Home » Promising Use Cases of Natural Language Processing in Fintech Business
The fintech industry is constantly evolving as a highly competitive space. Given the saturated state of the market, several players are vying for the attention of individuals in the same consumer pool. It can be challenging for fintech companies to maintain their leadership positions, but there are ways to stay on top of this game. One of the reliable methods to stand out and attract consumers is to adopt new, innovative solutions that can enhance user experience. Natural Language Processing (NLP) is one of them.
Let’s take an in-depth look at some promising use cases of this technology in the fintech business.
Optimized Customer Service with Chatbots and Virtual Assistants
Programs that allow you to perform tasks using the user’s voice are not new, but still effective business solutions. By implementing the speech recognition function in your fintech application, you enable some voice-activated features, such as checking account information be it balance or transaction history, personal identification, or even voice-activated payments. For example, Westpac Banking Corporation has integrated its services with Alexa, allowing bank customers to check their account information from Alexa-enabled devices.
However, in addition to connecting ready-made solutions, you can go further and create your own chatbot or AI virtual assistant using the capabilities of NLP technology. NLP makes it possible for machines to do more than identify keywords. Instead of approaching speech or text as lined-up symbols, NLP-based chatbots comprehend intent and meaning from a client’s input. They then can respond in a human-like manner taking elements such as sentence structure, and context into consideration.
To do so, NLP-based chatbots employ a dialog system that features the two key aspects of Natural Language Processing: Natural Language Understanding (NLU) and Natural Language Generation (NLG).
NLU addresses the ability of conversation AI to process what a user is saying. Human language is undeniably complex thus NLU is essential as a chatbot or virtual assistant must understand the client’s needs to actually provide a response. In turn, Natural Language Generation allows virtual assistants and chatbots to give human-like answers.
Perhaps the most popular custom virtual assistant in the financial sector is Erica by Bank of America. Erica has a wide range of features and can provide users with balance information, notifications, bill payment assistance, and even personalized advice in making financial decisions based on a customer profile.
Effective Financial Document Processing for Banks
According to AI market research firm EmerJ, NLP-based offerings constitute 28.1% of AI-optimized products across various sectors. Information retrieval or document search-based products make up the majority of this percentage. Fintech companies handle vast quantities of data in the form of research and analytics reports, corporate filings, and much more. These documents come in a variety of formats, making them challenging to sort through. Not surprisingly, AI is one of the top fintech trends to help with this task.
NLP solutions can help conduct text analytics to extract relevant information from large loads of unstructured data, identifying core aspects of the text in certain document types for the appropriate classification.
Techniques that can be involved in this process include the following:
Named Entity Recognition: This entails spotting mentions of an entity that show up within the text. The computer then places them in specific categories such as person names, organizations, locations, time expressions, and monetary values. This can be used, for example, to automatically process invoices to reduce costs and speed up the payment process.
Sentiment Analysis: This technique recognizes and categorizes opinions that are conveyed in texts, whether articles, social media posts, or reviews. The machine could deem it positive or negative or take it a step further, giving a more detailed categorization of the sentiment. Financial companies can use sentimental analysis to determine customer opinions about their financial products to improve them in the future.
Text Similarity allows the computer to determine how similar sentences and paragraphs in a text might be and this can even be used for whole documents. It can then spot document duplicates as well as identify plagiarized material.
Text Classification: NLP solutions can examine the content of a text or document and classify it accordingly, saving a company the time it would take when worked manually by personnel. The document can then see swift action and processing by the relevant specialists. Text classification can help support teams of financial companies to prioritize requests and assign them to appropriate specialists. For example, if a customer sent a request for a refund, you can automatically assign the ticket to the accounting team.
Relationship Extraction: Also reliant on named entity recognition, this extracts semantic relationships between entities. It could process unstructured legal documents to identify the relationships between cases, companies, and the personnel involved.
Summarisation: This reduces the text into a well-organized summary of its core points. Summarisation could be extraction-based which entails extracting the most relevant sections without altering the document. It could also be abstraction-based which involves paraphrasing the contents of the text to reduce it. Automatic text summarization can be used to create financial reports and summarize large analytical documents.
Multinational investment bank Jp Morgan Chase’s COIN (Contract Intelligence) software utilizes NLP to help the bank’s legal team search and review large volumes of legal documents.
Improved Credit Scoring and Claim Processing for Insurance Companies
Insurance companies sort through large volumes of unstructured data in claim processing. With manual workflows, this can be tedious and can result in overlooked problem areas and even fraudulent insurance claims. Indeed, yearly, US-based insurance companies record losses scaling $80B due to fraudulent claims. Credit scoring is another sector where up-to-date relevant information is a requirement but isn’t always accessible. Financial platforms don’t have enough personnel to consider all the additional data and make better lending decisions.
NLP-based processing systems can help turn all of this around with the techniques mentioned above. Banks can automate the underwriting process which typically requires personnel to verify customer data to see if they qualify for loans. NLP systems can analyze and filter the relevant data to speed this up. NLP-based systems can also be incorporated into the application process. Indeed, fintech software experts can implement AI to loan applications to identify which customers fulfill the requirements.
Insurance companies can be better informed as NLP solutions can help them review all the necessary information related to a client’s credit history and finances for improved lending practices. Insurance companies can also utilize AI models for risk prediction as NLP technology can assess claims data swiftly enough to flag at-risk claims allowing these potential problem zones to be dealt with on time.
Companies across the fintech sector are transitioning to automated systems and NLP solutions are a major part of this. By introducing products that incorporate natural language processing technology you can give your business the competitive edge it needs to shine. Ensure you adopt the technology in a way that best suits your company’s needs and ultimately enhances your offerings.
To do this, be sure to enlist the support of experienced AI engineers who know all the challenges of technology implementation and the best practices to overcome them. Use cases of NLP in fintech are not limited to the above, and experienced specialists will always be able to find the best match between the capabilities of the technology and the needs of your business.