Machine Learning for Business

According to the report by MMC Ventures, 2019, one out of ten enterprises uses about ten AI applications, including chatbots and fraud analysis. And by 2024, the global Machine Learning market is estimated to reach $30.6B with a 43% CAGR growth, compared to its state in 2020. Hence, it becomes easy to imagine the role that Machine Learning plays in the transformation of numerous industries today.

Machine Learning indeed brings certain benefits that result in better performance, significant savings of costs, and increased profit for companies. In this article, we will have a look at the most popular and beneficial applications of ML technology in some of the biggest industries.

Machine learning in finance 

Even though the financial industry has been considered a very traditional (and even legacy) one, businesses within it quickly realized how much a suitable technology can help. While some of the processes are still being performed in a “traditional” way, there are certain areas where machine learning works wonders.

Fraud detection

Fraudulent behavior is one of the biggest issues for any bank or other financial institution. Fraud can result in massive financial losses and, on average, costs approximately $80B annually. Not to mention the fact that, as technology advances, fraudulent behavior advances too and threat agents are getting more options to trick the system.

Luckily, there is an advanced digital solution to any digital problem and we are talking about machine learning here. By applying machine learning, financial companies can significantly reduce the chance of fraud happening. So how exactly does ML help? In order to better understand it, let’s compare the two ways of fraud detection within a financial company. 

The first method is a traditional one and is called a rule-based approach. In this case, fraud detection scenarios are written and updated manually. This, in turn, leads to a whole system being not flexible enough for adapting to the changing conditions and behaviors. Hence, a lot of risks might be missed in case there is no written scenario to detect them. As well, a rule-based approach is based on detecting the most common and obvious signals but is not able to detect hidden or non-obvious ones.

This leads us to the second method – machine learning fraud detection. Because an ML-powered model can analyze huge amounts of data, it then uses this data to detect hidden, non-obvious, and implicit fraud signals. Hence, ML-based fraud detection tends to be much more successful than rule-based detection. In addition to that, an ML model is capable of self-learning so in the future it can adapt to changing scenarios and identify new threats that were not met before. 

Smarter lending

If we talk about lending, one of its biggest challenges is identifying trustworthy borrowers. The inability of a lending company to separate trustworthy borrowers from fraudulent ones usually leads to massive financial losses for a company.

Machine learning successfully resolves the issue by using its ability to identify implicit patterns and hidden dependencies. In addition, the data fed to the ML model usually contains non-traditional data (i.e. social networks data) which is normally not used in traditional credit scoring. As a result, machine learning produces highly accurate results that lending companies can use for further estimation of borrowers.

Machine learning in healthcare

The healthcare industry impacts everyone’s lives so no wonder it always keeps up with the technological advances and implements the latest tech innovations, including machine learning. The main thing to remember about machine learning in healthcare though is that this technology is not here to replace medical workers but to assist them and increase the efficiency and quality of medical services.

Drug research and manufacturing

Did you know how long it takes to discover and manufacture a new drug? It was estimated that a pharmaceutical company spends about 10-15 years and approximately $4 billion on bringing a new drug to the market. And more than that, only 10% of the manufactured drugs really make it to the market, with 90% being unmarketable.

The good news is that machine learning can generate a value of about $100B/year, according to the McKinsey report. Here is what ML can do in terms of drug discovery and research:

As you can see, the whole idea behind using ML in the pharmaceutical industry is speeding up the whole process of drug discovery and making it less risky and more accurate, thus, helping companies save their finances and resources.

Medical diagnosis

Machine learning is all about analyzing Big Data and this ability makes ML a highly valuable tool for medical diagnosis. While it can be quite challenging to correctly identify a disease, its implicit symptoms, or even its development factors manually, ML uses all the available data to draw an accurate prediction of a possible disease. The data fed to the ML model includes:

By being able to analyze all this data, a machine learning model can easily find any risk factors or a developed disease with a high level of accuracy. This, in turn, significantly saves time for medical workers and allows them to focus more on disease treatment instead of its identification.

Machine learning in retail

One of the most popular and profitable uses of ML in retail is personalized recommendations. We all encountered them at some point while browsing an online store or simply seeing ads that were selected specifically for us. How does it work?

The machine learning system uses historical user data to draw assumptions on possible products and/or services that might interest a specific user. The data analyzed includes your past shopping history, searches, and product ratings. Based on that, the system can predict what might interest you and provides a corresponding ad. 

So what are the benefits of using personalized recommendations? While it’s quite obvious, let’s review them one more time:

The key point here is that the system offers relevant products and services to users and the guesswork is almost eliminated. And since irrelevant offers are what annoys users the most, it’s easy to guess how beneficial a personalized recommendation system is for any retailer.

The surge pricing dilemma

One more area of applying machine learning in retail is dynamic pricing. That means machine learning technology is used to predict the demand for a certain product and hence, help adjust the prices correspondingly. One of the most well-known examples of dynamic pricing is surge pricing, patented by Uber. Its concept is really simple: by using machine learning, Uber is able to predict the traffic patterns and demand for cars and adjust the pricing correspondingly. An example would be charging up to $135 for a one-mile journey on New Year Eve – how crazy does it sound?

And while Uber benefits from such dynamic pricing, customers (naturally) remain quite unhappy about it. This is why Uber is now focusing more on predicting high demand in order to prepare the drivers in advance so pricing will not be so high.

Machine learning in manufacturing

One of the biggest challenges for the manufacturing industry is maintaining low costs of unplanned downtime and maintenance costs. An inability to timely detect an issue with equipment may cost a company millions of dollars – and obviously, such a major issue requires an efficient solution aka machine learning.

Predictive maintenance

Usually, tech specialists perform maintenance according to the maintenance schedule (that is created manually) or by using SCADA (Supervisory Control and Data Acquisition) systems – but note that these systems have human-coded configurations too. Hence, manually performed maintenance still possesses risks of an error and can lead to equipment failure and malfunctions.

Machine learning algorithms, on the other hand, are not only capable to analyze the system and predict its possible failure or the nearest maintenance time. As well, an ML-powered solution can also detect sudden and unknown signals, such as temperature rise, and alert the personnel.

Predictive quality

Another way of deploying machine learning in manufacturing is predictive quality, also called predictive quality and yield. Predictive quality is aimed at revealing hidden causes of equipment malfunctions and production losses.

Needless to say, this solution is great for companies in terms of financial savings as specialists are able to immediately identify and dismiss the issue. In addition, the predictive quality solution can be configured to send automatic recommendations and alerts to specialists in order to notify them about a possible issue and share information and best practices.

Machine learning for supply chain management

Supply chain management is incredibly complex as it consists of many phases: from manufacturing the product to delivering it to the client. With hundreds of processes happening at each stage, it’s easy to lose track and miss something. This is where machine learning helps.

In terms of supply chain management, machine learning assists with the following:

Of course, this is not a full list of all the benefits that machine learning brings to supply chain management. However, these are the core areas that it covers and where it benefits the most.

Summing up

Without a doubt, machine learning is a highly valuable asset that can bring immense benefits and profit to any company that decides to implement it. On the other hand, it is important to realistically estimate whether your company really needs an ML-powered solution since it’s easy to buy into a buzzword without fully understanding the cost of its implementation.

At SoftTeco, we develop custom ML solutions and apply our data science expertise to help you strengthen your business. Curious to learn more or have any questions unanswered? Contact us and we will gladly provide you with all the information.