Revolutionising InsurTech with simplified model governance

The InsurTech industry has been booming, but with increasing and competing demand among data products, regulations, and compliance, companies in this space need help to provide evidence of governance for models in their AI. Speed and efficiency matters. Without repeatable methods to produce this evidence quickly, they’re experiencing slowdowns in sales cycles.

  • Policy setting -> Proof in less than 100 days
  • Faster sales & regulatory cycles
  • Improved data science alignment & workflows


CAPE Analytics, a Palo Alto-based InsurTech, is pioneering the creation of new, predictive property insights using big data and AI. They enable clients to better understand, underwrite, insure, and invest in properties. The quality, robustness, and compliance of CAPE's AI are critical to its success, as they have always been committed to intentional model development best practices.


However, as a rapidly growing company, scaling these practices was affecting engineering productivity. They knew they needed to scale their best practices to maintain development velocity and process repeatability. CAPE's clients are regulated financial institutions and, due in large part to a rapidly changing regulatory environment, CAPE’s need to require proof and assurance of its robust model governance was increasing.

"Model governance is not an option—but a requirement—for our engineering organization - we're building high-consequence applications in a regulated environment. By streamlining and standardizing the governance work at CAPE, Monitaur reduces the amount of effort and mental cycles my team has to spend on governance tasks. The result is more time and focus that our data scientists can spend on data analysis, improving model quality, and creating value for our clients."

- Fabian Richter, VP of Engineering, Machine Learning at CAPE Analytics


CAPE approached Monitaur based on its reputation and trust with insurance regulators and customers. Within four months of the initial meeting, CAPE had its highest-risk model on board with an operationalized and sensible approach to model governance. With best practices and policies already included in a managed and flexible controls library, all CAPE teams with a stake in model development now have visibility and timely information from their model governance program.

The solution also aligns CAPE’s risk and technology requirements. The company will have visibility into specific regulatory requirements, and the structure of the solution will allow updating as these requirements change and grow. The data science and engineering teams are satisfied that they are fully enabled with best practices throughout the entire model development lifecycle.

“Monitaur’s control library and understanding of common control frameworks not only saved us months initially but have provided an agility to continually adjust and improve our process over time.”

- Susan Bow, General Counsel at CAPE Analytics

Webinar replay: How an AI company formalized AI governacne


CAPE Analytics was able to establish and operationalize a robust model governance approach that delivers great models and trust with its clients. They were also able to move from frameworks and best practices to repeatable actions in a matter of months, and they are confident that their diverse teams can readily follow the expected process. Moreover, developers execute repeatable model-building practices aligned with standards while ensuring optimal performance.