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How Machine Learning (ML) Is Changing Our Life: 3 Interesting Applications

If you stay abreast of tech-related news, you’ve likely come across a few stories covering machine learning. An ever-growing number of applications and services are leveraging this technology to produce faster and better results. And the market for machine learning isn’t slowing down any time soon; more than $37 billion in funding has been allocated toward machine learning as of 2019, and the worldwide market for machine learning is slated to surpass $209 billion by 2029.

The modern digital age has required that businesses, large and small, rapidly adapt so they can process more data and stay competitive. Machine learning has allowed these businesses to adapt and explore machine learning-driven business ideas, of which there is a seemingly endless supply across many industries. 

To that end, we’re going to explore some of the most interesting and useful machine learning applications that span different industries, as well as what the future holds for machine learning.

1. Medical Industry

New applications of machine learning in the healthcare industry are allowing doctors to make their patient treatment and communication more efficient. For example, patients will soon be able to benefit from less expensive treatment thanks to machine learning-enabled systems that ingest patient data and recommend which medical tests can be avoided. Techniques in machine learning will also increase how efficient clinical trials are and the overall efficiency of research in a clinical setting, eventually leading to more effective patient treatment.

Patient Communication

Patient communication services are a great example of a tool that uses machine learning in a direct healthcare setting. Doctors can rely on a patient communication service that utilizes machine learning techniques for many important tasks, including automating appointment and recall reminders, providing patients with different in-person and remote payment options, online scheduling, and much more. Patient communication services make it much simpler for healthcare professionals to stay connected with all of their patients at multiple touchpoints, from the moment they first begin treatment up to the final payment.

Personalized Medical Treatment

Machine learning also makes it more viable for doctors to create personalized treatments. ML-enabled systems can ingest patient data such as their family medical history and lifetime health records and then compare that data to current research to recommend personalized medications and treatment. Machine learning can significantly impact personalized treatment moving forward once it begins assessing patients’ genes and genetic markers that are best addressed with specific medications.

Personalized medicine allows for the selection and delivery of treatments that are specific to a patient and that give that patient as positive of an outcome as possible. The greatest hurdle to overcome when it comes to machine learning-enabled personalized medicine and treatment is finding the best patient-specific treatment even as the number of positive-response predictors (genetics and biomarkers, for example) and treatment options continue to grow. 

A good example to look to is the Combined Pharmacotherapies and Behavioral Interventions for Alcohol Dependence (COMBINE) Study, one of the biggest clinical trials in the United States whose primary analysis focuses on alcohol dependence. The Study uses CART (Classification and Regression Trees) methods that take into account multiple potential predictors in order to find out which combinations of positive outcomes and patient characteristics may exist. A customized tree-based method such as CART  allows for the accurate selection of optimal patient-specific treatments based only on baseline characteristics.

2. Finance Industry

As a growing number of businesses are focusing on driving more e-commerce sales and catering to online shoppers, cases of fraudulent transactions continue to rise. Luckily, machine learning-enabled algorithms allow these businesses to push back against fraudsters and mitigate cases of fraudulent transactions. 

Fraud Detection

The Coalition Against Insurance Fraud estimates that acts of fraud rob American consumers of at least $80 billion annually. As such, a huge goal of machine learning applications in the world of finance is to detect, minimize, and ultimately prevent acts of fraud from occurring. Machine learning is ideal for fraud prevention: it can parse through huge sets of data to detect unusual behavior patterns.

Machine learning systems enable financial institutions to detect irregularities and identify subtle changes in large data sets in a much better precision (image source: Sdk Finance).

A machine learning-based application operating in the finance and banking domain could, for instance, analyze each transaction a customer makes to assign it a fraud score. This score can indicate how likely it is that a transaction is fraudulent; if a customer transaction is indeed fraudulent, a machine learning-based application can block and flag it to be manually reviewed.

This entire process occurs within seconds, particularly since a machine learning-based application can assign a certain threshold to fraud scores that automatically triggers rejections if scores are too high. Without the help of machine learning, this process would be exceedingly different since no human is capable of reviewing thousands of data points in a matter of seconds to make split-second decisions.

Loan Eligibility Prediction

The technology can also be applied to the banking industry to predict loan eligibility. Essentially speaking, banks generate a profit from the loans they give out, and it’s necessary for them to appropriately validate loan eligibility before offering them. Machine learning-based applications can help banks determine whether a loan recipient is likely to repay a loan or not. 

3. Face Recognition & Computer Vision

If you’re interested in growing a business through social media marketing, you know you need a way to engage many of your users simultaneously. Machine learning exists at the core of the most popular social media platforms to both engage users and benefit those platforms: from targeted ads to news feeds specifically tailored to user interests, machine learning has allowed social media applications to rapidly evolve how they keep their users entertained. 

Among the most popular applications of machine learning in social media is face recognition. Popular social media platforms such as Meta use face recognition to identify users’ faces by referencing just a couple of tagged images. Face recognition algorithms are highly accurate and follow the steps of detection, classification, and recognition to detect a human face and accurately classify it as a real person’s face. Face recognition, like the kind that Meta employs, is achieved with the help of deep neural networks that ingest multiple pictures with labels and help train machine learning models to accurately differentiate between objects in those labeled images.

Before it shut down its facial recognition system in 2021, Meta's use of facial recognition on Facebook was one of the most popular applications of facial recognition technology in the context of social media. Facebook users who opted into the system's ‘Face Recognition' setting would be automatically identified in videos and photos. 

At one point, more than one-third of daily Facebook users had been using the ‘Face Recognition' setting and were able to be recognized across any photo or video that was uploaded to the Facebook website. Users also had the option to be automatically notified whenever they were identified in videos or photos that other Facebook users had posted on the website, and Facebook would even provide users with recommendations for users that they would want to consider tagging.


If we’ve shown you anything in this article, it’s that modern applications rely extensively on machine learning-based algorithms. Plenty of technologies these days are predicated on ML models, and applications of machine learning will continue to provide advancements in technology. As of 2022, it’s predicted that three-quarters of end-user solutions that rely on machine learning will be constructed with commercial rather than open-source platforms, meaning machine learning will continue to unlock exciting opportunities businesses can take advantage of. It’s only a matter of time until most organizations leverage machine learning-enabled solutions in some form or another. 

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Article by Lee Li Feng

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