Ivy Feb 13, 2020 No Comments
So far we have posted several articles about Data Science, about technologies like R, Python, Tableau and Power BI, Data Science competitions, the top Data Science influencers on Twitter and many more. The ones who are actively pursuing this field will know that Data Science has spread its wings across all the industries. Various amazing use cases have been implemented over the years starting from the Finance industry to e-commerce. In this article, we are going to talk about several prominent Data Science use cases applied in various industries like finance, healthcare, Insurance, etc.
The moment we read the word fraud, we immediately relate it to monetary fraud. As a matter of fact, the Banking, Finance and Insurance sector is mired with fraudulent incidences. Cases of fraud can result in huge financial losses to banks. This also hits the customer’s trust in the bank related to the safety of their money. Many times fraudulent claims are made by customers to gain insurance money. This is a global menace.
The rise of Data Science has come to rescue for these industries from losses. Machine Learning and Deep Learning are being utilized to predict and detect potential fraud. The use cases like predicting loan defaulters, identifying credit card defaulters, fraudulent activities in insurance, etc are a few examples where these industries are able to utilize Data Science. Some real-time implementation examples can be found here.
It is a very common utility these days. Siri, Cortana, Alexa, etc. are being used as virtual assistants globally. It does help the user to some extent with its day to day simple usage on devices and gadgets by interacting with them. But that’s not it. The real power of virtual assistants is unleashed for the healthcare industry.
AI-Powered mobile apps can be used for basic health support. A patient can simply mention his/her symptoms and get relevant answers from a wide collection of patient data. Also, these assistants can provide 24/7 patient monitoring which can prove beneficial with an exploding senior population. Detailed usage of virtual assistants can be found here.
Image recognition is a powerful use case. It is now being used in various industries like gaming, retail, automobile industry, social media, security, etc. The implementation of this technology in social media needs no elaboration. Image recognition is widely put to use for security purposes. Netatmo is a smart indoor camera that starts recording when it detects an unknown face. Another use case is pet monitoring. Facial recognition is also seeing a rising usage related to security concerns at airports. Many countries are using satellite imaging for crop monitoring to inspect agricultural areas more quickly.
Additionally, the automobile industry working on autonomous cars is utilizing the image/object recognition technology to make driving safer by finding ways to reduce accidents, following traffic rules, etc. Another important use case implementation is in the healthcare industry. Using computer vision technology, x-ray reports, and other reports can be scanned and health issues can be identified with considerable accuracy. Real-time facial recognition is used to identify the emotions of admitted patients to understand how they are reacting to the treatment. There are a lot of other industries putting the use of this technology which can be read here.
Every industry is affected by the market fluctuations which happens due to various reasons. Prices of products and commodities are affected accordingly. It becomes prerogative for the manufacturers, retailers, and others to somehow analyze the market trends so that they can price their products efficiently.
The e-commerce giants are using Machine Learning algorithms to analyze several factors at once to generate optimized prices for their products. They even change the price value of a product several times a day. On the contrary, it can also be analyzed how the sale of a particular product can get affected if the price is varied. When the price optimization is done efficiently, it leads to increased customer loyalty as well.
Many industries like Banking, Insurance, Retail, e-commerce all need to know the life of their customers for doing efficient business. They all use Predictive Analytics using Machine Learning algorithms to solve this problem.
All the customer’s data right from the first transaction with the company to the most recent one, including all the good and bad experiences and feedbacks a customer has provided is analyzed to find customer’s life expectancy. This gives them an edge in making better plans to ensure the customers are retained and the life expectancy value is increased.
These are some highly used Data Science implementations in multiple industries. Furthermore, Data Science is massively used to solve climate change issues, food crisis, water scarcity, making life easy for mankind as well. There are various competition portals like Kaggle where contests are posted to find a solution to some real-time problems.
Last year Kaggle had a contest that wanted to improve the lives of pets by adoption prediction. Having said that, it will be interesting to see what all new use cases are developed and implemented this year.