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How Machine Learning is Being Applied to Regulated Industries Today 

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Summary: This article describes the various applications of machine learning in industries such as, healthcare, telecommunications, invoice processing and insurance.

Today, artificial intelligence (AI) is all the rage. The advent of OpenAI’s ChatGPT, Google Bard, Git-Hub Copilot and other generative AI solutions are empowering workers across a variety of industries to produce first drafts of content, write software code, develop strategic plans or conduct extensive research. Individuals with little to know technical expertise can interact with these programs and generate invaluable content and produce work in a fraction of the time it previously would have taken.  

But while generative AI is today’s new shiny object in the world of AI, there’s been a less showy, but as equally powerful tool that has been taking the business world by storm over the past few years, and that is Machine Learning.  

According to IBM, Machine learning is a branch of artificial intelligence (AI) which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving their accuracy over time. In a very general sense, machine learning represents the capability of a machine to imitate human intelligence and behavior. They’re trained on data to perform complex tasks similarly to how humans solve problems. The global machine learning market size was valued at $19.20B in 2022 and expected to grow from $26.03B in 2023 to $225.91B by 2030. 

Machine learning is being used in various ways across industries to improve efficiency, accuracy and decision-making, as well as to reduce costs. Consider some of the following ways it is being applied in key sectors: 


The telecommunication industry today is being confronted with challenges that require new ways to compete. Telecom providers are working to update their infrastructure to  accommodate the advent of 5G, new regulations are constantly being added for which they need to demonstrate compliance and they need to make sure that the many new devices being added to networks don’t create excessive network congestion and reduce the ability to tout always-on connectivity.  

Machine learning is helping to reduce these and other challenges. It can help to optimize network performance by predicting network congestion based on historical data, identifying and resolving faults without human intervention and ensuring efficient resource allocation. 

Machine learning is also enabling predictive maintenance, helping to predict the likelihood of equipment failures and recommending maintenance before they occur. Additionally, it’s playing a role in the front office and the call center, fueling the interactive chatbots that answer subscriber questions or report outages around the clock.  


The insurance industry is becoming highly competitive, with both traditional insurers and insurtech startups vying for market share. At the same time, insurance costs are rising and they are looking for ways to streamline processes and reduce unnecessary costs, while reducing insurance risk as much as possible.  

Machine learning is helping insurers analyze the huge volumes of data they are gathering on customers and their environments to assess insurance risk more accurately, in order to establish premium costs and determine policy terms, by evaluating an applicant’s risk profile based on various factors, such as demographics and medical history. It’s also helping them reduce fraud by identifying patterns and anomalies in claims data. 

Likewise, as with the telecommunications industry, it is supporting customer service centers, by fueling the chatbots that are answering routine customer questions and handling basic processing activities.  

Invoice Processing

Regardless of the industry, invoice processing can be a time-consuming and long-term process, but critical to the bottom line.  Many of the invoice processing steps can easily be handled with machine learning, supported by humans when exceptions occur in the data.  Machine learning models can eliminate manual data entry, extracting relevant information from invoices automatically. 

Today, invoices are still being sent in multiple formats, such as XML documents, PDFs, images and sometimes still in hard copy. Yet, putting these files into a single system is time consuming and error prone. Machine learning can automated invoice data extraction quickly and with less errors. 

Additionally, machine learning can be put to work matching purchase orders (POs), receipts, and invoices to ensure accuracy and prevent overbilling or duplicate payments. It also can identify irregularities or discrepancies in invoices, such as pricing errors or duplicate invoices. 


Healthcare markets have typically been on the forefront of technology innovation and thanks to precision medicine, telehealth and personalized care they’re collecting vast amounts of data to improve patient outcomes, boost the patient experience and reduce mounting healthcare costs.   

Healthcare markets have been on the cutting edge of machine learning, using it to help with  disease diagnosis, by analyzing medical images, such as X-rays, MRIs, and CT scans and sharing with medical professionals, with high accuracy. 

Machine learning is also enabling more accurate drug discovery, analyzing molecular data, identifying potential drug candidates and accelerating drug development thanks to its ability to crunch massive data-sets and make sense of the data. 

On the predictive side, machine learning models can help health systems and clinics predict the likelihood of patient readmissions or likely health outcomes, which can help them be more proactive with treatment plans.  

A role machine learning is playing in a variety of markets, it is helping to thwart fraud within healthcare market, by recognizing patterns in fraudulent claims and billing. 

Machine learning is hitting major strides in a variety of regulated markets, as they work to meet compliance standards, compete for market share, reduce costs and boost the customer experience. New use cases are being discovered regularly, yet one thing remains the same – the continued need for humans to remain leaders of the decision-making process, using machine learning as a very competent assistant. 

Wovenware has the machine learning expertise and innovation that is helping leading companies in regulated markets reach new levels of success. Learn how we can apply our best practices and experience to your business needs today. Please reach out at


Machine Learning in Regulated Industries: Today's Applications

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