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