Top 5 Machine Learning industrial use cases you want to know about
Machine learning is a science and a powerful AI technique of teaching computers to act and learn without explicitly having to be programmed. The basic concept behind machine learning is that it analyses petabytes of data to make a value of it.
To achieve the end goal provided by a developer, machine learning algorithms will fail again and again till they learn from the experience gained and complete the tasks.
Recently machine learning has brought a plenty of technological innovations, from digital assistants to Google self-driving cars. Today, machine learning is so strongly integrated into our lives that you can use it many times a day without even realizing it.
For example, Facebook facial recognition, filtering spam messages, speech recognition of Apple Siri and Amazon Alexa, and the price determination by Uber. And every day there appear new machine learning solutions that facilitate the business environment and improve data analysis. In this post, we’ll cover the most interesting and popular machine learning industrial use cases.
Machine learning use cases across different industries
1. Banking and Finance
Machine learning finds a successful application in banking and financial organizations. Financial crime and fraud detection, credit risk management, financial market analysis, customer segmentation, forecasting, cybersecurity – these all are use cases of machine learning solutions in the banking and finance industry.
Machine learning suits best for combating fraud. Facing multiple security challenges, companies integrate machine learning tools that collect real-time customer data, analyze client histories, trace all activities, immediately identify suspicious and illegitimate ones, and send notifications.
Such solution can work as follows: machine learning algorithms scan large volumes of transactional data and monitor them for any unusual behavior. The system analyses all activities in real-time and gives them a fraud-score. If it is above a certain threshold, a rejection will be instantaneously made and the customer will automatically receive an alert.
For example, PayPal decided to implement machine learning to fight against money laundering. Machine learning tools they use analyze billions of transactions, compare them and define whether a transaction is legitimate or a fraudulent one.
Customer loyalty is something valuable that can’t be bought. According to Harvard Business Review, acquiring new customers is 5-25 times more expensive than retaining the existing ones. That’s why personalized user experience is so important there. Today it is the top-priority thing customers want and look for.
Machine learning solutions can be successfully used in retail to solve this and other goals. While Big Data applications collect customer data, machine learning algorithms process them in real-time, analyze client buying and search history, commenting, and bookmarking, and offer them personalized advice and offers.
The Realities of Online Personalization Report says that now 42% of retailer companies apply machine learning tools to offer customers personalized product recommendations.
Concerning practical examples, Amazon uses machine learning to provide their customers with such valuable lists as “Customers who purchased this product also bought” and “Customers who viewed this product also viewed”. It helps the company either increase sales or engage their customers.
Another case is chatbots that you can build to replace the processes of request processing and answering phone calls by employees. For instance, Macy’s store integrated a chatbot that provides answers to simple questions about products as well as helps clients locate them within the store.
While most healthcare providers have already adopted IoT technology to automate operations and enhance treatment, many of them still don’t use machine learning tools in their work. However, now machine learning is an emerging trend having the potential to innovate and transform the industry.
According to McKinsey Global Institute estimation, the use of machine learning applications for decision making and data analysis improvement could generate up to $100 billion in value thanks to the increased efficiency of clinical trials and the integration of innovative tools and technologies.
With machine learning solutions, you can provide a quality personalized medical treatment. Imagine, for instance, a software system that has all patient data accurately stored and helps doctors identify problems that can be difficult to find or too small for the human eyes to see.
How it works: the doctor inputs the symptoms and other information into the computer and machine learning algorithms process records and health reports, analyze patient history and even his/her family’s medical history. Also, such solution can take into account the latest medical research and offer doctors valuable tips and recommendations.
Thus, by integrating machine learning tools, healthcare providers will be able to ensure personalized patient treatment, automate and improve decision-making process, and increase the efficacy of data.
Telecom companies can successfully apply machine learning for processing large data sets in real-time and implementing predictive modeling. Thanks to the use of machine learning tools they get the ability to predict which customers could stop using services after contract expiration.
For example, you can integrate machine learning solutions analyze customer activity (call usage) histories, their billing patterns and employee records in the client databases (often CRM systems) and make smart predictions based on them.
5. Social Media
Machine learning solutions are widely used in social media, helping companies to engage millions and billions of users. The analysis and processing of large data sets enable them to provide users with personalized news feeds and targeted ads.
Probably you’ve noticed that Linkedin offers you connections you may need and makes a list of jobs you may be interested in. Another famous social network Facebook uses machine learning tools for defining familiar faces from user friend contacts so that users can instantly synchronize photos with only those specific friends.
Hence, by integrating machine learning algorithms into the heart of your social media platform, you can ensure a great user experience, attract new users, increase customer loyalty, and thus get a competitive advantage.
As you see, many industries can benefit from machine learning solutions. There are a lot of advantages and use cases, that include data analysis, decision-making automation, customer segmentation, predictive modeling, risk management, market analysis, and fraud detection.
So, if you have an idea of a machine learning project, you’re welcome to apply to a reliable software development company. Having high expertise in machine learning, we are ready to offer you a project consultation for free!