4 Business Fields That Can Benefit from the Implementation of Machine Learning Services

In recent years, Machine Learning (ML), a vital component of AI, has emerged as a game-changer in automating various business sectors. With the continuous expansion of the internet, social media, and interconnected devices, there is an overwhelming influx of digital information. To cope with this data deluge, businesses are continuously seeking efficient solutions to filter and extract valuable insights. The ongoing expansion of the internet, social media, and connected devices has led to a vast amount of digital information, creating a pressing need for businesses to find effective solutions to filter and manage this data. ML is now regarded as a key tool in automating various business fields. This has sparked interest in exploring how to make money with AI, as ML plays a crucial role in leveraging data to generate profitable outcomes.

Although the term has only become popular in recent decades, the history of Machine Learning dates back to the 1950s, when the researcher Arthur Samuel first developed a program that successfully learned to play the game Checkers. ML is a discipline of Artificial Intelligence, designed to study algorithms and models, to help computers perform tasks, without prior knowledge of the system behavior model. It was designed to simulate the same learning capacities that humans have, processing information that is used later on to deliver accurate results in response to new input.

As the ML technology is maturing, businesses and organizations of various fields and sizes can benefit from its advantages. While Machine Learning can be used in many industries, to help make real-time business decisions, eliminate manual tasks or enhance security, there are some business fields where ML plays an industry-changing role.

Finance

Although reluctant and conservative towards it at first, the financial industry is a perfect fit for Machine Learning, especially because of the petabytes of data that flood financial platforms every day. Fortunately, more and more financial companies are adopting machine learning services, to reduce operational costs, increase revenues and reinforce security.

One of the main goals of financial companies is to minimize fraud, and for this to happen, they need solutions that can quickly analyze large volumes of data. ML can do so, by analyzing and spotting unusual patterns, blocking fraudulent actions with extreme accuracy. At the same time, by using ML, financial institutions can predict the creditworthiness of a client, by analyzing market trends and relevant news that can influence clients' abilities to pay.

In terms of customer service, AI chatbots are no news to the financial industry. But by using ML technologies, the bots can adapt their responses based on the behavior of each individual customer, resulting in a chatbot or financial assistant that acts and feels almost human.

Of course, one can not talk about the benefits of ML, without mentioning automation. Financial institutions are leaving spreadsheets in favor of cloud-based data storage, opening the possibility for smarter solutions. Blockchains can use smart contracts to automate many processes, but any fintech company that wants to maximize operational efficiency will want to incorporate ML to their data processes as well. It can be used to interpret documents, analyze data and even perform real-time audits of an institution’s processes, helping with regulatory compliance.

Healthcare

By using advanced analytics, medical staff is able to provide more valuable information to patients, based on their recent blood readings and medical history. Stanford University was able to develop a ML algorithm that is able to differentiate cancerous and non-cancerous skin lesions, in the hopes of putting a much faster diagnose on skin cancer patients.

Machine learning could also be used in the Pharma industry, to develop personalized treatments that are much more effective, as they are based on the patient’s entire medical history. These treatment recommendations can be based on smart health records. But transferring hand-written information into a computer can still take a long time, which is why new technology is focusing towards handwriting recognition, taking the first step towards smart health records.

In terms of outbreak prediction, ML-based technologies are being used to monitor and predict epidemics that may happen around the world. By collecting and analyzing social media updates, satellite data and reliable website information, artificial neural networks can predict any type of outbreak, from mild to severe infectious diseases. This comes in extremely helpful in third-world countries, where education and medical infrastructure are lacking.

Travel and Hospitality

Booking flights and hotels have almost entirely turned into an online experience for avid travelers, thus generating a large amount of data on their travel habits. This is giving insight to AI algorithms, which helps customize offerings and even plan an entire trip, offering a new and individually designed experience for them.

Chatbots seem to be gaining more and more popularity in the hospitality industry as well, being able to provide customer support 24/7. Currently, there are two types of chatbots that are being used in the travel industry:

• First type chatbots: they need to be manually programmed to deliver a set of pre-programmed answers, for questions such as: “What it the weather in London tomorrow?” and “When is the next flight to New York?”
• Second type chatbots: more complex, AI-driven bots which can understand commands and learn during interactions. These are used to answer more customized and complicated questions, such as “Where can I travel with $200?”.

Taking the example of Netflix, YouTube and other platforms that are focused on customized recommendations, travel providers are starting to incorporate ML in their business. By storing and analyzing the digital footprints of customers, they can tailor offerings based on the traveler's needs and preferences.

Marketing

Marketing professionals are constantly looking for ways to improve their lead scoring methods and Machine Learning seems to do just that. With the help of ML, marketers can monitor customer behavior, by writing algorithms that can track website visits, downloads, followed accounts, likes posts and adds that customers engage with.

After analyzing market conditions and spending habits in certain areas, companies are able to profit from dynamic pricing models, common for travel and entertainment industries, but fairly new to retail. This way, companies can use machine learning to make market predictions and generate more profit from their pricing strategy.

Algorithms can also predict which type of ad content will be more fitted to each visitor, giving marketing companies the opportunity to customize ads accordingly. This will give visitors a personalized customer experience and lead to higher visitor engagement.

Machine Learning is here to stay, and based on how fast technology is evolving, one can only expect to see it interfere with more industries, reshaping them to fit the ever-changing needs of humans. If understood and used to its maximum potential ML can be a valuable tool for all businesses, streamlining customer experience, offering smarter and faster solutions and even saving lives in a matter of seconds.