The implementation of Data Science in banking is changing the face of the banking industry rapidly. Each and every bank is searching for better ways. That will help them to understand the customers for increasing customer loyalty by providing more efficient operational efficiency.

The banks are trying to identify patterns in a large amount of available transaction data for interacting efficiently with their customers. With Data Science in banking, Banks utilize the data from customer transactions, previous history, trends, communication, and loyalty.

Extracting insights from such a large amount of data is a great challenge because this data is mostly unstructured which is difficult to deal with. Various methods of data analysis like data fusion and integration, Machine Learning, Natural Language Processing, signal processing, etc can be used for this purpose.

Banks are using Data Science for performing various important tasks like Fraud detection, Customer Segmentation, etc. In this article, we will walk through the different areas of banking in which Data Science is playing a significant role.

Wait!! Also, check how Data Science in finance is transforming the financial industries.

Use Cases of Data Science in Banking

The following are the most important use cases of Data Science in the Banking Industry.

1. Fraud Detection

Fraud Detection is a very crucial matter for Banking Industries. The biggest concern of the banking sector is to ensure the complete security of the customers and employees. Thus, the banks are searching for ways that can detect fraud as early as possible for minimizing the losses.

This is where Data Science is helping the Banking sector in achieving the necessary level of protection and avoiding financial losses. Data Scientists can improve the level of customer security. This can be done by monitoring and analyzing the different banking activities of the customers. So that they can detect any suspicious or malicious activity. The major steps included in the Fraud Detection process are:

Collecting a large number of data samples for training and testing the model.

Training the model for making predictions.

Testing the accuracy of the results and deployment.

Data Scientists need to have their hands on various data mining techniques like association, clustering, classification, etc. Just for working with different datasets and extracting some meaningful insights that can be applied to real-time banking problems.

For example, let us consider a system that holds further transactions if suddenly a large number of transactions occur from a customer’s account until the owner of the account himself verifies them. Such systems help the customers to keep an eye on their account activities.

2. Managing Customer Data

In today’s era of Big Data, banks have massive datasets to manage. Collecting, analyzing, and storing such an immense amount of data is difficult. Thus, various banking organizations are using various tools and techniques from Data Science and Machine learning. Just for transforming this data into such a format that it can be used for knowing their clients better for devising new strategies for better revenue generation.

Nowadays, many terabytes of data are being generated every day because of the increasing popularity and usage of digital banking. The Data Scientists first apply several methods to separate the data which is useful for them. The analysis of this data helps them to gain insights about customer behavior, priorities, etc. This will help them to build efficient models that produce more accurate results.

Applying different Machine Learning algorithms can help banks to derive new opportunities for revenue generation and take some important data-driven decisions.

3. Risk Modeling

The identification and evaluation of risks is a matter of concern for the investment banks. To regulate different financial activities and deciding the right price for various financial instruments banks use Data Science in banking. The different types of risk modeling are:

A. Credit Risk Modeling

This allows the banks to predict whether a customer will be able to repay their loan by analyzing the previous history and credit reports of the customer. The credit risk analysis helps to calculate a risk score for each individual case. Then the bank decides whether to sanction the loan or not depending upon the risk score value.

B. Investment Risk Modeling

The investment banks use risk modeling for detecting risky investments. This will help them to give better investment advice to the customers and taking the right decisions for increasing profit.

These are the reasons that make Risk Modeling so important for the banks. Now using Data Science solutions, banking organizations are designing new strategies for effective risk modeling. It will help them to make better data-driven decisions.

4. Customer Lifetime Value Prediction

Customer Lifetime Value Prediction( CLV ) value refers to the predicted value of the net profit. It is a value that a business will gain from a customer during their entire relationship.

The banks employ different predictive analytic approaches to predict the revenue that can be generated from any customer in the future. This helps the banks in segregating the customers in specific groups based on their predicted future values.

Identifying customers with high future values will enable the organization to maintain good relationships with such customers. It can be done by investing more time and resources on them such as better customer care services, prices, offers, discounts, etc.

The most commonly used Data Science tools for this purpose are Classification and Regression Tree(CART), Stepwise Regression and Generalized Linear Models(GLM). Finding and engaging reliable and profitable customers has always been a great challenge for banks.

With the increasing competition, the banks need to keep a check on each and every activity of their customers for utilizing their resources effectively. To solve this problem, Data Science in banking is being used by the banks for collecting, cleaning, and analysis of the customer data. Just for extracting actionable insights concerning customer behaviors and expectations.

Using Data Science models for predicting the CLV of a customer will help the organizations to take some suitable decisions for their growth and profit.

5. Customer Support

Providing effective customer support can help companies to engage their customers for a longer period of time. Customer Support is also a very important part of Customer Services. Helping customers to use the different services provided by the bank can help the banks to have a better interaction with their customers.

The various customer support services include replying to customer’s questions and complaints as early as possible for understanding your customers in a better way.

Data Science in banking is helping the Banking Industry to automate this service that will provide better and more accurate responses to customers. And, will also help the companies to reduce their investment of time and money on the employees.

6. Customer Segmentation

Banks perform the activity of Customer Segmentation for dividing the customers into specific groups. The groups can be formed either on the basis of customer behavior that is called behavioral segmentation or on the basis of some special characteristics of the customers that are called demographic segmentation. The demographic segmentation might include factors like religion, gender, age, income, etc.

Customer Segmentation helps the banks to invest their time and resources accordingly. There are different Data Science techniques such as clustering, decision trees, logistic regression, etc that can help banks. With these, they can predict the CLV for different segments of customers accordingly. Just for identifying high and low-value customer segments.

The customer segmentation helps the organizations to utilize their resources efficiently for increasing their sales by targeting specific customer groups. It is also used for providing better customer services and improving the loyalty of the customers.

7. Recommendation Engines

The key to success in any industry is offering those selected goods and services to the users which they really want. Different Data Science and Machine Learning tools can help the industries to identify the most suitable items for the customers by analyzing customer activities.

The Data Scientists take all the user data from their previous search history, transaction history, profile data. Just to analyze them and then predict the most accurate items that might interest the users.

The recommendation engines can be built by using two algorithms. The first one is the collaborative filtering method that can be either customer-centric or item-centric. It evaluates the user behavior to provide recommendations to new users.

The second one is the Content-Based Filtering algorithm, it recommends the most similar items to the user that are inspired by the products. With which they interacted during their previous activities. Any of the above methods can be used for building a recommendation engine according to your goals and circumstances.

8. Real-Time and Predictive Analysis

In the banking sector, each transaction of the user is a great source of data. On which we can apply various analytical methods and derive some useful information to predict future events.

Various Data Science and Machine Learning techniques are used for performing analytics in banking. The increasing amount of data has generated an increased number of opportunities for the Data Scientists to decipher something useful from that data that can help a business. There are basically two types of analytics used in banking:

Real-time analytics enables banks to consider the current scenario and take action accordingly.

Predictive analytics helps the banks to predict something about the future. Or we can say that it helps the bank to predict a problem that might appear in the near future and take suitable actions. Just to minimize its impact on the business.

Data Science in Banking Case Study

How Yes Bank used Data Science

We all have heard the name of YES bank which is one of the leading private sector banks and currently it is India’s fourth-largest private sector bank. The reason behind the success of Yes Bank is that they always keep their customers in the first place.

Therefore from the past few years, it has made significant investments in the analysis of customer data to design customer-centric business policies. They are focusing on predicting future events that might have some impacts on the real-time business by making use of Data Science in banking.

The Yes bank has made considerable progress and has expanded its business in the past few years by setting up a separate team of Data Scientists. Just for making some important data-driven decisions for providing the best possible services to their clients. They use various advanced Data Analytics and Data Science techniques for extracting insights from the customer data collected through different sources.

The YES bank performed a Recency, Frequency and Monetary, that is, RFM analysis on the data of customers’ debit card usage. So that they can provide a more personalized customer experience by providing targeted offers.

The results of this analysis were so overwhelming for the bank. As it increased the average spend of an individual customer by 29%. While the spending of the targeted customers increased by 44% that resulted in an overall 27% growth in the portfolio spend.

For doing this analysis, the Data Science team of the bank developed a predictive model by using a large amount of customer data. This helped them to increase their sales and operational gains.

Summary

After exploring the different applications of Data Science in banking we can say that Data Science is helping all the leading banking organizations. It helps in keeping up with the competition and providing better services to their customers. Data Science in banking plays a crucial role in various banking activities like fraud detection, developing recommendation engines, providing efficient customer support services, etc.

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