Banking transaction categorization: Leveraging Machine Learning and Artificial Intelligence in the financial industry
Executive summary of Banking transaction categorization
Open Finance and Open Banking are revolutionizing the financial services industry by making structured and especially unstructured data available from multiple sources (banks, telcos, insurance companies...) through multiple channels (scraping, OCR, API, etc...) and at a very high rate.
It is imperative to bring order and structure to these unstructured assets in order to extract useful information for the customer, thus opening the door to the opportunities offered by the open data economy, which requires the use of advanced analytics tools.
Categorization engines enrich financial transaction information by adding a “category”: a name that gives a meaningful description of the nature of the transaction (e.g., “salary”, “mortgage”, or “food and daily expenses”). To accomplish this task, the engine classifies the data according to some sort of criteria, such as merchant, location, or transaction amount.
This component plays a critical role in many banking processes, including digital engagement, risk management, fraud detection, and compliance. However, as the volume of financial transactions continues to grow, traditional classification methods, such as manual or rule-based categorization, are becoming increasingly difficult to scale.
The emergence of new technologies, such as machine learning (ML) and artificial intelligence (AI), has opened up new opportunities for transaction enrichment in the banking industry.
This situation has led banking institutions to look for reliable partners who can handle the inherent complexity of the open environment. This white paper summarizes some of the most common questions CRIF has received from its customers on their journey to take advantage of machine learning techniques.
