Abstract

Language translation models (LTM) in the banking sector are highly revolutionary and essential for a bank's success in global expansion, especially for non-English-speaking customers. The translation should be accurate and culturally sensitive to avoid damaging the bank's reputation. Also, avoid any penalties for the bank. Expansion and outreach to diverse communities are essential in today’s competitive banking market. There are multiple advantages to adapting the language translation model in banking. The banking industry is supervised, and given the varied nature of the finance sector, it is crucial to employ language translation techniques to attract a diverse customer base, including speakers of various languages. A clear understanding of banking terms and regulations is essential for a positive customer experience. If a customer can't understand the banking terms, then there will be a lack of confidence in the process. A language translation model could make this experience easier. In many countries, banks and financial institutions are legally required to provide loan statements, disclosures, and other relevant information in the local language. Hence, accurate information must be passed to the customer. Mismanagement or misrepresentation of financial transactions can occur due to poor translation, potentially leading to economic losses, fraud, or other issues. Accurate language translation is necessary to avoid any penalties and legal consequences. A global footprint for a bank requires cross-border mergers and acquisitions. Language translation model plays a critical role in this scenario. Hence, we have discussed some of the handy cases where a language translation model can give leverage to a financial institution. We will discuss the architecture of this model and explore how AI and Machine learning can enhance its sophistication and robustness.

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