We help banks, credit unions, and regulators improve risk-adjusted performance and ensure regulatory compliance

Risk Dynamics’ financial risk management services range from developing and validating traditional credit and stress-testing models to creating advanced artificial intelligence/machine learning models that cover a wide range of risk types.

What do we do?

We approach the development and validation of regulatory and management models holistically. We combine deep industry insight and strategic skills with:

  • A structured risk-management approach
  • Proven methodologies focused on transformation
  • Analytical tools
  • Practical implementation
Our banking team comprises over 150 experts. We also leverage McKinsey’s Banking and Risk practices and collaborate with external advisers who have decades of relevant experience.
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Our impact

For both global and regional banks, we have made a profound impact by providing:

  • Comprehensive value propositions for risk models, including review, model development, and model validation to ensure effective, efficient compliance
  • Robust model development and governance for new model types
  • A sound approach to risk data management, including data architecture, governance, and data validation
  • Industry insights using our proprietary benchmarking tools

Case studies

  • Review of IFRS9 models for retail and corporate bank segments

    Risk Dynamics supported a leading European bank by providing an independent review of the bank’s International Financial Reporting Standard 9 (IFRS9) models. The models covered the bank’s retail and corporate business segments and were developed to facilitate the calculation of expected credit losses (ECL). These two segments represented over 95 percent of the bank’s credit exposure.

    To improve the accuracy of the IFRS9 models, we challenged and developed new modelling techniques that reflected more accurate provisions and ensured compliance with regulatory requirements. Our work included the formulation of both collective and individual provisioning methodologies. As a result, we detected and reduced implicit conservatism in provisions by more than 2 percent.

  • Review and improvement of complicated model landscape

    For a large, universal, global bank, we strategically reviewed the model landscape and developed internal ratings-based (IRB) models across multiple jurisdictions and regulatory regimes. The bank’s decentralized operating model had created a highly fragmented model landscape that was complex and costly to maintain.

    Risk Dynamics simplified and optimized the target model landscape. We took into account key considerations, such as data requirements and global capital traps, and created an example action plan outlining key steps and timelines. The project enabled the client to save up to 30 percent in implementation costs and up to 20 percent in future maintenance costs, while still protecting capital efficiency and increasing the client’s flexibility and agility for future response times.

  • Building a set of credit and PPNR models for loss mitigation and stress-testing programs

    Risk Dynamics helped a leading US bank to build a unified set of credit and pre-provision net revenue (PPNR) models for their loss mitigation and stress-testing programs. This effort included an analysis of the bank’s model landscape to ensure that the models (both statistical and non-statistical) were efficient and effective.

    We developed more than 100 equations to model probability of default (PD), loss given default (LGD), and exposure at default (EAD) at a granular level for loss. We also developed equations for sales, payments, and new originations at an account level to project losses and balances. These models allowed the bank to fully understand its portfolio at a microsegment level and to ask “what if” questions to sharpen its future account strategies.

  • Establishing an off-shore model validation center

    We assisted a leading US bank in conceptualizing and actualizing an off-shore validation center. Specifically, Risk Dynamics developed the bank’s talent strategy for the center and provided on-the-job training for the newly hired team.


    Over time, we built up the team’s skills by validating more than 200 models side by side, sharing best practices, and materially moving their validation process forward. This new team’s work is expected to save the bank over $2 million annually.

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