Value Proposition for Financial Risk Management

We transform complex risk data into actionable insights that enhance risk management strategies for banks. Our expertise covers the entire risk data lifecycle, from collection to analysis. We design and implement robust risk management frameworks and data architectures that empower strategic decision-making, ensure regulatory compliance, and drive measurable outcomes in risk mitigation and financial stability.

What role should DM play in RM?

Data management is fundamental to effective risk management in financial institutions. It ensures data accuracy, consistency, and integration across all risk areas and entities, enabling reliable risk assessments and reporting. Strong and resilient data management supports regulatory compliance (e.g., BCBS 239 or Model Risk Management), allowing timely, accurate, and adaptable risk data aggregation and reporting. Additionally, it enhances real-time risk monitoring, governance, and the flexibility needed to adapt to evolving risk landscapes. In short, robust data management drives informed decision-making and proactive risk mitigation while aligning with regulatory demands.

Solve your strategic and technical risk management challenges

A high-performance data platform is a high-performance risk management platform. Drawing on deep and broad technical and risk management experience, we have successfully addressed every risk management challenge the ever evolving markets and regulatory requirements can pose.

Ever-changing environment

How well is your current Risk Data platform performing?

We ensure that your platforms not only meet regulatory standards but also adapt quickly to evolving requirements. Our solutions integrate seamlessly into your existing systems, reducing complexity while enhancing reliability and reporting efficiency.

Ever-changing markets

Are you able to perform efficient accurate scenario analysis for your entire bank?

We ensure your ability to perform ad-hoc simulations for quick and accurate scenario analysis. Our solutions integrate consistently data across finance and risk, underpinned by a robust data management framework and comprehensive modeling approach.

Complex (regulatory) modeling world

How confident are you that your models are driving real business value?

We understand that model risk management should do more than just meet regulatory standards—it should drive value and streamline decision-making. We specialize in designing frameworks that align robust compliance with lean, efficient processes.

Why is the full implementation of the BCBS239 principles stagnating?

The BCBS 239 progress report from late 2023 highlights that, despite notable improvements, many banks continue to face stagnation in key areas of building a high-performance data platform. One of the primary reasons, in our view, is that most banks still depend on complex legacy systems that are not designed to keep pace with the growing demands of increasing data volumes and new processes from expanding data sources. In today’s business and regulatory environment, which requires fast, reliable, and highly-aggregated risk data, these outdated platforms struggle to meet evolving expectations.

Have a look at the most common stagnation points and how the data platforms with associated data tools have developed to meet the ever-growing data challenges.

Stagnation Points

Data Governance & Accountability
Data Quality
Management
Data Lineage, Change Management & Traceability
Scalability and Flexibility of Data Architecture and IT Infrastructure
Timeliness and Accuracy of Risk Data Aggregation and Reporting
Adaptability to Changing Regulatory and Business Requirements

Key Adoption challenges

  • To establish a strong, relevant enterprise-wide data governance framework
  • Often gaps in defining clear roles and responsibilities for data management, including data ownership and stewardship

Key Stagnation Points

  • Inadequate senior management involvement
  • Poor alignment of data governance frameworks with business objectives and risk management practices
  • Insufficient investment in data governance capabilities, such as dedicated teams or data stewardship roles

Governance

Data Governance ensures data quality, security, and compliance by defining roles and policies for managing data access and usage, supporting business goals, and reducing risks.

Orchestration

Data Orchestration automates complex data workflows, ensuring seamless data integration, transformation, and delivery across diverse systems at scale, from ingestion to final report/dashboard delivery, including Data Quality and complex ML/AI operations.

Catalog

The Data Catalog centralizes data management, organizing metadata to support compliance and meet key BCBS requirements. It enables teams to discover, certify, and collaborate on trusted assets while surfacing data quality and observability results for better governance.

Data Observability

Data Observability is handled mostly by tooling for automated monitoring and testing of data quality, freshness, and schema across pipelines. Generates alerts and test results for data assets. Supports the data catalog in surfacing insights for manual certification and approval processes

Data Governance & Accountability

How to overcome the stagnation points

  • Involve more the main business stakeholders from risk and finance side incl. Senior Management showing the added value of a strong data governance for the business
  • Align data governance with business objectives and connect it with the right incentives
  • Build connected teams across IT and business and ensure to find  the right preventive and detective controls incl documentation
  • Use technical developments like Tag-based vs role-based access control, leveraging IdP attribute information, to automate policy enforcement
Governance

Data Governance ensures data quality, security, and compliance by defining roles and policies for managing data access and usage, supporting business goals, and reducing risks.

Orchestration

Data Orchestration automates complex data workflows, ensuring seamless data integration, transformation, and delivery across diverse systems at scale, from ingestion to final report/dashboard delivery, including Data Quality and complex ML/AI operations.

Catalog

The Data Catalog centralizes data management, organizing metadata to support compliance and meet key BCBS requirements . It enables teams to discover, certify, and collaborate on trusted assets while surfacing data quality and observability results for better governance.

Data Observability

Data Observability is handled mostly by tooling for automated monitoring and testing of data quality, freshness, and schema across pipelines. Generates alerts and test results for data assets. Supports the data catalog in surfacing insights for manual certification and approval processes

Key Adoption challenges

  • Data quality remains a significant challenge, with many banks failing to achieve consistent data accuracy, completeness, and reliability across their data assets

Key Stagnation Points

  • Lack of robust data quality controls and processes to monitor, measure, and remediate data quality issues
  • Inconsistent application of data quality standards across different business units and regions
  • Failure to implement automated data validation and reconciliation processes, leading to manual errors and delays

Governance

Data Governance ensures data quality, security, and compliance by defining roles and policies for managing data access and usage, supporting business goals, and reducing risks.

Orchestration

Data Orchestration automates complex data workflows, ensuring seamless data integration, transformation, and delivery across diverse systems at scale, from ingestion to final report/dashboard delivery, including Data Quality and complex ML/AI operations.

Catalog

The Data Catalog centralizes data management, organizing metadata to support compliance and meet key BCBS requirements. It enables teams to discover, certify, and collaborate on trusted assets while surfacing data quality and observability results for better governance.

Data Observability

Data Observability is handled mostly by tooling for automated monitoring and testing of data quality, freshness, and schema across pipelines. Generates alerts and test results for data assets. Supports the data catalog in surfacing insights for manual certification and approval processes

Data Quality
Management

How to overcome the stagnation points

  • Build up data observability platforms which enable you to implement neatly data set certifications (data contracts) for a flexible DQ monitoring environment invariant under ever-coming changes across the data flow
  • Use of data observability platforms also allow you as business to easily implement automated data validation and reconciliation processes saving a lot of tedious manual work or even enlightening blind spots and fostering collaboration with IT
  • DQ issues coming from ever-happening changes upstream the data flow can be detected much easier based on agreed data set certifications that are added in the development process and changes can be communicated over the data catalogue to downstream data owners
Governance

Data Governance ensures data quality, security, and compliance by defining roles and policies for managing data access and usage, supporting business goals, and reducing risks.

Orchestration

Data Orchestration automates complex data workflows, ensuring seamless data integration, transformation, and delivery across diverse systems at scale, from ingestion to final report/dashboard delivery, including Data Quality and complex ML/AI operations.

Catalog

The Data Catalog centralizes data management, organizing metadata to support compliance and meet key BCBS requirements . It enables teams to discover, certify, and collaborate on trusted assets while surfacing data quality and observability results for better governance.

Data Observability

Data Observability is handled mostly by tooling for automated monitoring and testing of data quality, freshness, and schema across pipelines. Generates alerts and test results for data assets. Supports the data catalog in surfacing insights for manual certification and approval processes

Key Adoption challenges

  • Results in reports are hard to be traced back and explained
  • No common understanding about internal data
  • Data consumers are not aware of underlying data changes and the state that the consumed information is in

Key Stagnation Points

  • Missing complete lineage
  • Lineage generation needs manual intervention
  • No common taxonomy
  • Lack of documentation automation
  • Discovery automation
  • Visualization of data pipelines

Governance

Data Governance ensures data quality, security, and compliance by defining roles and policies for managing data access and usage, supporting business goals, and reducing risks.

Orchestration

Data Orchestration automates complex data workflows, ensuring seamless data integration, transformation, and delivery across diverse systems at scale, from ingestion to final report/dashboard delivery, including Data Quality and complex ML/AI operations.

Catalog

The Data Catalog centralizes data management, organizing metadata to support compliance and meet key BCBS requirements. It enables teams to discover, certify, and collaborate on trusted assets while surfacing data quality and observability results for better governance.

Data Observability

Data Observability is handled mostly by tooling for automated monitoring and testing of data quality, freshness, and schema across pipelines. Generates alerts and test results for data assets. Supports the data catalog in surfacing insights for manual certification and approval processes

Data Lineage, Change Management & Traceability

How to overcome the stagnation points

  • Implement data cataloguing systems for a holistic view of data flows from source to reporting. These act as flexible, scalable repositories mapping data lineage and metadata.
  • Enable automated tracking of schema updates (new, changed, or deleted) and visualize the data landscape. This enhances data transparency and allows for easy identification of data relationships and dependencies.
Governance

Data Governance ensures data quality, security, and compliance by defining roles and policies for managing data access and usage, supporting business goals, and reducing risks.

Orchestration

Data Orchestration automates complex data workflows, ensuring seamless data integration, transformation, and delivery across diverse systems at scale, from ingestion to final report/dashboard delivery, including Data Quality and complex ML/AI operations.

Catalog

The Data Catalog centralizes data management, organizing metadata to support compliance and meet key BCBS requirements . It enables teams to discover, certify, and collaborate on trusted assets while surfacing data quality and observability results for better governance.

Data Observability

Data Observability is handled mostly by tooling for automated monitoring and testing of data quality, freshness, and schema across pipelines. Generates alerts and test results for data assets. Supports the data catalog in surfacing insights for manual certification and approval processes

Key Adoption challenges

  • Many banks' data architectures and IT infrastructures are not sufficiently scalable, flexible, or integrated to support comprehensive risk data aggregation and reporting

Key Stagnation Points

  • Legacy systems and architectures that are not easily adaptable to new data sources or regulatory changes
  • Lack of integration between risk data systems, resulting in inconsistent data across different risk types and functions
  • Inadequate investment in modern data infrastructure, such as cloud-based solutions or data lakes, to enhance scalability and adaptability

Governance

Data Governance ensures data quality, security, and compliance by defining roles and policies for managing data access and usage, supporting business goals, and reducing risks.

Orchestration

Data Orchestration automates complex data workflows, ensuring seamless data integration, transformation, and delivery across diverse systems at scale, from ingestion to final report/dashboard delivery, including Data Quality and complex ML/AI operations.

Catalog

The Data Catalog centralizes data management, organizing metadata to support compliance and meet key BCBS requirements. It enables teams to discover, certify, and collaborate on trusted assets while surfacing data quality and observability results for better governance.

Data Observability

Data Observability is handled mostly by tooling for automated monitoring and testing of data quality, freshness, and schema across pipelines. Generates alerts and test results for data assets. Supports the data catalog in surfacing insights for manual certification and approval processes

Scalability and Flexibility of Data Architecture and IT Infrastructure

How to overcome the stagnation points

  • Modernize data infrastructure - Invest in advanced, proven systems and techniques to overcome legacy systems and data silos. Leverage cloud offerings for scalable compute power and cost-effective storage
  • Adopt a hybrid approach allowing banks to choose their level of involvement. Decide which aspects to manage in-house and which to outsource to specialists or leverage third-party tools, balancing control and expertise
  • Data Vault offers a proven, strategic methodology to the ongoing challenge of managing change within data platforms. Its architecture enables seamless integration, adaptation, and decommissioning of data sources, all without operational disruption—ensuring agility in a fast-evolving regulatory and business environment
  • Modern data platforms residing in the cloud come with an offering to satisfy data warehousing and data science. This enables data science to leverage curated data which allows for faster time-to-market and the productionization of ML (machine learning) data pipelines
Governance

Data Governance ensures data quality, security, and compliance by defining roles and policies for managing data access and usage, supporting business goals, and reducing risks.

Orchestration

Data Orchestration automates complex data workflows, ensuring seamless data integration, transformation, and delivery across diverse systems at scale, from ingestion to final report/dashboard delivery, including Data Quality and complex ML/AI operations.

Catalog

The Data Catalog centralizes data management, organizing metadata to support compliance and meet key BCBS requirements . It enables teams to discover, certify, and collaborate on trusted assets while surfacing data quality and observability results for better governance.

Data Observability

Data Observability is handled mostly by tooling for automated monitoring and testing of data quality, freshness, and schema across pipelines. Generates alerts and test results for data assets. Supports the data catalog in surfacing insights for manual certification and approval processes

Key Adoption challenges

  • The ability to aggregate and report risk data in a timely and accurate manner remains a persistent problem, affecting banks' decision-making and regulatory compliance

Key Stagnation Points

  • Inconsistent reporting timelines across different risk categories, leading to delays in consolidated risk reporting
  • Manual and inefficient data aggregation processes that compromise both timeliness and accuracy
  • Lack of a unified, automated risk reporting framework that supports both routine and ad-hoc reporting needs

Governance

Data Governance ensures data quality, security, and compliance by defining roles and policies for managing data access and usage, supporting business goals, and reducing risks.

Orchestration

Data Orchestration automates complex data workflows, ensuring seamless data integration, transformation, and delivery across diverse systems at scale, from ingestion to final report/dashboard delivery, including Data Quality and complex ML/AI operations.

Catalog

The Data Catalog centralizes data management, organizing metadata to support compliance and meet key BCBS requirements. It enables teams to discover, certify, and collaborate on trusted assets while surfacing data quality and observability results for better governance.

Data Observability

Data Observability is handled mostly by tooling for automated monitoring and testing of data quality, freshness, and schema across pipelines. Generates alerts and test results for data assets. Supports the data catalog in surfacing insights for manual certification and approval processes

Timeliness and Accuracy of Risk Data Aggregation and Reporting

How to overcome the stagnation points

  • Modern systems allow for scaling up/down individual workloads as needed to ensure the appropriate resources required for a timely delivery of the aggregated risk data
  • The Data Vault methodology will significantly improve processing efficiency due to its ability to parallelize workloads, sparse key constructs and highly standardized patterns. When combined with AI/ML capabilities, it greatly enhances risk data aggregation, particularly for scenario analysis, providing faster, more accurate insights
Governance

Data Governance ensures data quality, security, and compliance by defining roles and policies for managing data access and usage, supporting business goals, and reducing risks.

Orchestration

Data Orchestration automates complex data workflows, ensuring seamless data integration, transformation, and delivery across diverse systems at scale, from ingestion to final report/dashboard delivery, including Data Quality and complex ML/AI operations.

Catalog

The Data Catalog centralizes data management, organizing metadata to support compliance and meet key BCBS requirements . It enables teams to discover, certify, and collaborate on trusted assets while surfacing data quality and observability results for better governance.

Data Observability

Data Observability is handled mostly by tooling for automated monitoring and testing of data quality, freshness, and schema across pipelines. Generates alerts and test results for data assets. Supports the data catalog in surfacing insights for manual certification and approval processes

Key Adoption challenges

  • Many banks struggle to maintain compliance with BCBS 239 due to their inability to adapt to changing regulatory requirements, business models, or risk environments

Key Stagnation Points

  • Rigid data architectures and governance frameworks that do not easily accommodate new regulatory demands or internal changes
  • Slow response times to evolving regulatory expectations, resulting in compliance gaps and regulatory scrutiny
  • Challenges in scaling data management practices to new geographies, products, or risk types

Governance

Data Governance ensures data quality, security, and compliance by defining roles and policies for managing data access and usage, supporting business goals, and reducing risks.

Orchestration

Data Orchestration automates complex data workflows, ensuring seamless data integration, transformation, and delivery across diverse systems at scale, from ingestion to final report/dashboard delivery, including Data Quality and complex ML/AI operations.

Catalog

The Data Catalog centralizes data management, organizing metadata to support compliance and meet key BCBS requirements. It enables teams to discover, certify, and collaborate on trusted assets while surfacing data quality and observability results for better governance.

Data Observability

Data Observability is handled mostly by tooling for automated monitoring and testing of data quality, freshness, and schema across pipelines. Generates alerts and test results for data assets. Supports the data catalog in surfacing insights for manual certification and approval processes

Adaptability to Changing Regulatory and Business Requirements

How to overcome the stagnation points:

  • As for all other challenges, a flexible, scalable data platform adopts almost automatically with changing environments since built for that
  • Timely inclusion of new requirements, entities or divisions or risk measurements are at the heart of the next-gen data platforms and data tools
  • Modelling techniques and semantic layers like the one provided by DBT can help end users and the data teams to quickly provide new data or derive new information within tools like excel, tableau, etc

For example, a semantic layer is providing for different data consumers the comprehensive data sets including a corporation-wide unified and preferably agreed-upon business logic. This relieves the end users from creating complex measures and ensures that the same calculations are being applied in the same context across the corporation.

Governance

Data Governance ensures data quality, security, and compliance by defining roles and policies for managing data access and usage, supporting business goals, and reducing risks.

Orchestration

Data Orchestration automates complex data workflows, ensuring seamless data integration, transformation, and delivery across diverse systems at scale, from ingestion to final report/dashboard delivery, including Data Quality and complex ML/AI operations.

Catalog

The Data Catalog centralizes data management, organizing metadata to support compliance and meet key BCBS requirements . It enables teams to discover, certify, and collaborate on trusted assets while surfacing data quality and observability results for better governance.

Data Observability

Data Observability is handled mostly by tooling for automated monitoring and testing of data quality, freshness, and schema across pipelines. Generates alerts and test results for data assets. Supports the data catalog in surfacing insights for manual certification and approval processes

How well is your
MRM-platform put together?

Want to know how a well-designed Model Risk Management framework ensures compliance, optimizes processes, and turns regulatory demands into strategic value for your business? Start here.

Key Challenges
  • Data quality
  • Integration of various data sources across entities
  • Timeliness of data inputs
What They Impact
  • Regulatory compliant and accurate models
  • Operational efficiency
  • Intransparent decision taking
Key Challenges
  • Complex development and validation processes regarding data sampling and analysis
  • Enough independent challenge
  • Enough business understanding
What They Impact
  • Regulatory compliant and accurate models
  • Operational efficiency
  • Missing buy-in from business side
Key Challenges
  • Aggregation of model results and its model risks
  • Good (visual) reporting of results and risks for senior management finding the right balance of aggregated information
  • Lean reporting process
What They Impact
  • Regulatory compliant reporting
  • Efficient and effective decision taking
  • Operational efficiency
Inputs
Model Lifecycle
Outputs

Solution

With dedicated data quality and data monitoring tools you can enable integrated DQ monitoring and testing with proven, seamless interoperability.

An easy access to internal & external data simplifies significantly model development and model testing and monitoring processes. One key advantage is the direct but also parallel access to productive data within a cloud-based data platform.

The set up and use of data catalogs and data contracts within the data platform supports a seamless and BCBS239-compliant data lineage and leads to significantly less operational burden enhancing a stable high quality data flow environment.

Solution

With dedicated data quality and data monitoring tools you can enable integrated DQ monitoring and testing with proven, seamless interoperability.

An easy access to internal & external data simplifies significantly model development and model testing and monitoring processes. One key advantage is the direct but also parallel access to productive data within a cloud-based data platform.

The set up and use of data catalogs and data contracts within the data platform supports a seamless and BCBS239-compliant data lineage and leads to significantly less operational burden enhancing a stable high quality data flow environment.

Solution

Take advantage of reusable data sets and features through direct but parallel access to productive data.

Benefit from a build-in model registry as the basis for a regulatory compliant model inventory to keep track of models and its results.

Build up of an automated model validation framework simplified through a common data basis for data providers, model developers and model validators.

Solution

Take advantage of reusable data sets and features through direct but parallel access to productive data.

Benefit from a build-in model registry as the basis for a regulatory compliant model inventory to keep track of models and its results.

Build up of an automated model validation framework simplified through a common data basis for data providers, model developers and model validators.

Solution

Make use of user-friendly visual analytics solutions that can be well integrated within your existing data platform.

Keep an auditable and agile data warehouse methodology across the organization with data vault 2.0 for a compliant aggregation of risk metrics.

Set up a semantic layer for a lean management reporting process without sacrificing transparency, data quality and adding additional processing time.

Solution

Make use of user-friendly visual analytics solutions that can be well integrated within your existing data platform.

Keep an auditable and agile data warehouse methodology across the organization with data vault 2.0 for a compliant aggregation of risk metrics.

Set up a semantic layer for a lean management reporting process without sacrificing transparency, data quality and adding additional processing time.

We can help you to understand what is possible and how to optimize efficiently your data platform to meet current and future data challenges.
Let us show you the next generation of data platforms.

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