Industry and AI Lifecycle Solutions
Truera’s Model Intelligence Platform helps companies maximize their machine learning investment and build trust across a broad set of use cases and industries. Examples include regulated, high-stakes, and low-automation use cases and industries like banking, financial services, insurance, pharmaceuticals, government, and healthcare. Truera can also improve the development of any AI application by improving how models are built and operationalized.
Higher Stakes, Lower Automation, More Regulated Use Cases Require More Trust
Machine learning in banking, insurance, healthcare, and regulated industries
Explainable machine learning is a must-have in regulated industries such as:
Credit Risk and Decisioning
Existing regulations on credit risk models and fairness-related laws require lenders to validate models and prevent bias but doing this adequately for machine learning models is not possible with existing technology. Truera helps lenders upgrade their risk management procedures to comply with regulations and ensure responsible use of AI.
Existing solutions cannot adequately support the use of machine learning in insurance decisioning and pricing, which are subject to consumer protections, risk management, and regulatory oversight. Truera enables data scientists to build high-quality models that gain the trust of business and actuarial teams, regulators, and customers.
High-stakes machine learning models
The use of machine learning to inform high-stakes decisions, such as asset management, algorithmic trading, energy investment, and drug development, is ineffective without providing users with explanations, confidence, and assurances behind a model’s prediction. Truera helps data scientists explain their model predictions, provide confidence bounds, and assure customers of model quality to support greater trust and adoption.
Human decision support and business workflow models
Machine learning applications that are intended to inform human decisions or that are part of business workflows, such as predictive maintenance, fraud detection, demand forecasting, advertising, merchandising, marketing, sales lead scoring, and more, will struggle to perform if humans can’t understand or collaborate with the machine learning app. Truera helps data scientists solve this black-box problem improving adoption, user happiness, and business results.
AI Lifecycle Solutions
Best practice and analytically driven model development
Machine learning model development is often ad-hoc, inconsistent, and fragmented across multiple platforms. Truera allows data science teams to consistently apply a best-practice set of model quality and comparison analytics to each model version across all development platforms, improving results and making the process more structured, analytical, and consistent.
Model review and validation
Data science partner teams, such model risk management teams, find it hard to review and validate black-box machine learning models. Truera provides these partners with the documentation, visibility, and understanding they need to efficiently complete their review and validation processes.
Machine learning monitoring
Black-box AI applications need new monitoring and management oversight. Truera’s AI.Q explanation-driven monitoring capabilities enable data scientists to analyze consequential data drift and model quality over time — even for applications with long feedback cycles — to efficiently measure, understand, and take action to maintain AI application business performance.