Application of Machine learning With Example
Wherever there is a substantial amount of past data, machine learning can be used to generate actionable insight from the data. Though machine learning is adopted in multiple forms in every business domain, we have covered below three major domains just to give some idea about what type of actions can be done using machine learning.
Nowadays machine learning is being used very widely in the field of technology and science. You use it dozens of times a day without even knowing it. Some of its major applications or applications and their examples are as follows:-
Banking and Finance
In the banking industry, fraudulent transactions, especially the ones related to credit cards, are extremely prevalent. Since the volumes, as well as the velocity of the transactions, are extremely high, high-performance machine learning solutions are implemented by almost all leading banks across the globe. The models work on a real-time basis, i.e. the fraudulent transactions are spotted and prevented right at the time of occurrence. This helps in avoiding a lot of operational hassles in settling the disputes that customers will otherwise raise against those fraudulent transactions.
Customers of a bank are often offered lucrative proposals by other competitor banks. Proposals like higher bank interest, lower processing charge of loans, zero balance savings accounts, no overdraft penalty, etc. are offered to customers, with the intent that the customer switches over to the competitor bank. Also, sometimes customers get demotivated by the poor quality of services of the banks and shift to competitor banks. Machine learning helps in preventing or at least reducing customer churn. Both descriptive and predictive learning can be applied for reducing customer churn. Using descriptive learning, the specific pockets of problem, i.e. a specific bank or a specific zone or a specific type of offering like a car loan, may be spotted where maximum churn is happening. Quite obviously, these are troubled areas where further investigation needs to be done to find and fix the root cause. Using predictive learning, the set of vulnerable customers who may leave the bank very soon can be identified. Proper action can be taken to make sure that the customers stay back.
Virtual Personal Assistants
Siri, Alexa, and Google Now are some main examples of virtual assistants. These applications help people find information by recognizing spoken words. For example, if you want to know “What is the capital of India?” Or you want to set an alarm for 6 am. So you can do all this work very easily by speaking to the virtual assistant and asking.
the insurance industry is extremely data intensive. For that reason, machine learning is extensively used in the insurance industry. Two major areas in the insurance industry where machine learning is used are risk prediction during new customer onboarding and claims management. During customer onboarding, based on past information the risk profile of a new customer needs to be predicted. Based on the quantum of risk predicted, the quote is generated for the prospective customer. When a customer claim comes for settlement, past information related to historic claims along with the adjustor notes are considered to predict whether there is any possibility of the claim to be fraudulent. Other than the past information related to the specific customer, information related to similar customers, i.e. customers belonging to the same geographical location, age group, ethnic group, etc., are also considered to formulate the model.
Using this technique, things can be recognized from a digital image. Police where it is used by other security-related departments to identify criminals. Also, it can be used by various government departments or non-government departments such as Passport or Aadhar card-making organizations to recognize the faces of people.
According to the type of activity you do on any social media such as YouTube, Facebook, and e-commerce website such as amazon, Flipkart, etc., different types of information are shown to you because machine learning it is Presents information to you as per the likes and dislikes of all social media and e-commerce websites. Not only this but advertisements on these websites are also shown to you according to your choice.
Online Fraud Detection
Machine learning capabilities are also used to make the internet world a safer place. Billions of dollars are transacted on the Internet every day. Along with this, there are also various criminal activities related to Virus Attacks, Malware attacks, Spam Emails, and financial fraud in crores every day. To protect against all these criminal activities, the experts of the institution providing security understand the patterns of fraud using machine learning and keep on fixing the loopholes in the system so that such threats can be avoided in the future. For example, companies like PayPal use it to differentiate between legitimate or illegal transactions between buyers and sellers.
Traffic Alerts (Maps)
How Google Maps application uses machine learning gives information about the correct route to reach a place and the correct traffic present on that route at the present time. To provide this type of information, Google collects a lot of information, and based on this information, it makes an idea at which time to choose which path may prove to be the best option.
Self Driving Cars
Companies like Tesla and Google have made self-driving cars using machine learning which means no driver is required to drive such a vehicle and very soon such vehicles will be available for use in the market.
Online Customer Support
You must have seen the option of live chat with customer support on many websites. However, on all such websites, there is no staff available to answer your queries. In most cases, it is the chatbot that you talk to. The chatbot is a type of application, which provides you the answers to the questions asked by you using machine learning.
Wearable device data form a rich source for applying machine learning and predict the health conditions of the person real time. In case there is some health issue which is predicted by the learning model, immediately the person is alerted to take preventive action. In case of some extreme problem, doctors or healthcare providers in the vicinity of the person can be alerted. Suppose an elderly person goes for a morning walk in a park close to his house. Suddenly, while walking, his blood pressure shoots up beyond a certain limit, which is tracked by the wearable. The wearable data is sent to a remote server and a machine learning algorithm is constantly analyzing the streaming data. It also has the history of the elderly person and persons of similar age group. The model predicts some fatality unless immediate action is taken. Alert can
be sent to the person to immediately stop walking and take rest. Also, doctors and healthcare providers can be alerted to be on standby. Machine learning along with computer vision also plays a crucial role in disease diagnosis from medical imaging.