Halley Systems
Halley Systems provides an AI model analysis platform that systematically uncovers hidden error concentrations and learned relationships in machine learning models used for credit, fraud, pricing, and underwriting decisions. The platform enables financial institutions to identify segments where models consistently misbehave, with every finding independently verifiable in the underlying data. It supports both model performance improvement and stronger model governance for risk and validation teams.
- Artificial Intelligence
- Data & Analytics
- Financial Technology
- Regulatory & Compliance Technology
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Machine learning models can achieve high overall accuracy scores while systematically making incorrect decisions in specific, hard-to-find segments of a portfolio. These issues—such as a credit model declining good applicants or a pricing model mispricing a customer segment—are nearly impossible to detect with current techniques unless a team already has the exact right hypothesis among billions of possibilities. At institutional scale, undetectable model errors can lead to substantial losses, missed business, customer friction, or unmanaged risk.
Solution
Halley analyzes a trained model together with the data needed to evaluate it, systematically searching for important learned relationships and concentrations of error. The platform reveals the interactions, thresholds, and relationships that materially affect model decisions, then identifies specific segments where the model is consistently overestimating, underestimating, approving, declining, flagging, or missing. Every finding can be reproduced independently in the underlying data, ensuring that business leaders, model owners, and validators can work from the same evidence. The same analysis supports both model performance improvement and stronger model governance, complementing existing development, validation, and monitoring frameworks. Halley works across all types of predictive models trained on structured tabular data, from logistic regression and tree-based models to XGBoost and neural networks. The platform deploys on-premises or in a private cloud, keeping models and data inside the institution's infrastructure with no proprietary model format or vendor lock-in.
Target Audience
Primary customers are financial institutions using machine learning for credit, lending, fraud, pricing, and underwriting decisions, including model risk and validation teams seeking stronger evidence and model owners looking to improve performance.
Features
- Automated discovery of interactions, thresholds, and relationships that materially affect model decisions
- Identification of hidden error concentrations across specific portfolio segments, including overestimation, underestimation, approval, decline, flagging, or miss patterns
- Independent verification of every finding in the underlying data, allowing business leaders, model owners, and validators to work from the same evidence
- Segment-level performance analysis including scale, potential significance, and underlying observations needed for verification
- Works across all predictive model types trained on structured tabular data, including logistic regression, tree-based models, XGBoost, and neural networks
- Deployable on-premises or in private cloud with read-only access, no changes to production systems, and no proprietary model format or vendor lock-in
- Applicable to credit, lending, fraud, payments, pricing, and underwriting decision models