In nearly every industry throughout the last few years, the massive impacts of AI and machine learning are undeniable. Organizations are gaining competency in AI/ML and deploying it to achieve goals such as cost reduction, bias mitigation, and so much more.
They are also recognizing that mastering AI is a make-or-break for companies that want to be around in the next decade—in fact, a whopping 94% of business leaders in a recent survey said that AI is critical to success. As our CEO Adam Wenchel spoke about at last year’s AI Summit, the enterprises that become AI-native first will be industry leaders for decades.
In this blog, we’ll share four industries where machine learning has had a particularly large influence, as well as how the leading organizations within those industries are using it—and more specifically, model monitoring—to stay ahead of the curve.
Even in traditionally conservative spaces like financial services, AI is rapidly changing the business landscape. While there is no enforceable federal AI legislation in the U.S. currently, there is a growing momentum to regulate the financial services industry around biased algorithms and govern black box underwriting by various agencies.
Financial services organizations are using AI and machine learning for activities like credit approvals, fraud detection, and customer support. But what happens when bias creeps into an algorithmic model which results in the wrong lending decision being made, impacting millions of customers? Companies must consider the potential business and compliance risks of these technologies.
Leading financial institutions are using Arthur to monitor, measure, and improve machine learning models for better results across top industry use cases: fraud/KYC, forecasting models, fair lending, robo-advisory programming, credit worthiness, customer service, and more.
Learn more by downloading our financial services whitepaper or watching our on-demand webinar.
No matter how the economy evolves in the coming years, individuals and companies will continue to need insurance. The growing reality of company layoffs and downsized industries may shrink average coverage amounts, but consumers and businesses trust carriers to bring them peace of mind when faced with economic market uncertainty and climate change volatility.
While the insurance industry has typically been a late adopter of technology, that isn’t true with AI—insurance companies are applying CV and NLP technologies across the value chain to improve their own pain points while simultaneously benefiting the customer. According to Deloitte’s 2022 Insurance Industry Outlook report, almost 74% of global respondents said they planned to increase spending on AI-related technologies.
AI helps insurers assess risk, detect fraud, and reduce human error in the application process. It also helps customers, who benefit from the streamlined service and claims processing that AI provides. Specific use cases include underwriting, premium forecasting, pricing strategy, and customer servicing.
With Arthur, companies are proactively mitigating reputational, regulatory, and strategic/financial risk while saving money and driving business goals.
Explore industry use cases by downloading our insurance whitepaper.
From medical imaging analysis to disease prediction to drug discovery and development, AI has already revolutionized the healthcare industry from a technology perspective. Another piece of this puzzle, however, is ensuring health equity—particularly as it pertains to levels of care across underrepresented and minority groups.
Additional healthcare use cases include hospital management, predictive insights for patient outcomes, capacity planning, staff training, medical diagnosis bias detection & mitigation, and medical document NLP classification. Arthur helps healthcare organizations avert harmful patient outcomes and reduce operational risk through proactive MLOps monitoring, resulting in early detection of data anomalies and model errors.
One of our first customers and the leading AI-enabled healthcare enterprise, Humana, is deploying Arthur to manage mission-critical AI across both clinical and membership use cases. Arthur seamlessly integrates into Humana’s AI tech stack and is a core component of Humana’s approach to responsible and high-performing AI, providing a continuous view into model performance and bias, governance support, and alerting capabilities.
Discover how AI drives business impact by downloading our Humana case study.
The global transition from centralized office workplaces to regular work-from-home arrangements accelerated the adoption of automated AI tools to make HR departments run more efficiently. These tools are being used in areas like talent acquisition, hiring, performance management, and employee experience. In fact, 99% of Fortune 500 companies rely on the aid of talent-sifting software and 55% of human resources leaders in the U.S. use predictive algorithms to support hiring.1
While AI technology yields significant operational benefits, it also introduces risk—and the challenge is balancing the two. Any company using AI systems that analyze protected, special categories or sensitive personal datasets (age, race, gender, ethnicity, etc.) needs to exercise caution and ensure the data and/or algorithms being used are not causing systemic issues of bias and inequity resulting in disparate impact or discrimination.
And this is no longer just an issue of morality: Starting in January 2023, companies in New York City will actually be legally restricted from using employment decision tools unless they have been the subject of an independent bias audit, in what is likely just the first of many similar laws that will be passed throughout the country and beyond.
Deepen your knowledge by downloading our human resources whitepaper.