Data Science: A boon to the banking and healthcare sector.
Digital computers have practically developed a perspective in every section of the economy in the past few decades. Earlier it was a vision that one day, machines would be able to learn. But today, almost all aspects are being replaced by big data and machine learning. For example, Netflix predicts the movies one might be interested in and Google, from search histories, knows what people want to explore. Facebook recognizes faces in photos. Webpages proffer ads of our interest and e-mails detect spam. Most of us use speech and text recognition almost every day. These are some of the examples of our daily life driven by machine learning.
Big Data Analytics is one of the most foreseen fields. Big business houses and internet giants are engaged in exploring the benefits of data analytics and have implemented the concept tremendously in the last decade to bring a great revolution in the disciplines of data search, online retailing, digital marketing, web mining, social networking community site growth and much more.
Data analytics is the process of scrutinizing data sets to derive an inference by which conclusions can be drawn using specialized systems and software. Data Analytics is playing a huge role in making business settlements which helps industries to shoot up their turn over. It has a huge market around the competitive world which can help in rendering better services for the customers and it can be applied efficaciously in all the spheres such as banking, healthcare, stock markets, logistics, transportation and supply chain management, etc
Let’s have a quick look at some of the applications of data science in banking and healthcare sectors:
Banking:
Data Science is used in almost every aspect of banking, be its customer outreach, risk management, pricing, marketing outreach, cost and revenue allocation or product development. The decreased customer loyalty and growing size of banks force customers to demand agile and efficient operational productivity. Typically, banks are looking at numerous ways to retain customers and comprehend their requirements. Banks are beginning to realize the significance of comparing and utilizing their internal data such as purchase history and patterns, debit and credit transactions, mode of interaction and trademark loyalty.
Internet banking, social media, and mobile banking have further flooded data into the banking operation. Digital banking generates huge amounts of data which is analyzed by the data scientist’s team who further unhitch new revenue possibilities for banks by separating and handling only relevant client info with the help of reliable machine learning models. They further invent a customized proposal that suits the needs of clients.
Data mining is extensively used to recognize potential customers for a new product. Enormous amounts of data such as indications of the client’s profitability, use of distinct banking products and services, as well as other client’s features are analyzed. Data scientists construct models using the demographic, behavioral, and historical data hence received to foretell the likelihood of a customer’s response to a proposal. Banks use this information to make effective, personalized outreach and enhance customer relations. With increasing competition, banks effectively use these resources to collect and preserve profitable customers. Hence data science helps banks make important decisions, and generate useful strategies from their client’s data.
Healthcare:
Health care systems currently produce large volumes of data in the form of clinical prescriptions, test reports, billing reports, and laboratory tests. It is a real challenge in health care systems to process this enormous data in various formats. It is crucial to predict and interpret the thus obtained data faster without being stored in traditional methods. Big Data Analytics is an approach that addresses these issues in health care systems. With the help of big data pipelines, it is possible to minimize the cost for several treatments, predict and detect diseases at more primitive stages and decrease the human error in diagnosis.
A lot of modern challenges such as reducing medical inaccuracies in diagnosis and identification of diseases, increasing the revenue of hospitals, improving patient safety, etc. can be solved using data science. The reduction of medical errors is very crucial to the reputation of any hospital. Sometimes, these medical errors can prove to be fatal. Right from admission to discharge of a patient, medical errors can occur at any stage. The increasing number of diseases pose a challenge to the healthcare sector to discover new medicines. Also, Existing medicines might not be affordable to the underprivileged.
It is here that data science comes to the rescue! It helps in predicting diseases at an early stage and hence reduce treatment expenses. Data Analytics not only helps in the predictive analysis but also in increasing operational efficiency. Data analytics helps to create and analyze summary reports or models so that, one can easily derive inferences from voluminous data.
Computers and internet apps have spawned high dimension data like text, photo, video, and voice at soaring speeds posing challenges for data analysis and decision-making processes. With this big data, interests in machine language have spurred.
Machine learning along with data mining is competent in solving a wide range of problems. Machine learning is a rapidly growing technology with numerous underexplored research opportunities. The social and ethical impact of machine learning will continue to stir the world’s dreams. Its ability to work on big data and predicting outputs for complex problems would help in creating a new framework for problem-solving.
Hoping that this article has inspired you to read more about Data Science and Machine learning and how it can prove to be a boon in various fields.
Thanks for reading.
Team FACE,
In c<>de we trust.
