Thanks for being with us! This is our third and final blog on application of Data Science in Banking Sector.
It is important to know the customers and based on the knowledge every business has to recommend customer centric products or services to its existing and future clients. Banks are no different in this case. At SeekACE we have tried to develop the recommendation engine for the banks. In this blog, we will be discussing about development of Recommendation Engine and Real Time predictive analysis models with the help of Data Science for Banks.
Providing customized experiences to clients based on customer transactions and personal information to suggest offers and extended services. Banks also estimate what products the customer may be interested in buying after analyzing historical purchases. With this, banks will be able to recommend the product of the companies that have tied up with them.
It also recommends customer centric or product-centric offering based on their preferences. Banks can also recommend offers that are highly appealing to customers. There are two types of recommendation engines that are used by the banks –
User-Based Collaborative Filtering
In this case, we analyze the existing data of customers and try to figure out the consumer profile or group them with similarities. For large groups which gets created the recommendation engines prescribes or predicts the most useful solution to those customers. When we prescribe a solution, we try to determine what will be the future behavior of the customer based on their past transaction history.
Item-Based Collaborative Filtering
In this case, we try to group together customers who going through similar transactions. For example, the pattern of ordering online food for a group of customers or frequency of purchasing vocational tour programs for customers helps the banks to cluster them. In real scenario, there are many variables which are considered while clustering and this is where Data Science utilization comes into play. Now, what we do is, we predict the next buying cycle of the customer and the before the customer buys banks let them know about the products which can be more useful to their customer.
This is how the Recommendation engine works.
Real-Time Predictive Analytics
Predictive Analytics is the process of using computational techniques to predict future events. Machine Learning is the main toolbox of predictive analytics. Machine Learning is an ideal tool for improving the analytical strategy of the banks. With the rapid increase in data, there is an abundance of used cases and the exigency of analyzing data is at its peak.
There are two types of major analytics techniques –
Real-time analytics allows customers to understand problems that impede businesses. Predictive Analytics, on the other hand, allow the customers to select the right technique to solve the problems. There are areas like financial management of banking sectors that allow the industries to manage the finances and devise new strategies.
There are also other applications of Data Science in Banking Sector. Till date we have worked with few of these and through this article I wanted to share it with my beloved readers. We have developed an education center “Study@SeekACE”, wherein various professionals from different fields like Mathematics, Statistics and Computer Science background collaborate and co-operate with each other to develop and refine solutions practically useable by banks.
If you know or if you are working with Data Science and its applications please let us know. I will love to here [hear] from you. If are interested in learning Data Science you may contact us at any time, we will love to help you. If you wish to join our team you may send your updated resume at email@example.com we are an equal opportunity organization.
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