Utilising data to enhance operational risk scenario and capital assessments for investment management firms
Insights and practical steps from analysis of ORIC International's investment management consortium data
Download the white paper executive summary
Operational risk scenario analysis plays a critical role in strengthening risk governance and informing capital assessments across investment management firms. However, many firms continue to face challenges in consistently integrating internal loss event data (ILD), external loss event data (ELD), and expert judgement within their scenario and capital frameworks. This can lead to scenario outcomes that are less comparable over time, less defensible to senior stakeholders, more difficult to assess consistently in a supervisory context, and less useful in informing capital decisions and driving actionable risk management decisions.
A key driver of this inconsistency is the absence of a clearly articulated and widely adopted, end-to-end implementation architecture for scenario-based operational risk capital in the investment management sector. Critical design decisions, including scenario risk paradigm selection, severity quantile definition, integration of ILD and ELD, and the treatment of distributional assumptions, are often embedded within practice without explicit comparison of alternatives or systematic testing of their implications. As these choices can materially influence capital outcomes, variation in implementation can lead to materially different capital assessments across firms facing similar underlying risk exposures.
In this white paper, Deloitte and ORIC International (ORIC) have built on a long-standing relationship and shared engagement with investment management firms. With many investment management firms engaging with both ORIC’s data consortium and Deloitte’s operational risk programmes, we have repeatedly observed common challenges in how loss data is interpreted and translated into scenario design, calibration and capital outcomes. In that context, Deloitte and ORIC partnered to apply a structured and disciplined analytical approach to ORIC’s investment management loss dataset, testing hypotheses against observed loss experience and jointly interpreting the findings. The insights and techniques in this white paper are shared in the same spirit that underpins ORIC’s practitioner-led network: openness, shared learning and data-driven insight with the aim of supporting continuous improvement in scenario analysis and operational risk decision-making across the sector.
This white paper presents the findings of that analysis, based on over 2,000 material operational loss events from 2005 to 2025 drawn from ORIC’s investment management loss dataset. The paper has two objectives. First, it examines the operational risk loss landscape of the investment management sector as reflected in this dataset, highlighting the scenarios, risk drivers and structural patterns that most consistently shape observed loss experience. Second, it sets out a structured implementation framework for scenario-based capital modelling. The analysis is grounded in observed loss experience where empirical evidence is informative; however, scenario-based capital frameworks also involve structural design choices that cannot be resolved by data alone. We therefore examine alternative approaches explicitly, assess their implications for calibration stability and capital impact, and provide a transparent, repeatable and governance-aligned method for integrating ILD, ELD and expert judgement into scenario analysis and capital assessment.
This executive summary focuses primarily on the headline findings and key implications. The full technical version provides the detailed step-by-step framework and practical application.
While the analysis is grounded in investment management-specific loss data and written with investment management firms’ scenario and capital processes in mind, many of the concepts, analytical techniques, and governance considerations are likely to be relevant to other regulated sectors. In particular, banks, undertaking Internal Capital Adequacy Assessment Process (ICAAP) submissions, and insurers, performing Own Risk and Solvency Assessment (ORSA) exercises, may find elements of the approach informative, where similar challenges arise in combining loss data, expert judgement, and capital modelling.
This white paper is designed for three primary audiences:
- C-suite leaders, boards, and heads of operational risk, who can use the findings to benchmark scenario selection, capital drivers, and risk governance against broader industry patterns (see section 1.2: Headline findings);
- Operational risk practitioners facilitating scenario workshops, who can apply scenario-level insights (including typical loss frequency and severity behaviour) to improve consistency, reduce bias, and strengthen challenge and discussion (see section 1.3: Using our findings within your scenario workshops); and
- Operational risk data and modelling professionals, who can use the techniques and empirical observations to inform data processing, scenario mapping, model calibration, validation, and ongoing governance (see section 1.4: Key technical findings).
Key features of this white paper include:
- practical guidance to support the consistent categorisation of complex operational loss events into relevant scenarios;
- discussion of the analytical and judgement-based decisions which firms face when interpreting loss experience - including frequency, severity magnitude and severity volatility – and the implications these decisions can have for capital outcomes; and
- benchmarking insights intended to complement, rather than replace, firm-specific analysis and expert judgement.
Differences in business models, control environments, risk appetites, and materiality thresholds mean that no external dataset can provide a one-size-fits-all solution. Accordingly, this white paper presents a structured and evidence-based implementation framework to support more consistent, transparent and defensible scenario definition and calibration, together with stronger governance and validation practices in operational risk assessment.
The framework is intended to complement, not replace, firm-specific analysis, internal data, and expert judgement. Where helpful, the paper introduces practical working concepts and terminology used consistently throughout to support clearer discussion and more repeatable scenario workshops and calibration exercises.
The paper does not propose new academic statistical methods. Instead, it focuses on strengthening implementation discipline, benchmarking interpretation, and decision transparency when applying operational loss data and scenario analysis in practice.
We hope that by engaging with these insights, investment management firms can strengthen operational risk practices through more consistent scenario definition and calibration, clearer benchmarking and challenge, and more robust validation and backtesting, helping to align internal approaches with evolving sector standards.
