Machine Learning: Who’s Using it and Why

October 11, 2017

Whether we realize it or not, we encounter machine learning on a daily basis. Aside from in our day-to-day lives, industries from retail to government and more are depending on machine learning to get things done. Below is a short list of how different industries are utilizing this technology. 

This is not a complete list, as dozens of industries are using machine learning in a vast number of ways.


With its quantitative nature, banking and finance are an ideal application for machine learning. The technology is being used in dozens of ways industry-wide, but here are a few of the most commonly used:

  • Fraud – Algorithms can analyze an enormous amount of transactions at a time, and learn a person’s typical spending patterns. If a transaction is made that is unusual, it will reject the transaction and indicate potential fraud.
  • Trading floors – With its ability to efficiently assess data and patterns, machine learning can assist with quick decision-making in real-time.
  • Credit and risk management – Typically assessing credit risk is labor intensive and is prone to human-subjected errors. With machine learning, certain algorithms can help to provide mitigation recommendations.


Utility companies can utilize machine learning in a number of ways, including uncovering hidden energy patterns, learning customer’s energy behaviors, and more.


  • Diagnoses – Machine learning can analyze data and identify trends or red flags within patients to potentially lead to earlier diagnoses and better treatments.
  • Patient information – Data can be collected from a patient’s device to assess their health in real-time. Drug discovery – Given its ability to detect patterns within data, scientists are able to better predict drug side effects and results of drug experiments without actually performing them.


  • Personalization – Machine learning allows online brands to suggest and advertise things you may like based on your browser and search history. Brands use their collected data to give customers a unique and personalized experience.


  • Energy sources – By analyzing different minerals in the ground, machine learning provides the potential to discover new energy sources.
  • Streamlining oil distribution – Algorithms work to make oil distribution more efficient and cost-effective.
  • Reservoir modeling – Certain machine learning techniques can focus on optimization of hydraulic fracturing, reservoir simulation, and more.


  • Efficient transportation – Analysis of data can identify certain patterns and trends to make routes more efficient for public transportation, delivery companies, and more.


To learn more about this technology and the various ways it is advancing technologies we use every day, download our whitepaper.

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