Over the years, the impact of machine learning on our everyday lives has increased. Used in a number of applications, from email filtering and online shopping algorithms to self-driving cars, machine learning is one of the most common subsets of artificial intelligence. According to SAS, “While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data … is a recent development.”

It should come as no surprise that machine learning, given its ubiquity, is shaping the landscape of the healthcare, finance and accounting industries.


In the healthcare industry, machine learning is already making a huge impact. On the diagnostic front, different data analysis technologies are helping doctors detect and diagnose diseases earlier than previously possible. Medium contributor Igor Bobriakov writes, “The deep-learning based algorithms increase the diagnostic accuracy by learning from the previous examples and then suggest better treatment solutions.”

These new technologies are also advancing the efficacy of genomic mapping and the creation of new drugs. Bobriakov writes, “The goal is to understand the impact of the DNA on our health and find individual biological connections between genetics, diseases, and drug response.” These burgeoning algorithms help speed up the process of drug development, and facilitate our understanding of genetic links to disorders and diseases.


Data analysis and finance go hand in hand, and machine learning algorithms are shaping the finance industry in significant ways. Financial Executives International contributor Tony Levy writes, “Machine-learning technology can provide FP&A [financial planning and analysis] teams with more opportunities to uncover and analyze business drivers from external and internal data that can help finance leaders make … more insightful business decisions.”

This technology can interpret different variables, ranging from past sales data to weather forecasts, to help inform and streamline the decision-making process. The ability to interpret nearly countless streams of disparate data into actionable information has the potential to completely change the landscape of the financial sector.


Thanks to advances in new technology, some jobs in customer service and data entry may be on the chopping block. Bloomberg contributor Jeremy Kahn writes, “Accenture Operations, the company’s outsourcing unit, once used human workers in mostly low-wage countries … to handle routine data entry and customer service tasks for clients. Now that unit is hoping new software will help clients’ achieve further savings by … eliminating the need for humans altogether.”

While this development might sound borderline dystopian, these technologies have the potential to make a single worker more productive by orders of magnitude. As with any paradigm-shifting technology, some collateral damage is likely, but accounting professionals who familiarize themselves with these new innovations are likely to land on their feet.

Machine learning has the potential to advance every industry it touches, and then some. This is evidenced by things like Amazon and Netflix algorithms that make suggestions based on past behavior to shoppers and viewers. Understanding these groundbreaking technologies is vital to anybody starting a career in these industries.

Learn more about Texas A&M University-Corpus Christi’s online MBA programs.


ActiveWizards: Top 7 Data Science Use Cases in Healthcare

Bloomberg: Accenture Debuts Platform That Automated 40,000 Roles

FEI: How Finance Can Use Machine Learning to Improve FP&A Practices

SAS: Machine Learning – What It Is and Why It Matters