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Model governance solutions

Our viewpoint on your model governance challenges.

We are presenting you with a set of tools and best practices based on our experience to enhance your company's risk management effectiveness, bringing you a competitive advantage.

THREE PHASES TO GET THE GREATEST VALUE FROM THE MODEL GOVERNANCE FRAMEWORK

There are no shortcuts. We know that an effective governance model is a long-term process and cannot happen immediately. To get the maximum benefit of your model governance it is necessary to see the entire process through, and we divide this path into three phases.

Phase 1: ESSENTIALS

A simple step to get organized and ready.

Model inventory
Catalogue of validations
Effective data management
Documentation

Phase 2: AUTOMATION

Say goodbye to monkey work.

Process automation
Integration to your systems
Automated reporting
Centers of excellence
Automation in model risk management

Phase 3: BUSINESS VALUE

Discover the real value of your model governance.

Optimized resource management
Process efficiency tracking
Automated model tiering
... and many others.

Our take on the model governance and much more - meet MGS

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Model Inventory

Model inventory contains all model information in a structured manner and should be able to store various attributes connected to a model and model lifecycle. Based on our experience, the good inventory needs to be able to quickly provide you with trustworthy answers to questions like: "Which currently valid models cover portfolio A in of branch B?" or "Which models provide input for model A and what is their current validation/implementation/risk score?"

Data

All sorts of data are essential for model development and monitoring. Certain data sets might be useful to keep for the entire model lifecycle, but others might play a temporary role for a particular monitoring task. The robust model governance framework should ensure that the process of data collection and exchange between various components is user-friendly and intuitive. Experts shouldn't spend time on "hunting down that latest dataset", but instead the system needs to transparently define roles for data collection and automated ETL processes.

Documentation

There are many types of documents out there - regulatory documentation, internal directives, and guidelines which need to be organized. Model documentation and validation results need to be created in a collaborative way based on the data analysis and team experience. The system should help organise and reduce typical repetitive tasks. Tracking all the changes with the possibility to compare or return to the previous version would be a nice bonus.

Workflows

The model lifecycle is all about various human interactions based on a defined scenario - workflows. The good workflow engine should be flexible in configuration but also intuitively lead users through. Its expression language should allow conditional branching, addressing several stakeholders in parallel, and, at the end, good visualization of the current status. All of this is necessary to formalize all the existing or newly introduced workflows.

Reporting

Access to all the model information is crucial for building your monitoring dashboards or any sort of ad-hoc reports. Accurate, real-time information allows you to maximise the effectiveness of your decision process and brings more confidence for key decisions.

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Automation

Many processes associated with model development and monitoring contain significant portions of repetitive work. The automation framework needs to be able to define flexible processes with rich integration capabilities - integration with internal components as well as external systems. And execution traceability is an important factor here. Managing of automated processes needs to be intuitively accessible from the user interface.

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Model risk management framework

The costs of managing model risk have never been higher, a fact caused by the constantly growing number of models, regulatory requirements, unreasonable number of validations, etc. The effective management of your model risk should be based on facts and real data which are instantly available or even on automated model risk tiering. Effectively implemented MRM would make decision making more based on reality which could lead at the end to higher profitability and cost-saving.

Optimised resource management

The system can evaluate the necessity for specific model validation in real time and assign your risk experts to particular portfolios accordingly, making sure that your experts are prioritised for high-importance portfolios.

ARE YOU SERIOUS ABOUT MODEL GOVERNANCE?

We are, too. So let's talk about it. During a one-on-one call or personal meeting with our specialist you discover how our offering can help you.

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