Practical Example: Data Science in Banking

Practical Example: Data Science in Banking

Introduction:

How Data Science can Improve the Profitability of a Bank’s Credit Card Business

The credit card business of a bank was not generating the expected profits, prompting a need to optimize the pricing strategy of this segment. However, it was crucial to avoid customer attrition due to increased interest costs. The question that arose was: which customers would accept higher interest costs, and which would cancel their credit cards if the interest rates were raised? To avoid cancellations, clear customer segments needed to be identified. The goal was to offer new, more lucrative credit products to less price-sensitive customers without jeopardizing customer loyalty.

To identify different customer groups, a clustering analysis was conducted, where customers were grouped into distinct segments based on several attributes. This analysis allowed for the consideration of multiple attributes simultaneously and the ability to adapt the segments dynamically with new data. Additionally, a clustering model facilitated the evaluation of new customers and their assignment to an existing cluster if relevant data was available.

The bank had access to diverse customer data, including personal information, credit product usage, transaction history, and customer-bank relationship information such as contact with customer service, complaints, and Net Promoter Score. After subjecting customers to the clustering analysis based on these attributes, various groups were identified. A comparison of these groups revealed customers for whom the extent of services offered by the bank was more important than the interest rate. These customers were thus considered less price-sensitive regarding interest costs. Another segment identified customers who would accept an increase in interest rates due to frequent credit card usage. By determining these less price-sensitive clusters, the bank was able to offer new and more lucrative credit products to these customers.

To prevent customer churn, the bank also monitored customer satisfaction and loyalty. One way to assess customer satisfaction and loyalty is to examine their language when interacting with customer service. AI technologies can determine the emotional state of customers based on their choice of words, whether spoken or written. Positive emotions generally indicate loyalty and satisfaction, whereas negative emotions often appear in complaints or negative reviews that can lead to customer loss. The bank aimed to detect and prioritize requests with negative emotions, most likely complaints or negative reviews, in order to respond promptly and avoid potential customer attrition. Positive or negative emotions expressed in language can be examined through sentiment analysis, using AI technology to analyze human language. Incoming messages and reviews were subjected to an automatic pre-analysis using this technology, enabling the prioritized handling of messages associated with negative emotions. As a result, response times to complaints were reduced by 50%.

By applying different approaches, the bank was able to optimize its credit card business, measure customer response to interest rate increases, and monitor customer loyalty in real-time. Clustering allowed for the identification of customer segments with similar attributes, enabling the bank to offer new and more lucrative credit products to less price-sensitive customers, thus increasing the profitability of the credit card business. Natural Language Processing facilitated the capturing and analysis of customer emotions during phone or email interactions with customer service. Prioritizing negative messages improved customer satisfaction and loyalty.

This example illustrates how Data Science can provide insights that can be effectively utilized in the banking industry. The Haufe Akademie offers seminars and training on Data Science that can equip professionals to communicate with Data Scientists effectively, ask the right questions, and delve into the world of programming. Explore the qualifications that suit your needs and stay ahead in Data Science.

About Haufe Akademie
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Full Article: Practical Example: Data Science in Banking

How Data Science Can Help Banks Increase the Profitability of Their Credit Card Business

Introduction
The credit card business of a bank did not generate the expected profits, prompting the need to optimize the pricing strategy of this business segment. However, it was crucial to avoid customers cancelling their credit cards due to increased interest costs. The main question that arose was: which customers would accept higher interest costs, and which would cancel their credit cards if the interest rates were increased? In order to prevent cancellations, it was necessary to first identify clear customer segments. The goal was to offer new, more lucrative credit products to customers who were less price-sensitive, without jeopardizing customer loyalty.

Identifying Customer Segments through Clustering Analysis
To identify the different customer groups, a clustering analysis was used to group customers into distinct segments. Clustering analysis is a machine learning technique that groups data points, in this case customers, into clusters or segments based on similarities in various attributes. This approach allows for the consideration of multiple attributes simultaneously. Additionally, the segments can be dynamically adjusted when new data is added to the analysis. Furthermore, a clustering model provides the opportunity to evaluate and assign new customers to existing clusters, given the relevant data about them.

Customer Segmentation Process
The bank had access to diverse customer data, including personal information such as age, gender, creditworthiness, number and type of credit products used, number and type of transactions made with the credit card, as well as information about the customer’s relationship with the bank, such as contact with customer service, complaints, Net Promoter Score, etc. After subjecting customers to a clustering analysis based on these attributes, different customer groups were identified. A comparison of these groups revealed that some customers valued the range of services offered by the bank more than the interest rate, indicating that they were less sensitive to price differences. Another segment showcased customers who would accept an interest rate increase because they frequently used the credit card. By determining these less price-sensitive clusters, the bank was able to offer new and more lucrative credit products to these customers, thereby improving the profitability of the credit card business.

Measuring Customer Loyalty
In addition, the bank wanted to closely monitor customer satisfaction and loyalty to avoid customer churn. One way to assess customer satisfaction and loyalty is to examine the language customers use when communicating with customer service. By analyzing their choice of words, whether spoken or written, AI technologies can determine customers’ emotional states. Positive emotions generally indicate loyalty and satisfaction, while negative emotions often manifest in complaints or poor ratings, which can lead to customer loss. The bank aimed to identify and prioritize requests with negative emotions, such as complaints or negative reviews, in order to respond promptly and prevent potential customer loss. Analyzing positive or negative emotions expressed in language can be achieved through a sentiment analysis, in which AI technologies are used to analyze human language. Incoming messages and reviews were subjected to an automatic preliminary examination using this technology. Messages and reviews associated with negative emotions were given priority in handling. Through prioritized handling, a 50% reduction in response times to complaints was achieved.

Conclusion
This project allowed the bank to optimize its credit card business and measure customer reactions to interest rate increases in real-time by employing various approaches:
– Clustering analysis enabled the bank to categorize customers into clusters that shared important attributes. By identifying less price-sensitive clusters, the bank was able to offer new and more lucrative credit products to these customers, making the credit card business more profitable.
– Natural Language Processing was used to capture and evaluate customer sentiments expressed in phone conversations with customer service or in emails. This prioritized handling of negative messages subsequently had a positive impact on customer satisfaction and loyalty.

Curious to learn more?
This is just one example of how data science can provide valuable insights that can be utilized to generate profits or cost savings in the banking industry. If you want to communicate with data scientists on the same level and ask the right questions at the right time, or if you want to dive deep into the world of data science and learn how to program yourself, Haufe Akademie offers the qualifications that suit your needs. Check out our current course offerings related to data science on our blog.

About the Author:
Haufe Akademie is one of the leading providers of qualification and development for individuals and companies in the German-speaking region.

Summary: Practical Example: Data Science in Banking

With the help of data science, a bank can increase the profitability of its credit card business sustainably. By optimizing the pricing strategy of this business segment, the bank can avoid customers switching due to increased interest costs. By using clustering analysis, the bank can identify different customer segments and offer new, more lucrative credit products to less price-sensitive customers without jeopardizing their loyalty. Additionally, by analyzing customer language through sentiment analysis, the bank can prioritize and respond to complaints or negative feedback, ultimately improving customer satisfaction and loyalty. This project showcases how data science can optimize the credit card business and measure customer reactions and loyalty in real-time. If you want to learn more about how data science can benefit the banking industry, the Haufe Akademie offers seminars and training on the topic.

Frequently Asked Questions:

1. What is data science and why is it important?

Answer: Data science is an interdisciplinary field that combines statistical analysis, machine learning, and various scientific techniques to extract insights and knowledge from data. It plays a crucial role in making informed decisions, predicting trends, and enabling organizations to gain a competitive edge. By leveraging data science techniques, businesses can optimize operations, improve customer experiences, and drive innovation.

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Answer: Data science has significantly transformed various industries. In finance, data science helps detect fraud, predict market trends, and optimize trading strategies. In healthcare, it aids in disease prediction, drug discovery, personalized treatments, and patient monitoring. Retailers leverage data science to enhance customer experiences, optimize pricing and inventory management, and conduct market segmentation. These are just a few examples, and data science has vast applications in numerous sectors, impacting decision-making, efficiency, and innovation.

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