Financial Risk Management

We help financial institutions design, build, and embed risk models that are fit for purpose, regulator-ready, and aligned to strategic decision-making. Our team combines quantitative expertise with deep business and regulatory knowledge to develop end-to-end credit risk models—covering data processing and transformation, methodology selection, calibration, validation readiness, and governance. By integrating robust model risk management and transparent documentation from day one, we enable clients to improve risk sensitivity, accelerate delivery, and strengthen confidence with supervisors, auditors, and senior management.

Our Model Validation service provides independent, rigorous assurance that risk models are conceptually sound, empirically robust, and fit for intended use—meeting supervisory expectations and internal Model Risk Management standards. We perform end-to-end validations across credit and IFRS 9 models, covering methodology review, input data and assumptions assessment, model output and back-testing, sensitivity and stress analysis, benchmarking, and ongoing performance monitoring. We deliver clear, actionable findings and remediation roadmaps, supported by audit-ready documentation and governance insights to strengthen confidence with senior management, auditors, and regulators.

We support institutions in developing robust stress testing frameworks that quantify the impact of adverse economic conditions on credit portfolios. Our practice specializes in the design of macroeconomic scenarios and the calibration of satellite models to project Probability of Default (PD) and Loss Given Default (LGD) shifts. By integrating top-down and bottom-up approaches, we ensure that stress testing serves as a functional tool for capital planning and risk appetite setting, rather than a mere compliance exercise.

Our team provides comprehensive support for the technical implementation and validation of IFRS 9 impairment models and Capital Requirements Regulation (CRR). We focus on the accuracy of Expected Credit Loss (ECL) calculations, consider "Significant Increase in Credit Risk" (SICR) triggers and forward-looking adjustments. Furthermore, we assist clients in optimizing Risk-Weighted Assets (RWA) and ensuring the accuracy of COREP and FINREP reporting, maintaining strict adherence to the latest EBA Regulatory Technical Standards (RTS) and the Supervisory (CRR/CRD) framework.

Our Credit Risk Strategy & Policy Advisory services help organizations strengthen how they define, govern, and execute credit decisioning by aligning risk appetite with business strategy and regulatory expectations. We support the design and refresh of risk appetite frameworks (including metrics, limits, governance, escalation triggers, and reporting) and translate these into practical credit and underwriting policies and standards that are clear, consistent, and auditable. We also assist with end-to-end policy implementation—covering operating model, delegation of authority, portfolio segmentation, exception management, and monitoring—so underwriting remains disciplined, scalable, and responsive to changing market conditions.

We support the design and implementation of Early Warning Systems (EWS) that provide timely, actionable signals of emerging credit deterioration and portfolio stress. Our approach combines internal performance data with macroeconomic and sector indicators, along with obligor news sentiment (for Corporate) to build calibrated triggers, scorecards, and watchlist frameworks aligned to risk appetite and collections/coverage strategies. We help embed EWS into end-to-end governance and operating processes—case management, escalation, MI dashboards, and monitoring—so clients can intervene earlier, reduce surprises, and improve portfolio outcomes under normal and stressed conditions.

Our Actuarial Risk and Insurance Advisory services support insurers in
strengthening financial resilience, enhancing reporting transparency, and driving informed strategic decision-making in an increasingly complex regulatory environment. We assist organizations in the actuarial valuation of technical provisions and provide end-to-end support on statutory financial condition requirements, including regulatory engagement, statutory submissions, and expense analysis, ensuring compliance while maintaining robust financial governance. We combine advanced actuarial modelling and analytical techniques to support profit testing, leveraging methodologies such as stochastic simulations and frequency-severity modelling. In parallel, we support insurers in the ongoing application and optimization of IFRS 17, covering actuarial methodology refinement, financial impact analysis, and post-implementation enhancements. We also provide transition support where required, as well as audit and assurance services, helping ensure sustained compliance, robustness, and transparency in financial reporting. Our expertise extends to IFRS 17 business planning, where we develop sophisticated financial models to support balance sheet optimization, capital planning, and performance analysis. Additionally, we support capital modelling initiatives in line with evolving risk-based regulatory regimes such as Solvency II, covering model development, calibration, and forward-looking capital projections. Finally, we provide actuarial support across due diligence and M&A transactions, delivering independent insights on liability valuation and the fair value of insurance portfolios, enabling clients to execute transactions with confidence and a clear understanding of underlying risks and opportunities.

We help risk functions build trusted, scalable Data Management & Analytics capabilities that turn fragmented data into decision-grade insight. Our services span data collection, data cleansing and controls, data quality management, and the design of robust risk data marts aligned to BCBS 239 and internal data governance policies. Leveraging modern analytics and automation, we enable faster reporting, stronger portfolio monitoring, and advanced use cases such as predictive risk insights, while ensuring transparency, auditability, and regulatory readiness.

We assist institutions in the enhancement of their Internal Capital Adequacy Assessment Process (ICAAP) and Internal Liquidity Adequacy Assessment Process (ILAAP), ensuring alignment with the ECB’s multi-year guides. Our strength lies in bridging the gap between regulatory requirements and internal risk management, considering key risk capital and liquidity indicators. We provide quantitative support for capital buffer management and the development of liquidity contingency funding plans (CFP) under stressed market conditions.

Our practice offers specialized guidance on Interest Rate Risk in the Banking Book (IRRBB) and Credit Spread Risk in the Banking Book (CSRBB). We support clients in implementing the Standardized Outlier Test (SOT) for both Economic Value of Equity (EVE) and Net Interest Income (NII) perspectives. Our team provides the technical expertise required to model behavioral assumptions—such as non-maturity deposits (NMDs) and prepayment risks—and to define the boundaries of CSRBB within the banking book perimeter.

We provide end-to-end support in the drafting and operationalization of Recovery Plans under the Bank Recovery and Resolution Directive (BRRD). Our team ensures that recovery indicators, triggers, and options are sufficiently calibrated to restore financial viability during severe stress. In the Recovery and Resolution planning continuum, we support our clients in meeting the Single Resolution Board’s (SRB) expectations across all resolvability dimensions, from Minimum Requirement for own funds and Eligible Liabilities (MREL), valuation capabilities, Operational Continuity in Resolution to Communication, Governance and Separability.

We help banks assess, design, and implement enhancements to their Risk Data Aggregation and Risk Reporting (RDARR) capabilities in line with BCBS 239 principles and supervisory expectations. Our support spans end-to-end RDARR—from data sourcing and lineage through aggregation, reporting, and governance—focusing on accuracy, completeness, timeliness, and adaptability. We conduct gap assessments, remediation roadmaps, detailed time plans communicated to the supervisory authorities, and strengthen the operating model (e.g., roles and responsibilities, policies/standards, data ownership). We also help establish sustainable controls such as critical data element (CDE) frameworks, data quality rules and dashboards, reconciliations between Finance and Risk, and evidence packs for internal audit and supervisors. Where needed, we support tooling enablement (metadata/catalog, lineage, workflow) and integration with broader data and regulatory programmes. The result is more reliable risk information, faster management decision-making, and improved readiness for regulatory reviews and stress scenarios.

We support banks in translating BCBS 239 principles into a practical, sustainable RDARR implementation. Our work typically starts with defining the RDARR framework and scope of application (material legal entities, material risk reports, key risk indicators, material risk management models) and establishing the appropriate governance for steering the institution towards compliance as well as for ongoing monitoring. We help identify and maintain Critical Data Elements (CDEs) linked to material key risk indicators, help establish data governance, data management and data quality monitoring frameworks and policies. In parallel, we support clients with documenting end-to-end data lineage (source-to-report) and key transformations, enabling transparency, impact analysis, and auditability. Where required, we support implementation of  supporting tooling and workflows (metadata management, lineage repositories, control attestations) and help produce regulator-ready operationalization documents. The outcome is a scalable RDARR capability that improves data reliability, accelerates change, and strengthens supervisory confidence.

We help banks strengthen regulatory reporting end-to-end—improving accuracy, consistency, timeliness, and
auditability across the reporting lifecycle. Our support covers diagnostic reviews of current processes and controls, remediation of recurring defects, and the design of a robust target operating model with clear ownership, documentation, and sign-off. We enhance control frameworks through automated validations, completeness and plausibility checks, and structured reconciliations between Regulatory Reporting, Finance, and Risk (e.g., balances, RWA drivers, key prudential metrics). Where appropriate, we support automation and workflow enablement—standardised data sourcing, rule-based transformations, control dashboards, and evidence retention—to reduce manual effort and operational risk. We also help clients implement sustainable governance, including change management for regulatory updates, issue tracking and root-cause analysis, and management information to monitor performance and compliance. The result is higher-quality submissions, fewer late-cycle adjustments, and improved readiness for internal audit and supervisory reviews.

We support clients in implementing and integrating risk models into production risk platforms and calculation engines—bridging model development with scalable, controlled, and auditable deployment. Our services cover requirements definition and solution design, technical implementation of model logic, and integration into upstream data pipelines and downstream reporting and decision processes. We help establish robust model execution frameworks, including batch orchestration, performance optimisation, versioning, and controlled releases, as well as end-to-end testing (unit, integration, regression, and reconciliation against development outputs). We also support governance and control requirements-documentation, change management, access controls, and monitoring of model runs and exceptions—aligned to model risk management and internal audit expectations. Where relevant, we assist with parallel runs, cutover planning, and handover to BAU teams. The outcome is reliable model outputs, faster time-to-production, and stronger traceability from input data to reported risk metrics.