A few centuries ago, people thought electricity was a miracle, and a few decades ago, artificial intelligence was just a part of science fiction books. But look at us now. Today, smartphones have become an integral part of our lives and virtual assistants like Siri aren’t as impressive as they used to be. Humanity is constantly evolving and tech professionals are finding new ways to make our lives easier and more convenient. And machine learning is playing an important role in this as more industries integrate it into their work.
According to IBM, 1 in 4 companies are using AI due to labor shortages. Programs developed with ML can predict device 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 various industries. Also, the global machine learning market is thriving. According to Fortune Business Insights, the value of adopting technological advances will grow from $21.17 billion in 2022 to $209.91 billion in 2029. With that in mind, let’s take a look at the biggest ML use cases in different domains.
We can adapt machine learning to improve multiple processes, particularly those dealing with data. Below are the most interesting examples of how industries are using ML technology to their advantage.
retail and consumer goods
Today, the use of AI technology and machine learning in retail is widespread. Store owners use ML technologies to analyze a large amount of customer data (such as online behavior, shopping preferences, purchase history, etc.) to provide a better customer experience. Machine learning also helps to understand industry dynamics more deeply than traditional analytics.
Machine learning mainly helps with:
analyze a significant amount of customer data; accurately predict upcoming trends and needs; improvement of inventory control; decide on pricing; increase sales;
Machine learning technologies and algorithms have become indispensable tools for businesses that rely on online sales. Large companies such as eBay and Amazon have successfully integrated these technologies into their retail processes. However, it is also beneficial for small businesses and physical stores.
Each user journey is unique and it can be challenging for sellers to provide the required advice in a timely manner. Customers often complain about long waiting times for phone calls or unqualified advice from the support team. This frustrating experience can easily drive your customers away. And customer loyalty is important to a company’s success. According to statistics, regular customers spend 67% more money than new customers. So while all businesses strive to attract new customers, it is also important to retain old customers.
Therefore, more and more retailers are using ML-driven virtual customer support and chatbots to make the shopping experience more satisfying. Intelligent assistants analyze personal data, process voice requests and automate daily tasks to solve customer queries in a timely manner and with personalized recommendations. Therefore, the customer care process is significantly faster and more efficient, resulting in happier customers.
Machine learning algorithms can help predict upcoming retail trends. And not just the products that customers want to buy, but also the price they are willing to pay. Therefore, it is beneficial to implement machine learning to adjust the price of products to changing circumstances. To predict the potential impact of such price elasticity and suggest changes or promotions, machine learning systems need to analyze a variety of parameters that would be difficult to perform manually. For example, consider the items customers view and buy, the time of year, the prices of similar products offered by competitors, and the time they spend on each website.
There used to be a few tools for analyzing large amounts of data, but they weren’t very advanced. People also relied heavily on their business acumen. Today, however, machine learning is helping to drive data analytics so businesses can gain deeper insights into each individual consumer. This gives businesses a better understanding of customer needs and preferences, enabling them to offer highly personalized recommendations. People prefer companies that can meet their needs. According to a study by Accenture, 75% of customers are more likely to buy products from retailers who use their name, make personal recommendations, and know their preferences.
Through the use of machine learning technology, the banking and finance industry has evolved in recent years. Machine learning enables companies to replace manual labor, automate repetitive tasks, improve customer experiences, and expand services. It also helps improve security and reduce the risk of fraud. Also, predictive analytics play an important role in supporting financial companies. Companies encounter large amounts of data such as payment transactions, invoices, etc. The use of machine learning also helps banks to offer customers personalized services at lower costs.
detection of a scam
Fraud has always been a big problem. Financial security threats are increasing along with the growing number of transactions, users and information stored online. Loss from fraud accounts for billions of dollars each year. Therefore, many companies use machine learning technologies to improve their security. For example, algorithms like KYC (Know Your Customer) and AML (Anti Money Laundering) focus on banning suspicious operations and preventing criminal activity. You can identify unique activity or anomalies and flag them for further investigation by security teams. Banks use this technology to monitor thousands of transactions for each account in real time. If the system finds certain actions suspicious, it can request additional identification from the user in order to validate the transaction. In addition, machine learning technologies are also used for biometric customer authentication. This method helps banks prevent identity theft – MasterCard, for example, uses facial recognition for payment transactions.
Credit Score Prediction
Proper validation is required before deciding who should and should not receive credit. In banks, the solvency of a customer is usually assessed by managers. Employees spend a lot of time evaluating customers, but they can still make mistakes. As a result, they refuse to lend to those who could pay and lend to those who are insolvent. The ML algorithm is therefore constantly learning to assess the solvency of bank customers. To do this, the algorithm uses information about previously issued loans, which helps to predict whether customers will be able to repay the loan without any problems.
Machine learning has opened up many opportunities in the transportation industry. With the help of ML technologies, modern cars provide parking assistance and ensure the safety of passengers and drivers by warning them about the speed of the vehicle. Machine learning also helps identify which routes are more efficient and where congestion can occur.
The predictive maintenance approach saves companies a lot of money by detecting problems in vehicles at an early stage. The preventive maintenance process follows a strict schedule and includes small checks or detailed inspections. This is very important because defects in vehicle systems can cause significant damage and lead to life-threatening accidents. ML systems typically use pattern recognition and image recognition to detect errors in maintenance.
Traffic jams are a tedious problem that many people face on a daily basis. Machine learning systems can recommend the shortest routes to drivers. They correlate data from sensors and cameras and use analytics for traffic forecasting to enable a smarter and more efficient way of travelling.
One of the most popular trends in the world of AI and machine learning is the development of self-driving cars. It became particularly accurate during the COVID-19 pandemic. Driverless cars have enormous potential to make driving safer by reducing the number of accidents. Some services, like Wayomo One, offer the option of renting autonomous cars as taxis. These cars use sensor cameras and radar to collect data about their surroundings and interpret them using ML technologies. They can identify objects, interpret situations, and make decisions based on this data and classification algorithms.
Machine learning greatly simplifies many processes in the healthcare industry: It helps, for example, to make your own diagnosis, to plan treatment or to track a patient’s progress. Some of the most commonly used ML learning applications in healthcare include:
Machine learning applications can help doctors make diagnoses, and here’s how. By analyzing patient data such as scans, medical history, or prescribed medications, a machine learning algorithm can predict whether a patient is susceptible to certain diseases or whether a patient may already be at risk. This technology has particularly high potential in the detection of cancer patients. And since machine learning can spot patterns and dependencies in huge data sets (and it’s almost impossible to perform such an analysis manually), the chances that ML will spot a risk are very high. If you need an example, consider the PathAI platform. PathAI is a machine learning diagnostic platform that helps healthcare professionals measure the accuracy of diagnostics. It analyzes patients’ medical histories and scans to make accurate predictions about the origin and symptoms of the disease.
Online and virtual medical assistants
Applications for primary diagnostics or advice at home are becoming increasingly popular. Machine learning chatbots help patients check their condition, remind them to take medication, and recommend actions or specialist visits. Medical chatbots can also use voice recognition technology to assess the patient’s condition. For example, the artificial intelligence Corti listens to the calls of an ambulance and can recognize a cardiac arrest based on the callers’ responses, their voice and their breathing.
The more individual the treatment of a patient, the better. Apps based on machine learning allow a wide range of personal data to be collected in order to make predictions about how the body would react to a given treatment. This helps to avoid the risk of allergic reactions, which in critical conditions can be fatal for the patients. Therefore, personalized medication based on individual health data coupled with analytics offers better disease assessment.
Source: SoftTeco – custom software development company
Interesting article on the topic: “Using Machine Learning Technologies in Healthcare”