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Model Risk Management with Causal AI

Use Causal AI to achieve 10X faster AI model deployment. Identify the risks and deploy more impactful, compliant models without compromising on explainability.

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AI Model Risk Management in 2022:

  • It can take up to two years for the internally developed models to be validated and accepted by regulators.
  • 9 out of 10 models do not pass internal governance checks due to inherent model flaws like bias and lack of explainability.
  • Spurious correlations mislead organizations as to the true value of their models and lead to nonsensical outcomes, undermining trust.
  • Data scientists’ time is wasted on countless iterations of models that are ultimately valueless to the business.

There’s a better way to deploy accurate ML models

Model risk managers, meet Causal MRM

Causal MRM is an easy-to-use tool that unites data scientists, model validators, and business teams in their quest to deploy robust and compliant AI models.

Intrinsic explainability increases trust

The transparent nature of the model’s causal reasoning allows domain experts and regulators to examine the model’s algorithms and probe causal relationship hypotheses.

Guaranteed performance in production

By undergoing rigorous robustness testing, models achieve consistently accurate output and quickly adapt to regime shifts.

10X faster deployment

Reduce iteration cycles, easily validate models to pass internal audits and move to production ten times faster.

Intrinsic explainability

The transparent nature of the model’s causal reasoning allows domain experts and regulators to examine the model’s algorithms and probe causal relationship hypotheses.

Regulatory preparedness

Meet the most demanding AI governance requirements while scaling AI adoption. 

90% lower cost

Drastically reduce costs across the model design and deployment cycle.

Robust and scalable

Achieve consistently accurate output and quickly adapt to regime shifts.

Automated reports

Boost productivity and enhance model governance by quickly creating detailed compliance reports.

Prepare for AI regulations

Causal AI enables enterprises to meet the most demanding AI governance requirements while scaling AI adoption. By deploying Causal AI organizations can satisfy explainability, fairness and bias, robustness, and human oversight requirements imposed by:

  • recent EU’s AI proposal
  • MAS’s FEAT framework in Singapore
  • SR11-7 guidance and CFPB Circular in the US
5 steps to prepare for AI regulations

#1 Solution for AI Model Risk Teams

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Stress-testing and regime changes

Uniquely, Causal AI enables enterprises to accurately predict how the model is likely to behave in all circumstances—even completely novel conditions like Covid-19 pandemic.

This sensitivity to change allows early detection and explanation of regime shifts and their consequences—a crucial advantage in addressing growing market volatility.

Achieve true explainability

Unlike traditional ML models,  Causal AI models are open to examination and interrogation. They reveal the systematic, causal relationships between both the input features and the target variables.

As causal thinking comes naturally to humans, non-technical domain experts can interact with and understand causal models easily.  This brings true transparency and inspires collaboration so technical, business and compliance teams work together more effectively.

Causal AI can explain why models fail, how to predict model failure, and what to do to fix it.

Eliminate biases in input data and correct for biases

Fairness and bias analysis

Existing AI algorithms assume that the future will be just like the past—unfortunately, this leads to perpetuating historical injustices.

Causal AI allows users to correct for fairness and bias factors based on sensitive characteristics like race and gender. Users can analyze historical biases and eliminate them by imposing fairness constraints on algorithms.

Accelerating the model review process with everyone in the same room is a game changer”

UK Tier 1 Bank – Head of Model Risk

Key business functions - united

Data scientists, model validators, and business users can work as one team on deploying robust and compliant AI models.

Business Teams

Domain experts can inspect and scrutinize models to ensure they are aligned with business logic and processes.

Model Developers

Extensively test models and produce model documentation before passing models to the governance team.

Model Validators

Easily test for robustness, sensitivity, regime shift, feature extraction, and anomaly detection. 

What to do if your model fails?

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Explainable

Causal AI is better

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