4 Best Use Cases of Data Science in Banking Industry

Data Science is booming, and tools like Python, Pandas, NumPy, and PySpark are gaining hype like never before. All the industries, including Finance and Banking, use Data Science tools to get the best out of their industry. This article will discuss data science use cases in the banking industry.

With the help of data science, the finance and banking industry is benefitting greatly in revenue generation and process optimization.

Banks acquire a lot of customer data and use it to sell their products and services. But the necessity to grow faster and keep up with the competition, big data technologies like Machine learning and artificial intelligence can help them make smarter decisions for their growth.

Processes in the banking industry require improvement and automation. With this much data and less time, banks are trying to use the data effectively to provide a better customer experience.

There are many ways in which banks can use data science to improve performance and give out-of-world experiences to their customers. Now, let us talk about how the banking industry can harness the power of data.

1. Customer Data Analysis

2. AI Chatbots For Customer Support

3. Fraud Detection

4. Recommendation System

Use Cases of Data Science in Banking

1. Customer Data Analysis: Based on the collected customer data, banks can perform demographical analysis on their customer datasets and find meaningful insights from the data that can help them understand their customers in a better way.

With the help of Data Science tools like Python and Pandas, banks can understand the patterns, age of their customers, the area which has most of their customers, their transaction patterns, and can understand their behavior and perceptions about the bank.

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2. AI Chatbots For Customer Support: To make their users feel important and experience priority customer support, banks can serve their customers by using Artificial Intelligence based Chatbots that can mimic a human and solve the customers’ problems without involving any human.

The use of AI chatbots on a bank’s web or mobile app can increase the rate of interaction and user engagement on the bank’s portal. It can also reduce the customers’ waiting time and help users get all the types of information they need.

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3. Fraud Detection: Machine learning algorithms are very helpful in detecting fraud related to insurance, credit cards, and OTPs. Banking companies all around the globe are investing billions of dollars in fraud prevention and making the world a safer place for their users.

All the financial institutions like banks, loan companies, insurance companies, credit card companies and the rest of them can detect fraudulent activities using customer databases and samples.

Fraud activities in the banking domain can affect the brand’s name significantly. Hence, the banks are willing to reduce the chances of these fraud activities using model sampling, data analysis, and testing based on the received data.

4. Recommendation System: Like Netflix, banks can analyze their user’s patterns and give them a personalized experience based on their past interactions, engagements, and behavior.

If some user is searching for a property loan on Google or social media, banks can present them with ads related to the same services. By doing this, they can give users a personalized marketing experience based on the patterns of the users and increase their revenue.

Showing exactly what the user is looking for can act like magic in the world of the internet.

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