Business

5 Applications of Machine Learning in Business

Published

5 years ago

December 19, 2020

Machine learning is a form of artificial intelligence. It allows systems to learn and improve from experience without the need for explicit programming. This process also automates analytical model building. From financial services to healthcare, it can deliver a plethora of benefits, such as reduced cost and improved efficiency. Across several industries, it has a wide array of applications, including those we’ll be talking about below.

Before we start, if you want to learn more about how to successfully integrate machine learning in business, consider studying online! With a machine learning course, you will know how to make the most out of such technology to improve business processes.

1. Dynamic Pricing

Also known as demand pricing, dynamic pricing uses real-time supply and demand to dictate price. The actions of a customer, such as engaging with a marketing campaign, will also provide the basis for pricing. It requires processing massive amounts of information, and this is one area where machine learning can be helpful. It mines data without programming. This will use advanced software that learns more as it is fed with more information.

2. Spam Detection

In the past, emails were filtered using a rule-based system. It relies on built-in knowledge. Machine learning offers a more sophisticated alternative. It does not need direct programming to mine data and makes sense of available information. It uses brain-like neural networks, which will be more intelligent in filtering spams. It recognizes junk mail and phishing messages to make a business less vulnerable to data breaches.

3. Fraud Detection

Beyond spams, machine learning also has a significant role in improving cybersecurity by detecting fraud. It can understand patterns in an instant, making it quick to spot potential anomalies. This explains why the finance sector is one of the biggest users of machine learning today. An example of its application would be in credit card usage. Machine learning stores data about usage, such as location. So, when it detects that a card is used in another country, it can automatically flag a transaction to prevent fraudulent activity.

4. Churn Modeling

From credit card companies to cable service providers, customer churn is one of the most important concepts to understand. It is the percentage of customers that stopped using a product or service within a period. Churn modeling aims to understand customer behaviors and will motivate businesses to elevate their strategies to improve customer retention. Machine learning uses data like demographics and sales for churn modeling.

5. Customer Segmentation

Separating customers into distinct groups requires a data-intensive approach and not just relying on intuition. With the help of machine learning, it is easier to cluster and classify your customers depending on factors like demographics or buyer personas. This will make it easier to understand the feelings, needs, motivations, and characteristics of customers, which will be crucial in creating more effective marketing campaigns, as well as products and services.

From dynamic pricing to customer segmentation, machine learning has a wide array of business applications. Regardless of the size and nature of your business, it can make processes more intelligent and efficient!

Business

Click for Counsel: YesLawyer Wants to Make Lawyers as Accessible as Wi-Fi

Published

1 day ago

April 1, 2026

Matthew Wiles

Photo Courtesy of: YesLawyer

Byline: Andi Stark

For many people facing a legal problem, the most difficult part is not understanding their rights but finding a lawyer willing to speak with them in the first place. Long wait times, unclear pricing, and administrative hurdles often delay even the most basic consultations. YesLawyer, an AI-enabled plaintiff firm operating across all 50 states, is testing whether technology can shorten that gap.

Founded in 2024 by 25-year-old entrepreneur Rob Epstein, the platform offers free intake, automated screening, and, in many cases, same-day conversations with licensed attorneys. The idea is simple: reduce the friction between a client’s first request for help and an actual legal discussion. In this interview, Epstein explains how the system works, where artificial intelligence fits into the process, and what problems the company is trying to address in the broader legal system

Q: When you say you want lawyers to be “as accessible as Wi-Fi,” what does that mean in practical terms?

A: It’s a way of describing speed and availability. Someone dealing with a workplace dispute, a serious injury, or an immigration issue should be able to move from an online form or phone call to a real conversation with counsel in hours, not weeks. YesLawyer is structured so that a client begins with a free case evaluation, goes through automated conflict checks and basic screening, and, in many instances, speaks with a lawyer the same day.

Q: How does the process work once someone contacts the platform?

A: We use a structured workflow. It starts with a short questionnaire and an initial conversation to capture basic facts. That information feeds into conflict checks and internal review. The system then proposes a match with a licensed attorney and provides a calendar link for a virtual consultation, often within 24 hours. After the meeting, the client receives a written legal plan outlining next steps, deadlines, and estimated fees.

Q: Where does artificial intelligence fit into that process, and where does it stop?

A: AI is used for organizing and routing information, not for giving legal advice. It helps with conflict checks at scale, case categorization, and structured summaries so attorneys can focus on the substance of the matter. Every consultation is conducted by a licensed lawyer, and all decisions about strategy or next steps are made by humans.

Q: What problem is this model trying to solve in the current legal system?

A: Delay and cost are still major barriers. Many civil plaintiffs face long waits just to get a first appointment, along with high retainers and hourly billing that make early legal advice risky. We try to respond with faster consultations, flat-fee options, and financing. The idea is to remove administrative friction so lawyers spend less time on logistics and more time speaking with clients.

Q: Some critics say platforms like this blur the line between a technology company and a law firm. How do you describe YesLawyer?

A: We describe ourselves as a national, AI-enabled plaintiff firm that connects clients with independent attorneys. That structure does raise regulatory questions, especially around responsibility and oversight. We focus on licensing verification, attorney-written case plans, and clear communication about fees and services.

Q: You’ve said the main bottleneck is “systems” rather than people. What do you mean by that?

A: The issue isn’t that lawyers don’t want to help more people. It’s that the systems around them make it hard to scale their time. Intake, scheduling, and document handling take hours. Automating those parts means attorneys can handle more matters without being overwhelmed by repetitive tasks.

Q: Does this model risk favoring only the most profitable cases?

A: That’s a real concern in legal technology. Automation often works best for repeatable, high-volume disputes. Our view is that lowering administrative cost can actually make it easier to take on smaller or more complex cases that might otherwise be turned away. Whether that holds over time depends on the data.

Measuring Impact Over Time

YesLawyer’s attempt to compress the timeline between inquiry and consultation reflects broader changes in how legal services are being delivered. As artificial intelligence becomes more common in administrative work, firms are experimenting with new ways to reduce wait times and clarify costs.

The company’s early growth suggests that many clients value faster access to an initial conversation, even before considering long-term representation. Whether this platform-based model becomes widely adopted or remains one of several emerging approaches will depend on regulatory developments, lawyer participation, and measurable outcomes for clients. For now, YesLawyer’s experiment highlights a central question in modern legal practice: how quickly can help realistically be made available to the people who need it.