A few centuries ago, people thought electricity was a miracle, and several decades ago, artificial intelligence was only a part of science fiction books. But look at us now. Today we cannot imagine our life without smartphones, and virtual assistants like Siri are no longer as awe-inspiring as they were. Humanity is constantly evolving and tech professionals are finding new ways to make our lives easier and more comfortable. And machine learning plays an important role in this, as more industries implement it in their work.
According to IBM, 1 in 4 companies adopts AI due to labor shortages. Programs developed with ML can predict equipment failures and customer behavior, and make logical and analytical decisions almost like humans. With modern technologies and resources, machine learning systems have become the driving force behind the development of different industries. Besides, the global machine-learning market is flourishing. According to Fortune Business Insights, the value of the adoption of technological advancements is going to grow from $21.17 billion in 2022 to $209.91 billion by 2029. With this in mind, let’s have a look at the biggest ML use cases in various domains.
Real-life use cases of machine learning across industries
We can adapt machine learning to improve multiple processes, especially those that have to do with data. Below are the most interesting examples of how industries use ML technology for their benefit.
Retail and consumer goods
Nowadays, the use of AI technology and machine learning in the retail world is pretty common. Store owners use ML technologies to analyze a large amount of customer data (like online behavior, shopping preferences, purchase history, etc) to provide a better customer experience. Machine learning also helps to understand the dynamics of the industry deeper than traditional analytics.
Mainly, machine learning helps to:
- analyze a significant amount of customer data;
- accurately predict the upcoming trends and needs;
- improve inventory control;
- decide on the pricing;
- increase sales;
Machine learning technologies and algorithms have become indispensable tools for companies that rely on online sales. Big companies, such as eBay and Amazon, have successfully integrated these technologies into their retail processes. However, it is also beneficial for small firms and physical stores.
Every user journey is unique and it can be challenging for shop assistants to provide the required consultation on time. Customers often complain about long waiting times on phone calls or unqualified advice from the support team. This frustrating experience can easily drive your clients away. And customer loyalty is important for a business to be successful. According to statistics, regular clients spend 67% more money than new ones. So while all businesses strive to attract new clients, keeping old ones is essential as well.
Thus, more retailers use ML-driven virtual customer support and chatbots to make the shopping experience more satisfying. Smart assistants analyze personal data, process voice requests, and automate daily tasks, thus solving clients’ queries promptly and with personalized recommendations. Therefore, the process of customer support is significantly faster and more efficient and leaves customers more satisfied.
Machine learning algorithms can help predict upcoming retail trends. And not only the products that clients would like to buy, but also the price that they are willing to pay. Thus, it is beneficial to implement machine learning to help adjust the price of products based on changing circumstances. To predict the potential impact of such price elasticity and suggest changes or promotions, machine learning systems need to analyze a wide range of parameters which would be difficult to perform manually. For instance, consider the items that customers look at and purchase, the time of the year, the prices of similar products offered by competitors, and the time they spend on each web page.
Previously, there were a few tools for analyzing large amounts of data, but they weren’t too advanced. People also relied on their business sense a lot. But nowadays, machine learning helps to advance data analytics so companies can gain deeper insights into each individual consumer. This gives businesses a better understanding of customer needs and preferences, which allows them to offer highly personalized recommendations. People favor companies that can meet their demands. According to a study by Accenture, 75% of customers are more likely to buy products from retailers that use their name, provide personal recommendations, and know their preferences.
With the use of machine learning technology, the banking and finance industry has evolved over the last couple of years. Machine learning enables companies to replace manual work, automate repetitive tasks, improve customer experiences, and scale up services. It also helps to improve security and reduce risks of fraud. Besides, predictive analytics play an important role in assisting financial companies. Companies encounter large volumes of data, such as payment transactions, bills, etc. The application of machine learning also helps banks offer personalized services to customers at a lower cost.
Fraud has always been a big problem. Security threats in finance are increasing along with the growing number of transactions, users, and information stored online. As it enables the identification of unusual or suspicious transactions that might call for further investigation, transaction classification is also becoming more and more crucial in preventing and detecting fraud. The loss from fraud accounts for billions of dollars each year. Therefore, many organizations use machine learning technologies to improve their security. For instance, algorithms such as KYC (Know Your Customer) and AML (Anti Money Laundering) focus on banning suspicious operations and preventing criminal activity. They can detect unique activities or anomalies and mark them for further investigation by security teams. Banks use this technology to monitor thousands of transactions for every account in real-time. If the system finds certain actions suspicious, it can request additional identification from the user to validate the transaction. Moreover, machine learning technologies are also used for biometric customer authentication. This method helps banks to prevent identity theft – for example, MasterCard uses facial recognition for payment procedures.
Loan eligibility prediction
Proper validation is necessary before deciding who should receive a loan and who shouldn’t. In banks, the solvency of a client is usually assessed by managers. Employees spend a lot of time on customer evaluation but still can make a mistake. As a result, they reject loans to those who could pay them and issue loans to those who are insolvent. So the ML algorithm is constantly learning to assess the paying capacity of bank customers. To do so, the algorithm loads uses information about previously issued loans, which helps to forecast whether customers can repay the loan with no difficulties.
Machine learning has opened a lot of possibilities in the transportation industry. With the help of ML technologies, modern cars provide assistance with parking and ensure passengers’ and drivers’ safety by alerting them about the speed of the vehicle. Machine learning also helps identify which routes are more efficient and where possible traffic jams are.
The predictive maintenance approach saves companies a lot of money by detecting problems in vehicles early on. The preventative maintenance process follows a tight schedule and includes small checkups or detailed inspections. This is highly important as defects in vehicle systems can cause significant damage and lead to life-threatening accidents. ML systems typically use pattern recognition and image recognition to discover flaws in maintenance.
Traffic jams are a tiring problem that many people face every day. Machine learning systems can advise drivers on the shortest routes to use. They correlate data obtained from sensors and cameras and use the analysis for traffic predictions to provide a smarter and more efficient way of traveling.
One of the most popular trends in the world of AI and machine learning is the development of self-driving cars. It became especially accurate during the COVID-19 pandemic. Driverless cars present a huge potential in making driving safer by reducing the number of accidents. Some services, like Wayomo One, provide the possibility to hire autonomous cars as taxis. These cars use sensor cameras and radars to collect data about their surroundings and interpret it with the help of ML technologies. They can identify objects, interpret situations and make decisions based on this data and classification algorithms.
Machine learning significantly simplifies a lot of processes in the healthcare industry: for example, it helps to determine one’s diagnosis, plan treatment, or keep track of a patient’s progress. The most actively used ML learning applications in healthcare include:
Machine learning applications can assist doctors in making diagnoses and here is how. By analyzing patients’ data like scans, medical history, or prescribed medications, a machine learning algorithm can predict whether a patient is prone to certain diseases or whether a patient may already be in the risk group. This technology has especially high potential in detecting patients at a risk for cancer. And since machine learning is capable of determining patterns and dependencies in massive data sets (and it’s almost impossible to perform such analysis manually), the chances that ML will detect a risk are very high. If you need an example, think of the PathAI platform. PathAI is a machine learning diagnosis platform that helps healthcare professionals measure the accuracy of diagnosis. It analyzes patients’ histories and scans to make accurate predictions on the disease’s origin and symptoms.
Online and virtual medical assistants
Applications for primary diagnosis or consultation at home are becoming more and more popular. Machine learning chatbots help patients to check their condition, remind them to take medication, and recommend actions or specialist visits. To estimate the patient’s condition, medical chatbots can also use voice recognition technology. For example, the artificial intelligence Corti listens to calls to an ambulance and can recognize a cardiac arrest based on the callers’ responses, their voices, and breathing.
The more personalized a patient’s treatment is, the better. Machine learning-powered apps allow gathering a wide range of personal data to make predictions about how the body would react to a certain treatment. This helps to prevent risks of allergic reactions, which in critical conditions can be lethal for the patients. Therefore, personalized medication based on individual health records paired with analytics provides better disease assessment.
Interesting Related Article: “Using Machine Learning Technologies in Healthcare“