Artificial intelligence - quality and performance
During our recent webinar with PwC Germany, the most prevalent questions were based on two major concerns about AI: regulatory transparency and model system quality.
Regulatory transparency
The simple realization is that many companies are not prepared to adhere to the changing regulatory requirement landscape at global or industry levels. In the US, the recent bulletin from NAIC and the technical requirements from NIST highlight the complexities of compliance. Model filings, market conduct, NAIC, ORSA, ASOP, 56 DOI’s, and insurance jurisdictions - We get you!
AI model quality and business outcomes
Also in the webinar, PwC cites that 80% of AI projects do not achieve business objectives (McKinsey, 2023). This shatters the myth that models must be fully developed before employing governance strategies.
Further, model governance is not isolated to regulation. When done correctly, robust governance enables higher achievement of business objectives, organizational alignment, and acceleration of modeling projects.
We hope our webinar helps you navigate the possibilities for future innovation with AI while navigating regulations and responsible development for high-impact business outcomes.
Good AI needs great governance. Great governance starts with Monitaur.
Revolutionizing insurance with simplified model governance
- Policy setting -> Proof in less than 100 days
- Faster sales & regulatory cycles
- Improved data science alignment & workflows through automation
Model systems in insurance are prevalent, but with increasing and competing demand among data products, industry regulations, and compliances, you need automated help to provide evidence of governance for your AI models.
Speed and efficiency matter when your product and customers are at stake. See how CAPE Analytics goes beyond good intentions for governance with Monitaur.
Whitepapers: Artificial intelligence risks
AI Risk Management for Insurance
With all the promise of AI, comes risk. Take a deeper dive into machine learning and AI for insurance, as well as risk management considerations when leveraging this technology.
Compliance, trust and transparency
Achieving machine learning compliance, and finding trust and transparency with machine learning
What's driving the need for machine learning assurance as a framework for building trust and confidence in AI decisions.
Software that operationalizes AI governance
Monitaur software establishes your risk framework and control library according to best practices in model risk management. Model development teams achieve greater success while also aligning with standards and regulations.
The policies and integrations embed in your developer lifecycle and give all stakeholders direct visibility and oversight across AI projects. Our risk and controls policies already deliver coverage and mapping to NIST, SB-169, and the most recent NAIC model bulletin.
Good AI Needs Great Governance
To succeed with AI, model governance and model risk management take on a new urgency. Monitaur was created to solve this problem. And we have the most comprehensive and effective AI and model governance solution available.
Scalable human oversight across high-risk models
Take the steps necessary to govern the fair, safe, and trusted models your business needs.
Continuously validate model performance
Leverage Monitaur's libraries and API's to connect to your models and model infrastructure and gain automation of controls and continuous objective validations.
Watch the Demo Video
We made the list!
The InsurTech100 is an annual list of 100 of the world’s most innovative InsurTech companies selected by a panel of industry experts and analysts. These are the companies every leader in the insurance industry needs to know about as they consider and develop their digital transformation strategies.
Monitaur is uniquely positioned to help its insurance customers excel with great governance
Are you using models to make critical business decisions?
It is crucial to have effective AI governance in place to ensure the compliant and responsible use of AI technologies within your organization.
- Increase transparency, accountability, and trust in AI model systems
- Mitigate the risks and unintended consequences of AI models on people and outcomes
Model governance for AI is a critical component of responsible AI strategy - and Monitaur is uniquely qualified to help.