Media Image of Correspondent Banking in the US (1990-2022): An NLP-based Analysis.

Reputation is an important aspect that has a major impact on the performance of banks and the overall banking system, including correspondent banking. However, it can be difficult to measure and evaluate. Reputation and trust are particularly crucial elements of (not only) the entire financial world. One important player that contributes to the reputation of correspondent banking is the media, especially newspapers and economic periodicals. This paper analyses the media image of the different actors of correspondent banking and the relationship between it and other important influences in the US between 1990 and 2022.

The study analyzes hundreds of articles from the most influential periodicals over the past 32 years using Natural Language Processing (NLP) techniques. The focus of the analysis is on content analysis, specifically sentiment analysis, and a comparison with the performance of correspondent banking. Archival sources, which include digital versions of newspapers, magazines, financial statements, annual reports, and earnings calls, are processed through OCR, and analyzed using Python programming and libraries such as NLTK, TextBlob, VADER, or Beautiful Soup. Major publications such as Reuters, Bloomberg, The Washington Post, The New York Times, The Herald (Everett), and The Capital are among the included sources.

The study also shows the strengths and limitations of NLP text data analysis. The principal advantages of the approach are the collection and analysis of enormous amounts of data. On the other hand, there are also risks of quantitative methods within historical sciences such as imprecision, incompleteness, lack of data, or confusion of correlation and causality. The findings, which are presented both in written and graphical form using Tableau, will provide valuable insights into the relationship between media image and cross-border financing.


NLP, Sentiment Analysis, Correspondent Banking, Economic Analysis, SWIFT, Cross-border payments

Petr Štěrba is a PhD student specializing in economic history at the Prague University of Economics and Business. He is also engaged in research in the field of text mining and alternative data analysis as part of his research at the Central Bank of the Czech Republic. Petr holds a master’s degree in Economic and Social History from Charles University, and has also completed a study visit at Northumbria University in the UK.