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Leveraging Natural Language Processing for Personalized Banking Services

EasyChair Preprint no. 13249

18 pagesDate: May 12, 2024


Personalized banking services have become increasingly important in the digital era, as customers expect tailored experiences that meet their specific financial needs. Natural Language Processing (NLP), a branch of artificial intelligence, has emerged as a powerful tool for improving customer interactions and delivering personalized banking services. This abstract explores the role of NLP in transforming the banking industry, enabling banks to analyze and understand customer data, provide customized recommendations, and enhance customer engagement.


NLP techniques enable banks to extract valuable insights from unstructured customer data, such as customer inquiries, social media interactions, and chat logs.  Employing machine learning algorithms, NLP models can analyze this data to identify patterns, sentiments, and customer preferences. These insights empower banks to understand their customers' financial goals, risk appetite, and spending habits, enabling them to offer personalized financial advice and targeted product recommendations.


Moreover, NLP-powered chatbots and virtual assistants have revolutionized customer support in the banking sector. These intelligent systems leverage natural language understanding and generation capabilities to engage in human-like conversations with customers. By interpreting customer queries and requests, these chatbots can provide instant responses, address common banking inquiries, and even execute transactions. Through continuous learning and improvement, NLP-powered chatbots can become increasingly proficient in understanding and responding to customers' needs, thereby enhancing customer satisfaction and reducing response times.

Keyphrases: Customer Experiences, Customer Preferences, Natural Language Processing (NLP), Personalized banking services, unstructured data

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Ayuns Luz and Harold Jonathan},
  title = {Leveraging Natural Language Processing for Personalized Banking Services},
  howpublished = {EasyChair Preprint no. 13249},

  year = {EasyChair, 2024}}
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