Cardinal
Cardinal provides a data-driven operational risk management platform for financial institutions, combining prediction markets, AI simulations, and advanced modeling to quantify risk and control effectiveness. The platform helps reduce capital requirements and regulatory risk by measuring expected loss reduction in monetary terms. It structures risk data into bow-tie forms and harmonizes it along a common taxonomy for precise, ratio-scale measurement.
- Artificial Intelligence
- Data & Analytics
- Financial Technology
- Regulatory & Compliance Technology
- Software Only
Funding
Founders
Product
Problem
Financial institutions struggle to measure and manage operational risk with precision, often relying on subjective, heat-map-driven assessments that fail to quantify the actual monetary impact of risks and controls. This leads to overly conservative capital buffers, inefficient allocation of risk-reduction spend, and increased regulatory exposure.
Solution
Cardinal provides a structured, data-driven platform that quantifies operational risk by combining prediction markets, AI simulations, and advanced modeling. The software serves as a central hub for collecting and analyzing risk and control data, connecting the dots between risks, controls, incidents, processes, and documents. It imposes best-practice structure on risk data, melding risks into bow-tie form and harmonizing data along a common taxonomy. Each risk is measured on a ratio scale with probabilities and loss-severity curves, enabling quantified expected losses and risk-appetite decisions on a line-by-line basis. The platform reports how much expected loss and tail risk each control reduces in monetary terms, enabling simple "risk reduction per EUR" comparisons that maximize impact within a given budget.
Target Audience
Primary customers are financial institutions, including banks, insurers, and other regulated entities, that need to measure and manage operational risk with greater precision to reduce capital requirements and regulatory risk.
Features
- Centralized data ingestion and analysis hub that connects risks, controls, incidents, processes, and documents
- Automated risk data preparation with bow-tie structuring, blindspot analysis, and taxonomy harmonization
- Quantitative measurement of each risk using ratio-scale probabilities and loss-severity curves
- AI-driven simulations and prediction markets for advanced modeling of expected losses and tail risk
- Dashboards and reports that express control effectiveness in monetary terms, supporting risk-reduction-per-EUR comparisons