This article was originally featured in Talk Fintech.
Numerous financial procedures can be accelerated with NLG when machine learning is used in conjunction with them, freeing up human resources for more complex tasks.
FinTech and NLG, or Natural Language Generation, are continually developing new, creative solutions that assist the financial sector in making better policies and safeguarding assets. Its use in the industry has grown significantly within the banking sector as a result of significant advancements. NLG can be utilized in FinTech for text analysis and human response. Below are some cases where NLG has impacted FinTech Industry-
Natural Language Generation is boosting workflow automation in the FinTech industry, giving more exponential value propositions in gathering business data. These advanced NLG has helped in detecting a range of complexity in conversations analyzing customer mood and satisfaction levels, and then generating sentiment analysis. With time, these customer data can be consolidated into a buyer persona to enable personalized financial services, products, and promotions reflecting customer journeys.
Emmanuel Walckenaer, CEO of Yseop, says, “With financial service firms generating tons of data, the industry as a whole is in search of FinTech solutions that can make sense of it all. The usefulness and effectiveness of an organization’s data when it comes to business growth are dependent on the way in which it is gathered, analyzed, interpreted, and stored. “
With Natural Language Generation management systems, the administration can easily overcome the root cause of the issues involving document generation. In the Fintech industry, especially in Insurance, the NLG software helps in navigating customers through the claiming process and developing a simple policy approval easily.
The companies are required to have a well-coded NLG solution to streamline document generation without any human intervention. The document analysis based on NLG builds keyword-based indexing and reviews databases comprising interferences, implications, and connected material through the analysis of language structure to return comprehensive results.
“In 2023, FinTech solutions that lean on emerging technologies like Natural Language Generation will provide more key decision-making insights and analytical opportunities that financial companies have not been able to rely on as much in the past.”
The NLG works much better when paired with Optical Character Recognition (OCR) technology as it analyzes scanned and handwritten documents and transforms those documents into new, clean versions.
Natural Language Generation (NLG) works as well as a sales tool that banks use to enhance customer engagement. With conversational AI, the FinTech industry is empowering CRM software by mitigating the requirement for manual entries and updates. The NLG evaluates all conversational patterns and notifies financial firms guiding them with investment suggestions such as most profitable sectors, customer retention, and customer satisfaction with the financial services they serve.
Through NLG-based conversational chatbots, Fintech companies can evaluate a customer’s loan or credit card request by checking the individual digital footprints consisting of their social media profiles, browsing history, and travel history to gather data and transform it into an accurate credit score.
Conversational banking is making a significant shift from simple chatbots to well-equipped digital assistants. Therefore, Fintech companies are investing in virtual assistants with advanced capabilities. This way NLG based chatbots help companies with functionality, translating user queries into information that can be used by automated systems for appropriate responses. This helps FinTech companies in counseling customers on bank account management. Notifying customers when approaching the spending limit and flagging payments for anomaly detection.
NLG-based customer support is going to be an important part of the worldwide protection market. A significant factor in the success of InsurTech is Artificial Intelligence. Applications that use Machine Learning can process the enormous volumes of data necessary to generate improvements in effectiveness and efficiency. This FinTech industry has many uses for InsurTech, such as customer-facing applications, more options for small business insurance, and customized policies.
“Financial data reports and articles will soon be the most widely used form of consuming analytics, and Natural Language Generation will keep enhancing their correctness. NLG is able to explain the analyzed data with quality and efficiency, leaving important data employees with more time for creative tasks,” adds Emmanuel.
Financial institutions and FinTech companies are using RegTech technology as a tool to decrease regulatory risk and reduce the expenses associated with compliance issues. It cannot be used for commercial purposes because it is currently under development and has a very small spectrum.
“Financial companies that continue to adopt NLG will find new ways to automate sophisticated reporting and analysis as the technology grows, and other enterprises that are thinking about adopting new technology will find that it can save them lots of time and money,” says Emmanuel.
In 2023, NLG can be used to underline specific places of charts where anomalies may appear that indicate profit or risk in the FinTech business. It can be used alone or in combination with prediction algorithms. Active market players can benefit from a better macro-level perspective using NLG, which will help them make better decisions.
If interested in learning more about financial workflow automation or seeing a demo, please contact firstname.lastname@example.org.