust Risk Data Aggregation (RDA) and impeccable Data Quality (DQ) non-negotiable for regulatory compliance and strategic decision-making. The increasing velocity and volume of data, compounded by stringent global mandates like BCBS 239, demand an enterprise-wide shift from fragmented systems to a unified data governance framework. Failure to achieve high-quality, aggregated risk data results in inaccurate risk exposures, flawed capital calculations, and an inability to respond swiftly during crisis scenarios, leading to significant financial losses and supervisory penalties. Risk Data Aggregation and Data Quality Controls Training Course is designed to empower professionals to operationalize these critical principles, transforming their data infrastructure from a vulnerability into a competitive advantage.
This specialized training delivers the practical skills necessary to design, implement, and maintain effective DQ controls across the entire data lifecycle, directly supporting the core pillars of effective risk management. Participants will learn how to build automated data lineage and reporting capabilities that ensure Accuracy, Completeness, and Timeliness of risk reports, moving beyond compliance checkbox exercises to genuine organizational resilience. By mastering techniques in data profiling, validation, and modern data architecture, attendees will be equipped to dismantle data silos and establish the foundational trust required for advanced analytics, stress testing, and proactive identification of emerging risks, thereby fortifying the institution’s overall Enterprise Risk Management (ERM) posture.
Course Duration
5 days
Course Objectives
Upon completion of this course, participants will be able to:
Operationalize the 14 principles of BCBS 239 within their respective risk and finance functions.
Design a comprehensive Data Governance framework with clear ownership and accountability.
Establish robust Data Lineage capabilities to trace risk data from source to final report.
Implement automated Data Quality Controls for Accuracy, Completeness, and Timeliness.
Develop effective Key Risk Indicators (KRIs) and Key Performance Indicators (KPIs) for DQ monitoring.
Execute advanced Data Profiling techniques to identify hidden inconsistencies and anomalies.
Apply Data Cleansing and standardization strategies across disparate source systems.
Master the process of Risk Data Aggregation for a single, consolidated group-wide risk view.
Streamline and Automate the production of internal and regulatory risk reports.
Integrate Risk and Finance Data to support unified financial and regulatory reporting
Prepare the data infrastructure to support complex Scenario Analysis and Stress Testing.
Design a Target Data Architecture that eliminates silos and ensures data Integrity.
Drive Cultural Change towards data stewardship and Enterprise Risk Management excellence.
Target Audience
Risk Management Professionals
Data Governance and Data Quality Managers/Officers
IT/Data Architects and Data Engineers in Financial Services
Compliance Officers and Regulatory Reporting Specialists
Internal and External Auditors focused on Risk and Data Controls
Senior Management and Board Members with BCBS 239 oversight responsibility
Finance Professionals involved in capital calculation and stress testing
Business Analysts and Project Managers for Data Transformation Initiatives
Course Modules
Module 1: The Regulatory Imperative – BCBS 239 and Data Governance
Deep dive into BCBS 239 principles and their impact on data infrastructure.
Defining and establishing the Data Governance Operating Model
Implementing a Data Stewardship program and the role of Data Owners.
Case Study: Analyzing a major G-SIB's regulatory remediation journey post-BCBS 239 violation.
Developing clear Risk Taxonomies and Data Dictionaries for standardization.
Module 2: Risk Data Aggregation (RDA) Architecture
Understanding the Data Silo problem and the need for a unified data repository.
Principles of effective data integration and data warehousing for risk.
Designing a Target Operating Model for risk data collection and processing.
Case Study: Mapping risk exposure across multiple legal entities/subsidiaries during a market crisis.
Evaluating modern technologies.
Module 3: Foundational Data Quality (DQ) Principles
Defining the core DQ dimensions.
Establishing Data Quality Rules
Quantitative and qualitative measurement of data quality levels and setting thresholds.
Case Study: Calculating data loss and potential capital miscalculation due to incomplete counterparty data.
The role of Metadata Management in achieving data standardization and understanding.
Module 4: Practical Data Profiling and Assessment
Introduction to Data Profiling tools and techniques for current state assessment.
Methods for detecting data anomalies, outliers, and structural defects.
Developing and reporting Data Quality Metrics
Case Study: Using profiling results to prioritize the most impactful data cleansing efforts in a loan portfolio.
Setting up continuous Data Quality Monitoring dashboards and alerts.
Module 5: Implementing Data Quality Controls and Remediation
Designing controls across the Data Lifecycle
Developing Data Cleansing and enrichment strategies
Establishing a formal Data Quality Issue Management and escalation process.
Case Study: Remediation of inconsistent risk ratings across different trading systems.
Implementing controls to reduce reliance on error-prone Manual Adjustments.
Module 6: Data Lineage and Traceability
The importance of End-to-End Data Lineage for regulatory scrutiny and auditing.
Techniques for capturing, documenting, and visualizing data flow from source to report.
Using lineage to perform Impact Analysis for system changes or data errors.
Case Study: Tracing the root cause of an erroneous Liquidity Coverage Ratio report figure.
Validating the control effectiveness of the Data Flow via independent review.
Module 7: Risk Reporting and Automation
Principles of effective risk reporting
Strategies for achieving a high degree of Report Automation to ensure timeliness.
Designing Risk Reports for the Board and Senior Management
Case Study: Developing an Ad-Hoc Reporting capability for rapid crisis response.
Integrating DQ measures directly into final risk reports to highlight limitations.
Module 8: Advanced Applications and Future Trends
Preparing data for advanced Stress Testing and Scenario Analysis
The role of Artificial Intelligence and Machine Learning in automated DQ and anomaly detection.
Managing data quality for new risk types
Case Study: Applying ML models to predict and prevent data entry errors in the front office.
Roadmap development for sustainable Data Quality Improvement and maturity assessment.
Training Methodology
This course employs a participatory and hands-on approach to ensure practical learning, including:
Interactive lectures and presentations.
Group discussions and brainstorming sessions.
Hands-on exercises using real-world datasets.
Role-playing and scenario-based simulations.
Analysis of case studies to bridge theory and practice.
Peer-to-peer learning and networking.
Expert-led Q&A sessions.
Continuous feedback and personalized guidance.
Register as a group from 3 participants for a Discount
Upon successful completion of this training, participants will be issued with a globally- recognized certificate.
Tailor-Made Course
We also offer tailor-made courses based on your needs.
Key Notes
a. The participant must be conversant with English.
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
c. Course duration is flexible and the contents can be modified to fit any number of days.
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
e. One-year post-training support Consultation and Coaching provided after the course.
f. Payment should be done at least a week before commence of the training, to FINESKILL TRAINING CENTER account, as indicated in the invoice so as to enable us prepare better for you.