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Understand the Reasons behind Declined Transactions in Credit Card Portfolios with Machine Learning and AI

Introduction

In today’s rapidly evolving financial landscape, credit cards have become a ubiquitous tool for consumers, offering convenience and flexibility in making purchases. For banks and credit card companies, maintaining a robust credit card portfolio is essential for fostering customer loyalty and driving revenue. However, one significant challenge that these financial institutions face is understanding the reasons behind declined transactions. Fortunately, advanced analytics offers a potent solution to this issue, providing valuable insights into transaction declines and enabling proactive measures to enhance the overall customer experience. In this blog post, we will explore how banks and credit card companies can leverage machine learning and AI to gain deeper insights into declined transactions and, consequently, optimize their credit card portfolios.

The Importance of Understanding Transaction Declines

Transaction declines can be a frustrating experience for both customers and financial institutions. For customers, a declined transaction can lead to embarrassment, inconvenience, and the potential risk of a damaged relationship with the bank or credit card provider. For financial institutions, transaction declines not only result in lost revenue but also lead to increased customer churn and reputational damage. Therefore, understanding the root causes of transaction declines is paramount in mitigating these negative impacts.

The Role of Machine Learning and AI in Transaction Analysis

Machine Learning and AI involves the use of sophisticated techniques and tools to analyze vast amounts of data, uncover hidden patterns, and gain meaningful insights. For credit card companies, this means harnessing the power of machine learning and AI to analyze transaction data and identify the reasons behind declined transactions. Some key techniques include:

  • Machine Learning Algorithms: Machine learning algorithms can be trained on historical data to identify patterns associated with declined transactions. These algorithms can be fine-tuned to recognize anomalies and patterns and then predict potential declines in real-time.
  • Data Visualization: Utilizing data visualization tools, such as interactive dashboards and charts, can aid in presenting complex transaction data in a more digestible and actionable format.
  • Predictive Analytics: Predictive analytics models can anticipate transaction declines based on customer behavior, spending patterns, and other relevant factors, enabling proactive measures to prevent declines.

Identifying Common Reasons for Declined Transactions

By leveraging machine learning and AI, banks and credit card companies can pinpoint the most common reasons behind transaction declines. Some of these reasons include:

  1. Insufficient Funds: This is a prevalent reason for declined transactions, where the customer does not have enough available credit or funds in their account to cover the purchase.
  2. Suspicious Activity: Financial institutions use fraud detection machine learning algorithms and AI to identify potentially fraudulent transactions, which may lead to immediate declines to protect customers.
  3. Excessive Usage: Some customers may reach their credit limit, leading to transaction declines until they pay off their outstanding balance.
  4. Geographical Restrictions: Transactions made from unfamiliar or high-risk locations can trigger declines to prevent potential fraud.
  5. Expired Cards: If the credit card has expired, transactions will be declined until the customer receives a new card.
  6. Merchant Category Code (MCC) Restrictions: Customers may have restrictions on specific merchant categories, leading to transaction declines at certain retailers.

Enhancing Customer Experience with Machine Learning and AI

By understanding the reasons behind declined transactions, banks and credit card companies can take proactive measures to enhance the customer experience and reduce the frequency of declines:

  1. Personalized Offers: machine learning and AI can help identify customers with frequent declines and offer personalized credit limit increases or targeted promotions to improve their credit utilization.
  2. Real-time Alerts: Implementing real-time alerts can notify customers of potential declines, enabling them to take immediate action and avoid inconvenience.
  3. Fraud Prevention: Machine learning and AI can improve fraud detection capabilities, distinguishing legitimate transactions from suspicious ones, thereby reducing false positives and unnecessary declines.
  4. Geographical Flexibility: By analyzing customer travel patterns, financial institutions can adjust geographical restrictions to accommodate legitimate purchases made in unfamiliar locations.
  5.   Credit Limit Management: Machine learning and AI can help determine appropriate credit limits for individual customers, ensuring they have sufficient funds to cover their intended purchases.

Case study: TransOrg Analytics worked for a Fortune 100 American MNC financial services corporation, with USD 50 billion in annual revenues that specializes in credit, charge, and payment cards, in analyzing transactional data and identifying significant anomalies in variables such as challenged transactions, particular merchants across countries, merchant country, automated address verification code, product type, chip card indicator to understand most common occurring errors, merchants and servers driving these results. Transorg identified frequently occurring errors such as invalid formats in email id and DS requestor id and the merchants and specific servers causing this error.

Besides machine learning models TransOrg also developed a rule-based algorithm – decision tree to understand the combination of variables that lead to a decline and the type of transaction indicators were generally pointing to different results captured from POS device vs. chip card indicator. Transorg categorized “all other” category transactions using POS codes and found that most of the transactions are categorized are ‘zero card’ transactions. All the declined transactions were declining either due to ‘exclusion’ or ‘miscellaneous’ reasons. Identified specific merchants causing approx. USD 24 Million in losses each month because of declined or challenged transactions.

Transorg identified merchants generating bad quality data saving approx. USD 3 Million each month that reduced significantly the losses faced by the client when transactions are declined.

Conclusion

Incorporating machine learning and AI into the credit card portfolio management process provides banks and credit card companies with invaluable insights into transaction declines. By understanding the reasons behind declines, financial institutions can implement targeted strategies to improve customer satisfaction, prevent fraud, and optimize their credit card portfolios. As technology continues to evolve, the power of machine learning and AI will play an increasingly crucial role in shaping the future of the financial industry, ultimately driving growth and fostering lasting customer relationships.