Unleashing Radical Transformation — Top 5 Industries Impacted by Machine Learning
TechnoStacks Infotech just published a new story on our App Developers Blog under the title Unleashing Radical Transformation — Top 5 Industries Impacted by Machine Learning.
To think of Machine Learning (ML) as just another tech buzzword would be doing an immense injustice to this technology. ML, as a subset of Artificial Intelligence (AI), is disrupting processes and systems across diverse business domains by delivering real-time, meaningful data-driven insights. These are helping businesses make better decisions, enhance their products/services offerings, improve productivity and do everything that makes them more customer-oriented and competitive.
The focus of ML is automatic learning and improving from this learning without being specifically programmed for the same. This technology is driven by the development of ML algorithms for specific tasks, and which access data to learn by themselves, without any human intervention. The nature of ML and its application benefits has meant businesses have begun using ML to revolutionize the way they function. According to IDC’s Spending Guide, the worldwide spending on Cognitive and AI systems will reach $19.1B in 2018 and grow to $52.2B in 2021. This means we are starting at an ML market that is maturing fast and is all set to reach new heights in the future.
So where is this spending happening?
The answer is simple. It is happening across industries. In this article, we take a look at the top five industries that are being disrupted by Machine Learning:
Financial Services
If there is one industry you want to pinpoint, which is reaping the benefits of ML, it is the Financial Services industry. The backbone of any financial service is data, which is what drives ML as well. The financial ecosystem and its working are propelled by data which is used for customer-oriented decision making, enhancing the internal process and, most importantly, risk assessment. ML can help essentially automate these critical processes and make them more accurate.
Fraud Detection and Prevention: With ML, banks and other financial institutions find it easy to stay on top of the large number of ways fraud happens. The need of the hour is putting an ML backed data-intensive system in place that can detect unusual activities that do not follow normal procedures, and immediately flag these to concerned stakeholders. ML can be used to not only detect anomalous behavior on the system but also to ensure that such transactions are declined, leaving no scope for fraud.
Managing Risks: One of the key challenges of financial institutions is risk management. At the most basic level, it involves making sure that an individual who wants a loan is creditworthy. Traditional software applications that measure creditworthiness depend on static information, but ML will enable better decision making by predicting creditworthiness based on dynamic data that includes changing economic scenario, market trends and other variables.
Productivity: Very often key decision makers at banks are burdened by mundane activities like account monitoring, exhaustive documentation processes, and record keeping. With ML, the burden of these activities can be shouldered by ML systems, which will free up a lot of time that can be utilized for other revenue generation activities.
Investment Predictions: Traditional predictive analysis systems are limited by their inability to make sense of unstructured data. Very often, it is this data that can offer key insights into market movement, price fluctuations, commodity pricing etc. that can help make better investment decisions. With ML, investment recommendations can be backed by drill down data evaluation and analysis, which makes for more profitable decision making. The use of ML can also automate certain functions that make following investment recommendations easier and more seamless for investors, big and small, alike.
Healthcare
Modern healthcare and its various disciplines depend on data to make life-saving discoveries and decisions. Some of the critical qualities required in this domain that help improve healthcare processes, medicinal research and patient care are data collection and analysis, critical thinking, accurate decision-making and also comprehensive observational skills. The use of ML can bring all these qualities to the table and help improve healthcare as a whole:
Identification and Diagnosis: This is one critical area in which machine learning is helping doctors identify illnesses correctly and make an accurate diagnosis. There is a tremendous amount of data available out there regarding specific diseases. There is also data resulting from the huge number of clinical trials taking place across the globe. ML helps researchers and doctors make sense of this data with the help of cognitive computing to improve diagnostics and therapeutic treatments. The use of ML is also driving the development of complex medical technologies that can help treat serious diseases in a more precise manner
Customized Treatment: While two patients can suffer from the same illness, the treatment procedure can differ based on the individual patient’s health history, bodily data and other parameters. This can ensure a more effective treatment that is in line with the patient’s complete health data. ML enters the picture to help make this possible through the use of data in conjunction with predictive analytics. Tailor-made treatment decisions, especially for critical illnesses backed by the analysis of patient medical history and available treatment data can make patient treatment and care even more effective.
Clinical Research: This is a hugely data-intensive field and ML can make successful interventions in clinical trials by enabling the use of a wider range of data that results in more successful learning and outcomes. Clinical researchers can analyze and evaluate data that not only materializes from clinical trials but also from the candidate’s overall medical history, genetic information and more; this can lead to more successful clinical trials. ML can also improve the efficiency of the clinical trial process and also the safety standards.
Education
ML helps in driving more successful learning outcomes and customized goal setting for each student. As the mainstream adoption of ML empowered learning solutions will continue, learning will be more student-driven than ever before and teachers will be able to track how their learning is being consumed by each student, which means it will be more results driven:
Improved Learning Process: With ML, you can use a combination of data insights and predictive analytics to plan a curriculum that is best placed to deliver results. This data can include historical curriculum records, learning outcomes and results; teachers can analyze these to understand what worked and more importantly which concept didn’t. Such data can also be used to identify the correct techniques that can be used to achieve a particular learning outcome. This can enhance the efficiency of the learning process.
Custom Learning Paths: One student is different from the other when it comes to capacity for understanding a particular subject. A particular learning methodology can work for one student but will fail to achieve the desired results in another. The question is how do you understand and analyze this differentiation and implement learning strategies that are beneficial for all stakeholders. ML helps to answer this question and implement personalized learning strategies for students.
Enhanced Student-Teacher Alignment: There are students that have special learning needs and there are teachers who have the experience and expertise to satisfy these requirements. The only problem here is, it is very difficult to match these students with their ideal teachers. But, ML helps simplify such appointments. An ML empowered data system can ensure this student-teacher alignment seamlessly. This improves the learning outcomes of students with special needs.
Transportation
One of the most innovative uses of machine learning is happening in the transportation industry. While the use of ML in transportation is still in its nascent stages, there is no doubt that this particular industry will reap rich dividends with the help of ML. The use of this technology is helping improve transportation infrastructure in many different ways:
Reliable Transportation Solutions: Companies such as Uber are using ML to improve the seamlessness and reliability of the Uber experience for customers. From accurate trip growth estimations to improving the efficiency of the ride-sharing marketplace and from identifying fraud accounts to offering optimal pickup and dropoff suggestions, ML is helping Uber improve its services dramatically. You will find other companies in the transportation domain using ML in a similar capacity.
Traffic Management: Traffic congestion is a problem not limited to a few cities in certain parts of the world, but it is a global phenomenon and this problem continues to rise. The use of machine learning can help put in place various solutions that can solve the congestion problem and optimize travel time for commuters. One of the ways ML intervenes to offer a solution is by using a combination of data and predictive analytics, wherein authorities get information on where a congestion can occur, and steps can be taken to reroute traffic before this congestion takes place.
Logistics: We are seeing ML make positive contributions in logistics, especially when it comes to improving shipping efficiency, bringing down overhead costs and improving the overall quality of shipping processes. The integration of ML has helped shippers speed up and improve decision making, automate certain processes and maximize the potential of their fleets by optimizing the use of Transportation Management Systems.
Marketing
There are plenty of data-intensive functions that make up marketing and each of these functions can be improved through the use of ML technology. It is revolutionizing marketing because marketing is all about getting accurate customer and campaign insights. ML is helping marketers offer better and more engaging content and personalize campaigns keeping specific needs of customers in mind:
Improved Customer Experiences: What is it that wins your customers over? It is a completely customer-centric experience, which means marketers must know what a customer wants. This learning is provided by ML as marketers can leverage a range of data and craft each and every marketing approach including promotion and selling from the customer viewpoint. They can also predict better marketing outcomes
Personalized Messaging: Everything about marketing is about the customer. Cookie cutter messaging worked once upon a time, but that time is long gone. A marketing message must be customized to meet the needs and demands of a specific customer. But for that to happen, a marketer must know the level of personalization needed to make a positive impact. This is possible through ML wherein customer and market data can be combined to offer more targeted messaging. This will improve the quality of Marketing Qualified Leads (MQL) and even Sales Qualified Leads (SQL) resulting in more business profitability
Foster Long-Term Customer Relationships: Customer churn is a part and parcel of a business, and successful businesses make sure that they are able to convince customers to stay. With ML, this process gets a little easier as data can help marketers know which customer is at risk (more likely to leave a business). This will help determine the best course of action to take, if they think a customer is no longer interested in their products and/or services. The right intervention at the right time can help decrease customer churn.
Conclusion
These are just some of the ways ML is helping industries improve their products and/or services and the way they handle their customers. ML ensures that data can be used to make clearer and more profitable business decisions when it comes to operations, product development, customer service and marketing.
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Originally published at www.appfutura.com.
