May 5, 2020 Last updated May 5th, 2020 3,582 Reads share

5 Sectors Where Machine Learning Is Making an Impact

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At the start of the year, Coronavirus (COVID-19) was making a few headlines and causing some concern around the world. Now countries everywhere are acting to prevent the spread of the virus. This includes closing schools and businesses, encouraging people to stay home (and work from home), and providing unprecedented economic support.

When humanity comes through this — as it will — attitudes to work and life are going to shift. Software solutions, including artificial intelligence and machine learning, are already playing an increasingly important role in our lives.  Could what we are going through change our relationship to these technologies?

Although it is difficult to predict accurately at present, this virus has already demonstrated how quickly humanity and our economy can adapt to working remotely. From the majority of us working in offices to millions suddenly interacting with colleagues, clients, suppliers and other stakeholders using messaging apps and VOIP solutions.

How Machine Learning Is Already Playing A Role

In many digital platforms and devices, people are using today, databases are collecting all sorts of information. We already have more data then has ever existed and the amount of data is growing exponentially. The world creates about 2.2 billion gigabytes of data every day.

Data sitting in servers, spreadsheets, and databases aren’t beneficial. However, when data scientists apply machine learning algorithms and techniques, that raw data becomes valuable. The untapped economic value of big data is over $3 trillion annually, according to a report from McKinsey. Healthcare, financial services, transport, and several other sectors are where these gains can be made.

Globally, our economy would benefit from a period of sustained innovation and uplift. There are already numerous ways ML is contributing to environmental and healthcare projects, both of which have wider ramifications than increasing investor value and returns.

ML can bring positive change to the economy in many ways, such as:

  • unlocking insights and actionable information in raw data,
  • supporting the creation of new data-driven products and services,
  • optimizing workloads and business processes across dozens of sectors.

Improved efficiencies, through machine learning and big data-backed automation, could ensure people spend time adding value, thereby changing how we work for the better.

5 Sectors Where Machine Learning Is Having A Positive Impact

Machine learning isn’t science fiction anymore. It’s already playing an important role across dozens of sectors, with projects of every size demonstrating its positive impact. Let’s look at five sectors where this technology is changing how companies operate and interact with their clients.

Financial Services

One of the first sectors to quickly start adapting ML technology was financial services. Vast amounts of data are generated, stored and processed in financial services; making it an ideal environment for testing ML algorithms and theories. Also, it’s worth noting that in financial services, there are much higher regulatory requirements than in other sectors.

Data stored within financial systems can be analyzed and used in many different ways. Financial firms have been applying various technologies over the years. But with machine learning, much larger datasets can be processed, analyzed, at greater accuracy rates, and the learnings can be applied more effectively. ML solutions for more accurate risk assessments, are already being used in investment portfolios to define strategic direction for business development.

ML is also used in fraud detection, thereby keeping financial and customer assets more secure. Customers are relying on them, and regulators come down heavy on those that fail to protect sensitive data, or suffer data breaches. As fraudsters and bots supporting them become smarter, banks need to take more proactive precautions. Cybersecurity uses ML solutions to detect a much broader range of threats than banks were used to dealing with.

JP Morgan, American Express, Experian, and many others often work with independent cybersecurity firms alongside internal security and risk assessment departments to analyze and neutralize threats. Financial companies have to take these threats seriously. For example, Palo Alto Network — a cybersecurity company — uses Magnifier (which runs on ML) to identify and neutralize threats that have got in through external defenses.


Legal work is data-intensive. Especially in court cases that are prolonged and include an enormous amount of discovery documentation and other information. Before the advent and widespread use of ML, AI and other data-driven tools, junior lawyers, paralegals and admin staff routinely sat and studied through piles and piles of documents.

Now with the development of a range of ML-powered solutions, this discovery phase of legal work can be completed with much greater efficiency. Documents can be scanned in, or uploaded, and computers with the relevant systems (most are custom made, although there are some off-the-shelf solutions from IBM, Google, Microsoft, and others) scan the data for keywords, before producing an analysis that lawyers can act on.


Healthcare is a sector that generates, processes and stores more data than most. Plus, the positive impact of AI/ML-powered solutions in this sector goes beyond profits and investor value; patients can experience improved healthcare outcomes and quality of life.

For example, there are already AI-powered diagnostic systems that support medical decisions with relevant data and insights. With more accurate, data-driven diagnostics, patients get the right treatment, mistakes are reduced, and outcomes are improved. Artificial neural networks, such as Kohonen’s Neural Network, are widely used in healthcare as a way of processing data and providing visual representations of complex datasets and diagnostic information.


 As populations increase, access to food is going to be a defining issue of this century. As a species, we need to increase productivity while reducing the impact on the environment, and maintaining enough bio-diversity to safeguard crops and livestock.

Automation, including robotics, is one way to achieve that. Alongside automation in agriculture, detecting weeds, monitoring soil, and diagnosing plant diseases are a few of the ways ML can directly support farming and food production. One such example is a collaboration between ICRISAT and Microsoft, which helped farmers improve crop yields with the AI Sowing App. The average increase in yield ranged from 10% to 30% across crops and locations.


In the transportation sector, AI and ML algorithms are used to predict, monitor and manage the flow of traffic. These solutions are increasingly useful when managing public transport too. Beyond these uses, self-driving cars and trucks are seen as the future of transport.

Even though self-driving vehicles are only in the testing phases, there are ongoing huge investments being made to achieve the ultimate goal. Safe, completely autonomous, self-driving and environmentally friendly vehicles. One of the ways this is being achieved is through the use of AI-powered software, such as computer vision and sensor fusion. With these technologies, objects can be analyzed in real-time, making self-driving control a practical outcome for vehicles in normal road conditions.

Bottom Line

As you can see, ML and other solutions in that category, are already making a positive impact in numerous sectors. Most projects start small, and one of the challenges many organizations have to overcome is the quality of the input data. Once that is overcome, machine learning algorithms can be applied to generate the sort of outcomes and added value that companies need.

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Dariya Lopukhina

Dariya Lopukhina

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