FRTB Gap Analysis Data & Processes

For the senior leadership of financial institutions, FRTB implementation is far more than a regulatory project — it is a strategic initiative with far-reaching implications for business models, capital efficiency and competitiveness. A well-founded data and process gap analysis forms the critical foundation for strategic decisions and sustainable implementation. Strategic significance for the C-suite: Business model implications: FRTB requirements can have fundamental impacts on the profitability of certain trading activities. A precise gap analysis identifies early on which business areas will come under pressure and enables strategic adjustments before the full regulatory effect takes hold. Capital efficiency and planning: The data-driven assessment of capitalisation impacts under various implementation scenarios (standardised approach vs. internal models) provides senior management with decision-relevant foundations for strategic capital allocation. Strategic competitive advantage: Institutions that conduct a precise gap analysis early can implement more targeted and efficiently, thereby creating a strategic lead in a consolidating market. Risk minimisation in transformation: The comprehensive analysis minimises the risk of costly misjudgements in the technological and process transformation required for FRTB.

FRTB regulation places exceptionally complex and extensive requirements on the data infrastructure and process landscape of banks. In terms of granularity, completeness and quality, these typically far exceed the existing capabilities of many institutions. ADVISORI supports with a methodical approach to precisely identifying and prioritising these gaps. Critical FRTB data requirements: Market data granularity and historisation: Requirement for highly granular market data with 10+ years of history for model validation and risk calculation, including complete curves and volatility surfaces for all relevant risk parameters. Risk factor modellability: Necessity of continuously assessing and documenting the modellability of risk factors based on strict criteria for the number and distribution of 'real' price observations. Attribution requirements: Comprehensive data integration between front office and risk management for detailed P&L attribution at desk level with extremely high explanation requirements. Non-Modellable Risk Factors (NMRFs): Identification, documentation and specific capitalisation of non-modellable risk factors requires new data capture and processing procedures. Sensitivity calculations: Structured capture and storage of thousands of delta, vega and curvature sensitivities for the standardised approach.

FRTB regulation requires an unprecedented level of integration between the front office and risk management, particularly when using internal models. This deep integration presents many banks with significant technical, organisational and cultural challenges that go far beyond classic risk projects. Central challenges of FRTB data integration: Diverging systems and valuation methods: Front office systems and risk management systems typically use different valuation methods, market data sources and modelling approaches, leading to significant P&L discrepancies and jeopardising the strict FRTB attribution requirements. Temporal synchronisation: FRTB requires a daily, time-synchronised valuation between trading and risk systems with minimal deviations — a major challenge in distributed system landscapes with different valuation timestamps and data extracts. Granularity differences: Front office systems typically operate at the individual transaction level, while risk systems often operate at the aggregated portfolio level — FRTB requires consistent granularity across all systems. Incomplete transaction attributes: Front office systems frequently lack FRTB-relevant risk attributes or capture them inconsistently, complicating correct risk factor assignment and sensitivity calculation.

A strategically conceived FRTB gap analysis offers banks the opportunity to go far beyond mere fulfilment of regulatory requirements. It can serve as a catalyst for a comprehensive transformation of the data and process landscape, thereby unlocking significant strategic advantages in a competitive market environment. Strategic transformation opportunities through FRTB: Data-driven business optimisation: The consolidation and quality improvement of market and position data required for FRTB creates the foundation for more precise and granular business analyses, leading to better trading and investment decisions. Front-to-risk integration as a competitive advantage: Overcoming the classic silo approach between front office and risk management enables more agile product development, faster time-to-market and more precise risk-based pricing. Cost-efficient IT architecture: The consolidation and modernisation of the system landscape as part of FRTB implementation can significantly reduce IT operating costs while simultaneously increasing flexibility. Analytical excellence: The improved data models and advanced analytical capabilities developed for FRTB form the foundation for expanded applications of machine learning and AI in trading and risk management.

The data requirements for the standardised approach (SA-TB) and the internal model approach (IMA) under FRTB differ fundamentally in their granularity, complexity and associated process requirements. These differences have far-reaching implications for data architecture and a bank's strategic decisions in FRTB implementation. Data requirements under the standardised approach (SA-TB): Sensitivity-based calculation: Capture and storage of thousands of delta, vega and curvature sensitivities for each position, structured according to precisely defined risk factors and buckets. Risk factor mapping: Detailed assignment of each position to standardised risk factors and risk buckets with extensive attributes for correct classification. Default Risk Charge (DRC): Granular information on issuers, credit quality, maturities and JTD (Jump-to-Default) amounts for bonds, loans and derivatives. Residual Risk Add-On (RRAO): Identification of exotic instruments and their specific risk components not covered by the sensitivity analysis. Data requirements under the internal model approach (IMA): Historical time series: Comprehensive data histories (10+ years) for all modellable risk factors with daily granularity for backtesting and Expected Shortfall calculation.

The Fundamental Review of the Trading Book requires a fundamental redesign of numerous core processes in market risk management compared to Basel 2.5. These process adjustments go far beyond incremental changes and affect virtually all aspects of trading book management, risk measurement and reporting. Impactful process requirements under FRTB: Trading book vs. banking book assignment: Implementation of a rigorous, documented process for assigning instruments to the trading or banking book with strict criteria for reclassifications and supervisory approval requirements. Trading desk definition and management: Establishment of a formal process for defining, documenting and monitoring trading desks in accordance with strict regulatory requirements, including precise mandate definition and P&L attribution. Daily P&L explanation: Implementation of a daily process for the detailed explanation and analysis of P&L components at trading desk level with direct linkage between front office and risk management. Risk factor modellability assessment: Establishment of a continuous process for assessing and documenting the modellability of each risk factor based on RFET (Risk Factor Eligibility Test) criteria with comprehensive documentation obligations.

The implementation of the Fundamental Review of the Trading Book places exceptionally high demands on the IT infrastructure and system landscape of financial institutions. These technological challenges require, in many cases, a fundamental reassessment and transformation of the existing architecture in order to meet regulatory requirements efficiently and sustainably. Core technological requirements for FRTB: High-performance computing capacity: Exponentially increased computational effort for the daily calculation of thousands of sensitivities or extensive Monte Carlo simulations for Expected Shortfall requires massive scaling of the computing infrastructure. Extended data storage: Necessity of storing and efficiently processing enormous volumes of data, including 10+ years of historical market data at daily granularity for all risk factors. Flexible aggregation capabilities: Ability to dynamically aggregate risk metrics across various hierarchy levels (instrument, trading desk, legal entity, group) with differing regulatory requirements. Extended analytical capabilities: Tools for detailed analysis and explanation of risk drivers, P&L components and deviations between different calculation approaches. Solid workflow management systems: Technological support for the complex FRTB processes with workflow tracking, approval mechanisms and audit trails.

A successful FRTB implementation requires a well-conceived system of key performance indicators (KPIs) and monitoring frameworks to measure progress, identify risks early and ensure the sustainability of compliance. ADVISORI supports financial institutions in developing tailored KPI and monitoring systems that cover both the implementation phase and ongoing operations. Multi-dimensional KPI framework for FRTB: Implementation KPIs: Metrics for monitoring project progress, resource utilisation and timely fulfilment of regulatory milestones during the implementation phase. Data quality KPIs: Indicators for continuously monitoring the completeness, timeliness, consistency and accuracy of critical data for FRTB calculations and reporting. Process efficiency KPIs: Metrics for assessing the efficiency and solidness of implemented processes, such as calculation throughput times, frequency of manual interventions and process error rates. Compliance KPIs: Key figures for continuously monitoring adherence to regulatory requirements, such as P&L attribution metrics, backtesting exceptions and risk factor modellability ratios. Capital efficiency KPIs: Indicators for measuring capital efficiency under FRTB, such as the development of capital requirements over time, capital allocation by trading desk and diversification effects.

The decision between the standardised approach (SA-TB) and the internal model approach (IMA) for FRTB represents a complex strategic choice that goes far beyond pure compliance considerations. A well-founded cost-benefit analysis requires a multi-dimensional assessment taking into account quantitative and qualitative factors that reach far into the future of the trading business. Multi-dimensional assessment criteria for the choice of approach: Capital impacts: Quantitative analysis of capital differences between SA-TB and IMA at desk, portfolio and institution level under various market scenarios and business strategies. Implementation costs: Detailed assessment of one-off investment costs for data infrastructure, system adjustments, model development and validation as well as process implementation for both approaches. Ongoing operating costs: Assessment of the ongoing costs for data management, calculation, reporting, model validation and regulatory processes in regular operations. Implementation risks: Analysis of implementation risks with regard to complexity, resource availability, timeline and possible regulatory changes during the implementation phase. Business-strategic implications: Assessment of the impacts on the trading business, product portfolio, competitive position and strategic development opportunities.

FRTB regulation places exceptionally high demands on data quality that go far beyond previous regulatory standards. Establishing solid data quality management is therefore a critical success factor for FRTB implementation and sustainable operations — particularly when using internal models. Specific data quality requirements under FRTB: Completeness and continuity: Necessity of complete time series without gaps for all relevant risk factors, with specific requirements for the minimum number of observations for modellability. Accuracy and precision: High requirements for the exactness of data, particularly for P&L attribution, where even small inaccuracies can lead to model failure. Consistency and integrity: Requirement for consistent data definitions and treatment across various systems and departments, particularly between front office and risk management. Timeliness and currency: Strict requirements for the timely availability of market and position data for daily calculations and validations. Traceability and auditability: Comprehensive requirements for documenting data origin, transformation and use with complete audit trails.

Successful FRTB implementation requires an unprecedented level of integration between market risk management and trading areas. This new level of collaboration presents not only technical and process-related, but also significant cultural and organisational challenges that require a well-conceived integration concept. Dimensions of integration for FRTB compliance: Data integration: Harmonisation of market data, valuation models and calculation methods between trading and risk systems to create a consistent basis for P&L attribution and risk assessment. Process integration: Synchronisation of daily workflows and schedules between front office and risk management to enable timely, coordinated valuations and analyses. Organisational integration: Creation of cross-functional team and governance structures that overcome traditional silos and establish shared responsibility for FRTB compliance. Cultural integration: Development of a common language and shared understanding between traders and risk experts with alignment of incentive structures and target agreements. Technological integration: Implementation of integrated or smoothly communicating system landscapes that ensure consistent calculations and data flows.

The modellability of risk factors represents one of the greatest challenges in implementing the internal model approach (IMA) under FRTB. The strict regulatory criteria for 'Real Price Observations' (RPOs) and their verification require effective approaches and strategic data management to minimise capitalisation as Non-Modellable Risk Factors (NMRFs). Core challenges of risk factor modellability: Strict RPO criteria: The requirement for at least

24 'real' price observations per year with a maximum of one month between consecutive observations for each risk factor is barely achievable for many less liquid markets. Complex documentation obligations: Extensive documentation requirements for each individual price observation, including proof of 'authenticity' and market representativeness, represent a massive operational challenge. High NMRF capital surcharges: The capitalisation of non-modellable risk factors typically leads to significantly higher capital requirements, which can fundamentally call into question the economic viability of certain trading activities. Volatility of modellability: The dynamic nature of modellability with potential monthly changes in the status of risk factors requires flexible systems and processes.

P&L attribution (PLA) and backtesting under FRTB place particularly demanding requirements that go significantly beyond earlier regulatory standards. Their successful implementation is critical for qualification under the internal model approach (IMA) and requires a well-conceived concept as well as proven implementation strategies. Core challenges for PLA and backtesting under FRTB: Strict acceptance criteria: The new PLA tests (Spearman correlation, Kolmogorov-Smirnov test and p-value test) place significantly higher demands on the alignment between hypothetical P&L and risk-based P&L. Daily execution: Requirement for daily PLA tests and backtesting at trading desk level with extensive documentation and analysis of outliers. Complex consequences: Structured 'traffic light' system with direct impacts on capital requirements and potential reversion to the standardised approach in the event of persistent breaches. Cross-system consistency: Necessity of consistent data, models and valuation methods between front office and risk systems as a fundamental prerequisite for successful PLA. Granular documentation: Comprehensive requirements for documenting all differences, outliers and measures taken with a complete audit trail.

A forward-looking FRTB data architecture should not only meet regulatory requirements, but simultaneously serve as a strategic asset for the bank and generate business value. ADVISORI supports financial institutions in designing and implementing such a dual data architecture that intelligently combines compliance and value creation. Dual objectives of an optimal FRTB data architecture: Regulatory compliance: Full fulfilment of all FRTB-specific data requirements with the necessary granularity, historisation, quality and traceability for internal models and the standardised approach. Business value: Creation of a data infrastructure that goes beyond compliance to offer competitive advantages through improved business insights, more efficient processes and support for new business models. Future-proofing: Development of a flexible architecture that can address not only current FRTB requirements but also future regulatory changes and business requirements with minimal adjustments. Cost efficiency: Optimisation of total cost of ownership through intelligent data reuse, consolidation of redundant systems and automation of manual processes. Scalability: Ensuring sufficient scalability for growing data volumes, increasing complexity and higher performance requirements over time.

Successful FRTB implementation requires more than technical expertise and regulatory know-how — it demands well-conceived change management that effectively accompanies the far-reaching organisational, process-related and cultural changes. ADVISORI's experience shows that change management often makes the decisive difference between successful and problematic FRTB implementations. Particular change management challenges in FRTB: Cross-functional transformation: FRTB requires an unprecedented level of collaboration between traditionally separate areas such as front office, risk management, IT and finance, fundamentally challenging established silos and ways of working. Cultural differences: Bridging significant cultural differences between trading areas and risk management, which have different priorities, languages and working methods. Complexity barriers: Communicating the highly complex FRTB requirements and their implications to various stakeholders with differing technical backgrounds and levels of understanding. Long-term transformation: Managing a multi-year transformation initiative with sustained motivation and engagement of all parties over an extended period. Parallel change initiatives: Coordination with numerous other regulatory and strategic initiatives competing for the same resources and attention.

Collaboration with external solution providers is a central component of many banks' FRTB implementation strategies. A structured vendor management framework is essential for managing the complexity of these partnerships, meeting regulatory requirements and extracting maximum value from external solutions. ADVISORI supports financial institutions in developing and implementing such a tailored framework. Particular challenges in FRTB vendor management: Regulatory responsibility: Despite the use of external solutions, regulatory responsibility remains fully with the financial institution, requiring deep understanding and strict control of external solutions. Complex integration: FRTB solutions must be integrated into a complex, often historically grown system landscape, bringing with it significant technical and process-related challenges. Methodological transparency: Regulatory requirements for the transparency and traceability of the methods and models used require detailed understanding of vendor solutions. Customisation requirements: Standard solutions often need to be substantially adapted to institution-specific requirements, data structures and processes, requiring complex specification and change processes. Multi-vendor landscape: Many institutions rely on multiple specialised providers for various FRTB components, further complicating coordination and integration.

A solid governance structure is a critical success factor for both FRTB implementation and sustainable operations. It forms the foundation for clear decision-making paths, effective risk management and ensuring regulatory compliance. ADVISORI supports financial institutions in designing and implementing tailored FRTB governance frameworks. Core elements of an effective FRTB governance structure: Multi-level decision-making bodies: Establishment of a clear hierarchy of decision-making bodies, from the operational FRTB steering committee to board-level oversight, with defined responsibilities and escalation paths. Integrated front-risk governance: Creation of shared governance structures between front office and risk management to bridge traditional silos and promote shared responsibility for FRTB compliance. Clear role allocation: Precise definition of the roles and responsibilities of all stakeholders involved in FRTB implementation and operations, from trading desks through risk management and IT to senior management. Documentation and evidence obligations: Establishment of solid processes for documenting all FRTB-relevant decisions, methodologies and validations that meet the strict regulatory evidence requirements.

The implementation of the Fundamental Review of the Trading Book represents a significant investment for many financial institutions, both in terms of financial resources and personnel capacity. A well-conceived cost and resource optimisation strategy is therefore of central importance for designing the implementation efficiently and maximising the long-term return on investment. Strategic approaches to cost optimisation in FRTB: Prioritisation methodology: Development of a systematic prioritisation methodology for FRTB measures based on regulatory urgency, capital impacts and implementation effort, to optimally allocate available resources. Phase-adapted resource planning: Implementation of dynamic resource planning aligned with the various project phases, ensuring an efficient mix of internal staff, external consultants and offshore resources. System consolidation: Identification and consolidation of redundant systems and data sources as part of FRTB implementation to reduce long-term operating costs and complexity. Automation strategy: Development of a comprehensive automation strategy for manual, resource-intensive processes in areas such as data management, P&L attribution and reporting, to minimise ongoing operating costs.

A well-conceived test strategy is a critical success factor for FRTB implementation. It not only ensures adherence to regulatory requirements, but also minimises implementation risks and guarantees operational reliability. ADVISORI supports financial institutions in developing and implementing tailored FRTB test strategies that combine regulatory compliance and operational excellence in an efficient framework. Multi-dimensional test requirements under FRTB: Functional test dimensions: Comprehensive validation of all functional requirements, from the correct calculation of regulatory metrics through risk factor modellability to P&L attribution and backtesting. Non-functional test dimensions: Systematic review of non-functional requirements such as performance, scalability, data volumes and processing times, which are critical for daily FRTB operations. Data quality tests: Specific tests for validating data quality, completeness and consistency across all FRTB-relevant data sources and systems. Integration tests: Comprehensive testing of the correct integration between various system components, from trading systems through market data repositories to risk calculation and reporting platforms. Regulatory compliance tests: Dedicated tests to ensure full adherence to all regulatory requirements, including specific validations for internal models and the standardised approach.

FRTB implementation offers banks a unique opportunity to fundamentally modernise their entire market risk management infrastructure and make it future-proof. With a strategic approach, the necessary regulatory investment can be transformed into a long-term competitive advantage that goes far beyond pure compliance. Strategic modernisation opportunities through FRTB: Technological modernisation: Using FRTB implementation as a catalyst for modernising outdated technologies, migrating to cloud-based solutions and introducing flexible architectures. Data management transformation: Development of a future-capable data management infrastructure that not only meets FRTB requirements but also serves as the foundation for data-driven business strategies and advanced analyses. Process optimisation and automation: Redesign of traditional manual and fragmented processes into end-to-end automated, integrated workflows that increase efficiency and reduce operational risks. Organisational realignment: Overcoming historically grown silos between front office, risk management and finance through new, integrated organisational models that promote more agile decision-making and innovation. Analytical excellence: Building advanced analytical capabilities that go beyond minimum regulatory requirements and deliver differentiating insights for strategic business decisions.