The Significance of Natural Language Processing in Banking

Natural Language Processing (NLP) is one Artificial Intelligent-based technology that is finding its way into a different number of verticals. While AI has established itself as a disruptive technology for decades, NLP is now making its graph up among AI vendor products in banking compared to those of other AI approaches.

NLP is everything related to human language, encompassing a various number of techniques which enable computers to understand human speech and text. Earlier, some of these techniques comprised rules-based statistical systems and later, machine learning.

NLP Solutions to Banks

There are several vendors selling NLP-based products to banks than any other single AI approach, growing with 28.1% of the total AI Approaches count across vendor product offerings. The largest shares of these NLP products are Information Retrieval, which often requires document search products. And Intent Parsing that often results in customer service applications, including chatbots. These two NLP-based products represent the two AI use-cases that are the most and least likely to be the focus of banks in years to come.

Today, banks are seeking to automate compliance processes, and here information retrieval or document search technology could assist them. Search capabilities could enable compliance officers at banks to find out germane information amongst thousands of digital documents relatively fast. As a result, they can determine whether wealth managers are interacting with customers in compliance with regulations or finding customer data and proving that it’s been deleted when a customer asks for their data to be purged as per GDPR. However, these processes don’t generate revenue for the banks, but merely ease risks.

Alternatively, customer-facing applications developed on intent parsing algorithms will likely have to wait for compliance processes to be automated and for NLP algorithms to advance before banks start focusing on building and deploying them.

Though a number of banks have introduced chatbots, which can only assist customers in very small ways, enabling them to perhaps check their bank balance. These chatbots are guiding customer inquiries to human employees when they can’t satisfy a customer’s intent.

Takeaways for Leaders in Banking

NLP can be utilized to assess a wide range of speech and text data from different contexts. It might assist banks to automate and optimize tasks like amassing customer information and searching documents.

Banks can expect NLP solutions from AI vendors to extract data from both structured and unstructured documents with a reasonable level of accuracy. Moreover, they might need to be aware of the fact that the data they have collected from transactions and loan documents in the past, might not be useful for training machine learning models unless it is appropriately cleaned and tagged.

In a nutshell, NLP is an imperative solution for banks as the industry rapidly shifting towards digitalization. It will be interesting to see how this technology will have an impact on the banking sector and how it will transform the industry at large.