Market data infrastructure and risk factor modellability for Basel III/IV
FRTB Data Management
The Fundamental Review of the Trading Book demands comprehensive market data, demonstrable risk factor modellability and audit-proof data governance. We build the data infrastructure your trading book needs — from real price observation pipelines and NMRF minimisation to automated data quality assurance.
- ✓Market data pipeline with automated validation and real price observation tracking
- ✓Risk factor modellability test (RPO test) for targeted NMRF reduction
- ✓Data governance for trading book data with full data lineage
- ✓Automated FRTB reporting processes with end-to-end data quality controls
Your strategic success starts here
Our clients trust our expertise in digital transformation, compliance, and risk management
30 Minutes • Non-binding • Immediately available
For optimal preparation of your strategy session:
- Your strategic goals and objectives
- Desired business outcomes and ROI
- Steps already taken
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Why FRTB data management determines your capital requirements
Why ADVISORI for FRTB data management
- Proven experience with FRTB data architectures at European banks
- Deep understanding of risk factor modellability and RPO requirements
- Interdisciplinary team of data engineers, risk managers and regulatory experts
- Battle-tested methodology for data governance implementation in complex banking environments
⚠
Reduce NMRF capital add-ons through better data
Non-modellable risk factors account for up to 30% of total FRTB capital requirements. A targeted market data strategy with systematic RPO tracking can significantly reduce these add-ons — directly freeing up regulatory capital.
ADVISORI in Numbers
11+
Years of Experience
120+
Employees
520+
Projects
We follow a structured, results-oriented approach that takes your existing data landscape as a starting point and systematically targets the biggest levers for capital savings and compliance assurance.
Our Approach:
Data landscape assessment: inventory of all market data sources, risk factors and RPO coverage
Target architecture design: FRTB-compliant data platform focused on modellability and data quality
Implementation: data pipelines, governance framework and quality monitoring in iterative sprints
Validation and audit preparation: evidence of data quality for internal audit and supervisory review
Ongoing operations: continuous monitoring, regular reviews and adaptation to regulatory changes
"Excellent FRTB compliance begins with excellent data. The complexity of modern trading book data landscapes requires not only technical solutions but also strategic data governance and continuous quality assurance. Our clients benefit from solid data architectures that not only ensure compliance but also support strategic decisions and enhance operational efficiency."
Melanie Düring
Head of Risk Management
Our Services
We offer you tailored solutions for your digital transformation
Enterprise Data Architecture for FRTB
We develop solid, flexible data architectures specifically optimized for FRTB requirements, meeting the highest standards for performance, security, and compliance.
- Comprehensive data model design with FRTB-specific entities and relationships
- High-performance data lake and data warehouse architectures for trading book data
- Real-time streaming architectures for time-critical FRTB calculations
- Cloud-based and hybrid architectures with enterprise-grade security
Data Governance and Quality Management
We implement comprehensive data governance frameworks and automated quality management systems that continuously ensure the highest data quality for FRTB compliance.
- Data governance framework with clear roles, responsibilities, and control processes
- Automated data quality monitoring with intelligent anomaly detection
- Data lineage tracking and impact analysis for full transparency
- Master data management and data stewardship programs for sustainable data quality
Our Competencies in Fundamental Review of the Trading Book (FRTB)
Choose the area that fits your requirements
Expected Shortfall Under FRTB – Calculation, Validation and Implementation
Expected Shortfall (ES) is the central risk measure for market risk capital requirements under the Fundamental Review of the Trading Book (FRTB). It replaces Value at Risk and measures the average loss in the tail of the loss distribution — at the 97.5% confidence level over a 250-day stress period. ADVISORI guides banks through implementation: from ES calculation through classification of modellable risk factors to regulatory validation.
FRTB Backtesting Requirements — Model Validation Standards for Market Risk
FRTB Backtesting Requirements demand precise implementation of Basel III model validation with specific backtesting performance requirements and validation procedures. As a leading consulting firm, we develop tailored RegTech solutions for intelligent backtesting compliance, automated model performance monitoring, and strategic validation optimization with full IP protection.
FRTB Boundary Trading Banking Book
The correct delineation between the trading book and banking book is critical for FRTB compliance and capital optimization. Together with you, we develop solid boundary management frameworks for precise classification and efficient management.
FRTB Credit Valuation Adjustment
FRTB Credit Valuation Adjustment presents new challenges for capital calculation and risk management. Together with you, we develop comprehensive CVA frameworks for precise capital calculation, effective hedging, and sustainable compliance excellence.
FRTB German Implementation
The Fundamental Review of the Trading Book presents German banks with specific challenges. We develop tailored implementation strategies that meet BaFin requirements while accounting for the particularities of the German banking market.
FRTB Implementation
Navigate the complex implementation of the Fundamental Review of the Trading Book with our comprehensive implementation support. We guide you through the entire process – from the initial assessment and gap analysis through concept development and system adaptation to full integration into your trading and risk management systems, including model adjustment, data infrastructure and process optimisation.
FRTB Implementation Strategy: Approach Selection, Capital Optimization & Phased Rollout
FRTB Implementation Strategy requires precise implementation of the Basel III Fundamental Review of the Trading Book with specific market risk capital requirements and supervisory validation. As a leading AI consultancy, we develop tailored RegTech solutions for intelligent FRTB compliance, automated trading book separation and strategic market risk optimization with full IP protection.
FRTB Internal Models Approach (IMA) — Requirements, Approval and Implementation
The FRTB Internal Models Approach (IMA) allows banks to use their own risk models for market risk capital calculations — provided they meet strict supervisory requirements for Expected Shortfall, backtesting and P&L attribution. As specialist FRTB consultants, ADVISORI supports institutions with IMA approval, model validation and ongoing compliance.
FRTB Market Risk Modeling – Sensitivity-Based Approach, Risk Classes & Risk Factor Modeling
The Fundamental Review of the Trading Book requires fundamentally new market risk modeling: The sensitivity-based approach (SbA) calculates delta, vega and curvature risks across seven risk classes – GIRR, CSR (non-sec, sec CTP, sec non-CTP), equity, FX and commodity. We support banks in the methodological design, risk factor modeling and operational implementation of these requirements.
FRTB Non-Modellable Risk Factors (NMRF) – RPO Test & SES Capital Add-On | ADVISORI
FRTB Non-Modellable Risk Factors require precise implementation of Basel III NMRF identification with specific capital calculation procedures and stress scenario calibration. As a leading AI consultancy, we develop tailored RegTech solutions for intelligent NMRF compliance, automated risk factor validation and strategic supervisory recognition optimization with full IP protection.
FRTB Ongoing Compliance
Ongoing adherence to FRTB requirements demands systematic monitoring, regular adjustments, and proactive optimization. We support you in establishing sustainable FRTB compliance.
FRTB P&L Attribution Test (PLAT) – Requirements, Methodology & Consulting | ADVISORI
FRTB Profit & Loss Attribution requires precise implementation of Basel III P&L allocation with specific risk factor decomposition requirements and model validation. As a leading AI consultancy, we develop tailored RegTech solutions for intelligent P&L attribution compliance, automated backtesting integration and strategic transparency optimisation with full IP protection.
FRTB Readiness Assessment
Our comprehensive FRTB readiness assessment identifies gaps in your current systems, processes, and data, quantifies the impact on your capital, and delivers a tailored implementation roadmap for efficient FRTB compliance.
Frequently Asked Questions about FRTB Data Management
What is the RPO test and why is it critical for FRTB data management?
The Real Price Observation test (RPO test) checks whether a risk factor has at least
24 actual market prices observed over the preceding
12 months. Only risk factors passing this test are classified as modellable under FRTB and may be calculated using expected shortfall models in the Internal Models Approach (IMA). Non-modellable risk factors (NMRFs) attract a separate, significantly higher capital charge. Systematic market data sourcing that closes RPO gaps is therefore the most effective lever for reducing FRTB capital requirements.
What market data is specifically required for FRTB compliance?
FRTB requires comprehensive market data in three categories: first, historical time series with at least
10 years of data including stress periods for expected shortfall calculation. Second, real-time or end-of-day market data for ongoing risk measurement and P&L attribution. Third, real price observations for the modellability test of each individual risk factor. This includes complete yield curves, volatility surfaces, credit spreads and commodity curves at the granularity level prescribed by regulators.
What are non-modellable risk factors (NMRFs) and how can they be reduced?
NMRFs are risk factors that fail the RPO test because too few observable market prices exist. They can account for 30% or more of total FRTB capital requirements, particularly for exotic products and emerging market positions. Reduction strategies include expanding market data sources (e.g. through data pooling initiatives like the DTCC RPO Service), optimising risk factor granularity by grouping related factors, and improving internal data capture for OTC transactions.
How do data management requirements differ between IMA and the standardised approach?
The Internal Models Approach (IMA) imposes significantly higher data requirements than the Standardised Approach (SA). IMA needs complete risk factor time series, RPO evidence for each factor, daily P&L data for the P&L attribution test and sufficient data for backtesting. The SA works with predefined sensitivities and primarily requires accurate position data. Many banks run both approaches in parallel, making a unified data foundation essential.
What role does data governance play in the FRTB context?
Data governance under FRTB is a regulatory requirement, not an optional framework. Supervisors expect traceable data lineage, defined data ownership, documented quality standards and audit-proof change logs. Every risk factor must be traceable to its data source, data changes must be recorded in an immutable audit trail, and clear escalation processes must exist for data quality issues.
How long does it take to build an FRTB-compliant data infrastructure?
A complete implementation typically takes
12 to
24 months:
3 months for assessment and architecture design,
6 to
12 months for core implementation (data pipelines, governance framework, quality monitoring), and a further
3 to
6 months for fine-tuning, validation and audit preparation. Faster capital relief can be achieved by starting with a targeted NMRF reduction initiative, where initial results are often measurable after
3 to
6 months.
How does ADVISORI support FRTB data management?
ADVISORI supports the entire process: analysis of your existing data landscape, identification of modellability gaps, assessment of RPO coverage, target architecture design, market data sourcing strategy, data governance framework and automated quality assurance. After go-live, we provide continuous monitoring, regular data quality reviews and preparation for supervisory examinations.
Success Stories
Discover how we support companies in their digital transformation
Digitalization in Steel Trading
Klöckner & Co
Digital Transformation in Steel Trading
Case Study
Results
Over 2 billion euros in annual revenue through digital channels
Goal to achieve 60% of revenue online by 2022
Improved customer satisfaction through automated processes
AI-Powered Manufacturing Optimization
Siemens
Smart Manufacturing Solutions for Maximum Value Creation
Case Study
Results
Significant increase in production performance
Reduction of downtime and production costs
Improved sustainability through more efficient resource utilization
AI Automation in Production
Festo
Intelligent Networking for Future-Proof Production Systems
Case Study
Results
Improved production speed and flexibility
Reduced manufacturing costs through more efficient resource utilization
Increased customer satisfaction through personalized products
Generative AI in Manufacturing
Bosch
AI Process Optimization for Improved Production Efficiency
Case Study
Results
Reduction of AI application implementation time to just a few weeks
Improvement in product quality through early defect detection
Increased manufacturing efficiency through reduced downtime
Let's
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Your strategic success starts here
Our clients trust our expertise in digital transformation, compliance, and risk management
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