With the increased popularity of Machine Learning (ML) and Artificial Intelligence (AI), almost all businesses started to wonder how they can leverage these technologies for higher business visibility, better user engagement and valuable insights. While ML and AI are now used in different industries, the meaning and opportunities they offer for businesses are growing.
And if you are looking for fresh ML use cases, this guide is your wild card. It’s unsurprising that Machine Learning considerably impacts many industries, like healthcare and cybersecurity. But do you know that use cases grow almost every day, and you have already collected the most unexpected ML use cases for businesses in 2023?
Emails are currently one of the most popular personal and professional communication tools. However, they also open wide doors for various malicious attacks like email phishing. This way, hackers can extract sensitive information by tricking the end-users.
That is where ML can be a helpful tool for detecting cybersecurity attacks since it can conduct real-time email monitoring. Machine Learning uses Natural Language Processing as the key technology to analyse email content and identify the risks of phishing attacks. Email monitoring performed by ML can also be done through anomaly detection methods.
Machine Learning Against Bots
While bots make up to 25% of the overall web traffic, they also make it harder for companies to track their real traffic and can even negatively affect website performance in some cases. That is because the lion’s share of these bots are malicious and can cause huge damage to the company’s health, including data breaches, malware attacks, and other dangerous threats. Bots can even take a company’s networks and apps under control to perform malicious activities. And this industry is also developing, leaving no space for traditional methods to deal with these bots.
Machine Learning technology can effectively fight bots. For example, bots on Twitter are quite tricky since they tend to spread fake information and cause misinformation. ML-powered Twitter bots can be used to identify good and bad ones, while a bot detection feature analyses many factors to deliver accurate and reliable results.
Customer Journey Optimisation
A smooth and engaging customer experience is vital for companies of all sizes. This is also a crucial factor for search engineers, which rank websites with the quality customer journey and behaviour higher. Machine Learning technology has been shown to be effective in marketing, so it quickly became a go-to tool for optimising customer journeys.
The key idea behind the process is to optimise the customer acquisition cost to a chosen conversion point. For example, one of the most popular approaches is the top-down method. It checks customer objectives, including purchase patterns, pricing, and competitors, to map the marketing touchpoints with the customer goals. A lack of data insights makes it harder for companies to earn significant revenue through this approach.
Fraud Detection and Secure Transactions
ML offers a vast range of use cases and tools powered by this technology designed for cybersecurity. The technology itself is believed to be applicable in increasing the transaction’s security by identifying the possibilities of fraud. For example, credit card fraud detection is a ready solution to improve transactional and financial security.
There are many tools that organisations use to improve their cybersecurity and not only in the financial domain. For example, ML-powered tools like openappsec help to increase web app security for businesses in different industries. The great thing about all these ML tools is that they work automatically and don’t need human involvement daily. They offer a real-time report of fraud and risks since they constantly monitor the possibility of fraud and generate alerts accordingly. Based on a study, banks spend $2.92 for every $1 lost in digital fraud, which is considered to be a recovery cost, it is clear that the use of ML is a profitable investment for banks.
Accurate Skin Cancer Diagnosis
Convolutional Neural Network algorithms are trendy in the healthcare industry since they have been shown to be effective in recognising and classifying images. Healthcare is one of the key domains where ML has almost endless use cases. It is also crucial for the industry to always have the most accurate results, so ML technology is the way to go.
For example, CNNs are already used to detect skin cancer, where the diagnosis accuracy is up to 95% using TensorFlow. The tech field constantly develops new tools and software that greatly helps in identifying skin cancer in its early stages using the CNN technique. The maximum accuracy of traditional systems and manual approaches is 85%.
Hiring & HR System Improvement
With the huge change in the labour market and the popularity of remote work, Machine Learning and similar technologies will certainly dominate the recruitment market as well. ML technologies were introduced to the HR domain a few years ago and have greatly improved many processes that have been done manually before. Therefore, ML in hiring can help reduce time-consuming tasks, greatly speeding up lots of processes.
ML and AI-powered tools and software products help companies to find perfect candidates twice faster than without using them since they allow for more accurate and fast searches based on many factors. In addition, ML tools can help identify the most qualified individuals before they even speak to recruiters, browse resumes effectively, and perform interviews without candidates visiting the office.
One of the most popular ML use cases in business is the authentication process. ML makes authentication secure and intuitive thanks to computer vision, image recognition, and natural language processing. In addition, businesses using machine learning are able to reach a wider audience since NLP provides easy access to worldwide locations, language holders, and ethnic groups.
Both Machine learning and artificial intelligence are the two most popular technologies today. Even though they’ve been here for many years, the market saw real potential in them during the last few years, which was mainly influenced by the COVID pandemic. However, machine learning isn’t a magic spell that will save all businesses and work in every circumstance.
Here we collected the most unexpected ML use cases in businesses in 2023 so you can explore whether it can suit your business needs or not. Make your analysis and adopt this technology if you believe it can benefit you long-term.