The banking and financial industries have been making data-driven decisions for more than a century. Since a wrong decision could have a heavy cost for a financial institution, they have been one of the early adopters of big data. A lot of machine learning use cases in the banking, financial services, and insurance industries (BFSI) have been using structured data like transactional history or CRM history. However, over the last few years there has been an increasing tendency to use text data to mainstream underwriting and ...
© Mathangi Sri 2021
M. SriPractical Natural Language Processing with Python https://doi.org/10.1007/978-1-4842-6246-7_44. NLP in Banking, Financial Services, and Insurance (BFSI)
Mathangi Sri1
(1)
Bangalore, Karnataka, India
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