Do you know your customer?

Maximizing the value of transactional data  


Among the assets held by banks, transactional data stands out as one of the crucial but often underrated  sources of customer information. The dynamic development of artificial intelligence has enabled the automatic enrichment of such data with various attributes, such as transaction category or merchant/brand  recognition. These resources can be fully employed in the bank's internal processes and customer presentations, intending to heighten client engagement on the banking platform and mobile applications.

Our eBook confirms that properly analyzed and enriched transactional data provides valuable insights into customer preferences and their buying patterns, allowing future behavior prediction and recognizing market trends.

Information about users is invaluable for companies offering products or services to individual customers, especially in the banking industry, but an excess of data complicates their effective utilization in business processes. As a result, important questions arise: which customer information is the most crucial? What should data analysis focus on? Our goal is to highlight the potential inherent in transactional data as a key source of information about bank customers.


Izabela Czech

Author, Data Science Analyst

What can you learn from our eBook?

  • What advantages do banks and financial institutions derive from enhancing transaction data analysis capabilities with ML models?
  • How banks and financial institutions can benefit from the full range of information contained in transactional data?
  • What valuable information does transactional data enrichment provide?
  • Which examples of machine learning models enrich transactional data?
  • In which cases can customer transaction analysis help improve marketing strategies?
  • How applying insights from transactional data can contribute to building better customer relations?

Proper analysis of customer transactions can bring multiple benefits to banks and financial institutions, making data analysis tools and techniques a vital element of their operational strategy. However, a challenge in transactional data analysis is the difficulty in automatically interpreting key attributes like recipient name or description or defining the actual purpose of the customer during the transaction. To leverage the full scope of information in transactional data, incorporating additional attributes is necessary, achievable through the application of machine learning models.


Jakub Porzycki

Machine Learning Team Leader


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