Machine Learning (ML) is one of the areas of Artificial Intelligence (AI) that has the most future potential and offers the most benefits to the industry. It allows us to create systems that can learn to make decisions with little human interference. These systems, instead of simply following “the orders” of their user, adapt from previous calculations and decisions, returning results that do not require human interaction on an ongoing basis.
The machine learning market will reach 96.7 billion dollars in 2025. Over the next few years, more and more companies will opt for machine learning technology to improve their businesses.
The growing popularity of the use of these technologies is justified by the advantages they bring to organisations, namely in the possibility of revealing patterns of behaviour, revealing market trends or identifying customer preferences. And data is establishing itself as one of the most precious assets for businesses.
There are many business areas that currently use ML to perform advanced calculations, improve the productivity of their teams or simply provide better customer service. Here are some of the advantages of these algorithms by business area.
Finance and the banking sector
The finance and the banking sector has seen several advanced uses of ML, especially when it comes to automating processes and the detection of fraud, as machines can process large amounts of information much faster than humans. Once these systems recognise unusual buying patterns, they can launch alerts for the human hand to verify whether it's a legitimate transaction or a fraud. At a time when there are more and more payment methods available, these algorithms gain special relevance.
In addition to fraud detection, these technologies can be used in predictive analytics (identifying the likelihood of a customer defaulting on a loan, for example), automating processes, investment modelling, trading, prevention of risks and improving customer service.
Banks, as well as financial institutions, use machine learning technology to personalise banking products as well as offerings for keeping pace with the competitive environments.
In a sector that still carries the stigma of bureaucracy and paper, machine learning technologies are used to detect tax fraud or triage medical care cases in hospitals.
ML algorithms are also useful to increase the productivity of institutions, as they are able to process automated tasks more efficiently, leaving employees more available to dedicate themselves to more critical and decision-oriented tasks. In this way, hybrid systems are created, in a union between the power of technology and human validation.
The healthcare and medical industry
The coronavirus Pandemic has highlighted the significance of investing in and optimising the healthcare industry and its systems. Thanks to machine learning which is one of the most promising technologies in the world today, healthcare providers are able to generate extensive volumes of data for making insightful and deep clinical decisions.
The use of these systems in the health area also makes it possible to prioritise the care of emergency situations, faster diagnoses and better medical treatment decisions. ML also helps systems that deal with healthcare delivery to boost their quality under reduced costs. In the future, experts in technology predict ML to be an indispensable component of healthcare clinical trials.
Machine learning also assists a number of processes in the discovery of drugs; it helps in reducing the time for the discovery of the drug and its development. This helps the industry to save a lot of costs.
Artificial intelligence algorithms are important tools for delivery and public transport companies that can have at their disposal indications on the best route for their journey, information on blocked roads or accidents. These systems are capable of recognising patterns and possible trouble spots or delays, suggesting alternative routes and predicting arrival times at the destination according to the chosen route.
These algorithms also made it possible to make autonomous vehicles available, a real revolution that is already available on the market. These vehicles use image processing algorithms, which can determine whether a collision is imminent, taking into account the vehicle's speed and its relationship to other vehicles on the road.
Retail and commerce
The application in retail is very varied and is already being used by many companies. At an operational level, the algorithms make it possible to optimise supply chains and forecast the time needed to sell stocks. But the retail industry witnessed a huge shift due to the Pandemic. It has disrupted several conventional practices that were in the past followed by the industry, and machine learning has become a major enabler of this change. From the perspective of physical stores and eCommerce companies, ML is assisting the retail and commerce industry to reinvent the supply chain, management of inventory, the prediction of user behaviour and analyse major trends. Dynamic pricing also has emerged as a major machine learning application to aid retailers to prosper in the competitive market.
In order to optimise the customer experience, these systems collect data on the most purchased products, which are the preferred times for shopping or which is the preferred means of accessing the store. The analysis of this data makes it possible to enhance the customer experience and increase sales through personalised discounts or organising products in the store according to consumer preferences.
In addition to these optimisation initiatives, there are already self-shopping stores that allow a completely autonomous shopping experience. One of them is Amazon Go, which already has several stores in the United States and allows you to buy products without checking out through a cashier.
Entertainment and the media industry
Netflix and Amazon are media giants that have widely popularised channels for data-based content in recent years. With the world at a standstill due to the global pandemic, the market demand for new consumption models has increased dramatically. This left companies to leverage their machine learning and artificial intelligence capabilities for creating value for their customers.
In the process, ML has become key to the entertainment and media industries. It can provide an improved recommendation engine to provide hyper-targeted services to customers as well as present relevant real-time relevant content. Predictive modelling also plays an important role in communicating with customers in a timely manner so that their future needs can be predicted and smarter investments made.
The manufacturing industry
Devices with IoT are already flooding the industry. ML is undoubtedly crucial to filling the gaps created by massive amounts of data. With data connectivity, automation, real-time error detection, cost reduction, asset tracking, supply chain visibility, and warehouse efficiency, ML is becoming a cornerstone of the industry. Machine learning will increase efficiency and innovation in the future.
Although machine learning algorithms are one of the most revolutionary technologies in recent years, the adoption of these technologies by organisations is still very embryonic. The benefits of these algorithms will be even more diverse in the future, providing organisations with improvements in productivity, the possibility to predict market trends and, of course, provide excellent customer service.
Machine learning technology has brought many benefits to the business world. Accurate prediction, automation, personalisation, cybersecurity and predictive maintenance are just some of the most important and prevalent for organisations across multiple industries. But the point is that machine learning opens the door to many new business opportunities. Therefore, it is a wise decision for any business to use them instead of ignoring the competition and falling behind.
Machine learning is having a profound impact on digitisation and is bringing positive changes to the above-mentioned industries around the world today. Not only in 2022 but also in the future, it is here to stay, to influence the progress and development of several companies.
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