Product       NLP      Computer Vision        Explainable AI

Model Risk Management, for a New Era

Maximize revenue.

Assure compliance.

Create trust & loyalty.

Centralized observability and auditability so you maintain control over your models.

Arthur for Financial Services

Arthur is the only centralized platform for comprehensive model monitoring across any stack for your entire organization, providing views into model performance for data scientists and business stakeholders with a wide range of interests, including data science operations, business P&L, compliance, and risk management. Arthur clients successfully mitigate legal, regulatory, and reputational risk, while maximizing revenue through more performant model behavior, ongoing throughout the entire model lifecycle.

Use Cases

Credit Decisioning & Risk Monitoring

Insider Trading

Fraud

KYC/AML

Algorithmic Trading

Customer Engagement

Performance

Automated Real-Time Monitoring & Hotspot Detection

Arthur gives you real-time visibility into model performance and outcomes and alerts you when things start to go off the rails.


Know at a glance which models need immediate attention due to dipping performance. Or identify opportunities to increase the performance of your models and detect data issues before they turn into costly problems.

Credit

Fraud

Customer Experience

  • Increase revenues by monitoring credit/loan models for unacceptable loss of accuracy
  • Gain immediate insight during Black Swan events like COVID-19 or sudden changes to upstream data sources that may affect accurate decisioning
  • Trigger Alerts across your existing systems to create tickets or automated refit/redeploy systems
  • Retrain models when needed instead of a time-based interval that may be too frequent or not frequent enough
  • Decrease fraudulent activity by understanding immediately when model accuracy suffers
  • Quickly understand when fraudsters have adopted new techniques to defeat your protections
  • Monitor the many data streams working within complex KYC or identity verification models for drift or sudden shifts
  • Catch anomalous inferences via alerts when individual behaviors significantly change
  • Understand when customers are engaging with recommender engines or when these predictive models are no longer as effective
  • Use insights to know when a customer may need special offers, support, or other intervention to avoid churn
  • Increase conversion rates by understanding when collections strategies are working well or when models need to be tweaked to better suit changing real-world conditions
  • Decrease support calls with better-automated customer service
Explainability

Make the BlackBox Transparent & Simulate What-Ifs

Arthur enables you to understand which features are driving model outcomes, prioritize drift alerts, and compare What-If scenarios.


Decisions made by BlackBox models, regardless of the toolkit, can be made transparent. Explanations are presented clearly to all stakeholders, consistently for all models, at enterprise scale.

Credit

Fraud

Customer Experience

  • Inform clients on the rationale behind approval/denial of credit, even when using complex predictive technologies or alternative data sources
  • Share searchable, rich audit trail with nontechnical stakeholders to investigate and validate ongoing compliance
  • Understand when credit models are over-reliant on particular features, some of which may be hidden proxies for protected classes
  • Gain transparency into the rationale behind new account creation rejections to see where your model’s strengths lie in real-world applications
  • Filter decisions by cohort, time, or type to recognize actionable insights in your model’s production behavior
  • Explain counterfactually what customers can do to prevent account denials or fraudulent flags
  • Provide customers with explanations on why they’re getting certain recommenda- tions or offers
  • Demonstrate to support users how their interactions with your product influence their customer support path
  • Recommend financial health actions programmatically, but explain why these actions are correct in a personal, tailored way based on users’ behavior
  • Explain why NLP models detect changes in sentiment regarding public comments or stock/company ratings
Bias

Operationalize Fairness & Eliminate Bias

Arthur equips you to easily compare model outcomes for sensitive groups against various fairness metrics to avoid unintended bias. Or understand what subpopulations your model simply doesn't perform great against.


Aggregate inference data and configure thresholds to monitor for unwanted bias in models that make consumer-affecting decisions. Mitigate and benchmark bias in existing models and track improvements as newer versions are deployed.

Credit

Fraud

Customer Experience

  • Avoid undue reputational damage and legal action by understanding instantly when models drift beyond acceptable risk tolerance for disparate impact
  • Explore not just protected classes, but subgroups where bias may not be as immediately evident during testing/training time
  • Monitor for error rates that may be disproportionate across subpopulations to guide your search for underrepresented segments of population data
  • Continually improve your models to make them ever fairer by automatically boosting decision weights upon a predetermined threshold
  • Gain visibility into ongoing model performance disparities under protected classes, being informed when models disproportionately deny customers based on legally protected class data
  • Understand when you need to retrain or add representative data to certain classes to increase performance on under-represented groups
  • Recommend sensitive and legally protected products in a fair, balanced way across age, gender, race, and any other sensitive attributes you may watch for
  • Instantly flag support request disparities in how your automated chatbots may treat user requests differently among groups
  • Ensure marketing content is targeted at a disparate and diverse group

Built with data scientists and developers in mind

Seamlessly integrate models from any development or deployment environment into Arthur’s monitoring platform. With advanced monitoring and alerts across a variety of performance metrics, debug issues faster—so you can spend more time solving interesting problems, not babysitting models.

Any Model, Any Platform, Anywhere

Arthur works seamlessly with all your favorite data science and MLOps tools. Our platform is model-agnostic and platform-agnostic, which means you get one centralized dashboard for all of your models, no matter which tools you used to build them or where they're deployed.

Why Arthur

Quick Setup

Fits Your Workflows

Unified Dashboard

Quick get up and running with only a few lines of code, so you don't skip a beat. Arthur can be deployed on-prem, in a cloud environment, or via SaaS.

Arthur is model- & platform-agnostic, meaning they will easily integrate with the tools you use every day.

Monitor all of your models at a glance by connecting them to the Arthur dashboard.

Enables Collaboration

Intelligent Alerting

Cutting-edge Technology

Involve all relevant stakeholders in the model auditing process using easy-to-digest exportable reports and other multi-persona collaboration features.

Set up alerts to track issues in your models, so that you can react as quickly as your business needs you to.

The only platform that supports both structured and unstructured data (NLP & CV) while utilizing the latest and greatest in AI observability techniques developed by experts.

"We care deeply about equity in healthcare at Humana. Thanks to Arthur, we know that our preventative care models are fair."

– Heather Carroll Cox, Chief Digital Health & Analytics Officer

"Our team has many models in production. With the Arthur platform, checking their output and status at a glance is easy."

– Chris Poirier, VP of Data

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