Beyond Traditional Algorithms: Leveraging LLMs for Accurate Cross-Border Entity Identification

This process involves assigning a unique identification code which is key to maintaining operations tracking and risk valuation at an optimal level. In the current global market context, an accurate identification of foreign entities also helps regulatory authorities to better monitor credit institutions' economic and financial activities, reinforcing national and international standards compliance as well as financial information transparency and integrity. Additionally, these unique identifications play a critical role in preventing fraud and money laundering by providing a standardized method for the identification of counterparties involved in financial transactions. These identifications are currently assigned through a labor-intensive entity-matching process which consists of receiving a daily list of foreign entities whose details (name, address, legal form...) are compared against the available source of reference (hereinafter referred to as ASR). ASR includes a series of datasets sourced from a wide range of different national and international databases such as Einforma (mainly Spain and Portugal), Companies House (UK) or Bundesanzeiger (Germany). If the information of the incoming record matches all attributes in the ASR, the identification will be approved and given a unique code (a new or an existing one). On the contrary, if there is no match (or a very poor matching) between the incoming data and the ASR, the incoming record will be rejected. Therefore, there is a permanent entity-matching challenge since small differences between incoming data and the ASR could easily lead to wrong conclusions, for example, considering two datasets as different entities when they are actually referring to the same one and vice versa.