The Promising Duo: Five Use Cases for Natural Language Processing in FinTech

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Since financial technology (FinTech) has always been one of the early adopters of new technologies, AI software development seems to have great potential to transform the daily financial operations as we know them. According to the recent stats, within five to ten years from now, 95% of all interactions with clients in the finance industry will be performed by AI.
One of the AI subfields, natural language processing (NLP), turns out to be a great fit for automating numerous banking-related procedures. From data processing and analysis to regulatory compliance to instant customer service, such AI solutions can enhance business-critical decisions and increase overall productivity.
Intelligent Document Processing

Oksana Mikhalchuk
Oksana Mikhalchuk is a Technology Writer at Oxagile, a New York-based provider of next-gen software engineering solutions around IoT, AI, computer vision, biometrics, and more. Oksana creates content about state-of-art tech opportunities in healthcare, education, entertainment, and manufacturing.

Financial institutions deal with countless legal documents on a daily basis. And AI could help them revamp the process of document search and processing.
Namely, by leveraging an NLP-enabled solution, you can automatically extract key data from a database of structured and unstructured documents in a matter of seconds, and classify it according to various parameters. If you want to add more value to such processing, take advantage of optical character recognition tech. It will enable you to smoothly analyze handwritten and scanned documents, enriching the obtained insights.
By using NLP to assign metadata to every uploaded document, you can enhance document search — fueling it with broader notions, not just standalone keywords. Besides drastically cutting document processing time and increasing information accessibility, such a method will allow you to understand the complex logic behind legal regulations.
Digital Counseling

Yana Yelina
Yana Yelina is a Technology Writer at Oxagile, a provider of software engineering and IT consulting services. Her articles have been featured on KDNuggets, ITProPortal, Jaxenter, Singularity Hub, and Datafloq, to name a few. Yana is passionate about the untapped potential of technology and explores the perks it can bring businesses of every stripe. You can reach Yana at [email protected] or connect via LinkedIn or Twitter.

Researchers predict a 3,150% growth for chatbots interactions in the FinTech industry between 2019-2023, which will save financial organizations over 800+ million in working hours. Powered by NLP, such solutions can also enable seamless communication with clients and facilitate worldwide 24/7 customer service.
Here’s a case in point. To automate a wide spectrum of operations available to their clientele, Bank of America launched Erica chatbot in 2019. This virtual assistant is designed to handle client inquiries, assist with bank account management, track financial habits, and notify about security threats.
It’s obvious that chatbots are more than an animated search engine for digging up receipts. Virtual assistants can go through transaction histories and notify card holders about recurring payments or an upcoming zero balance. Chatbots can also help clients improve their financial habits by notifying them about exceeding spending limits and offering smart budget plans — sparing you the need to address repetitive client requests and saving time for tasks with a higher priority.
Streamlined Regulatory Compliance
The finance industry is so heavily regulated that it requires much time and effort from financiers to ensure strict adherence to constantly evolving standards. Mistakes result in massive fines and reputational damage, urging financial institutions to search for RegTech solutions able to automate regulatory compliance.
To wit, there are players on the market like Rabobank that successfully implemented an ingest-and-search system that helped them reduce the time their team spends on investigating regulation changes. Underpinned by NLP, this system automatically indexes heterogeneous data from various sources like documents, emails, and calls to enable a flexible, rapid compliance analysis across separate business units and jurisdictions.
Similar NLP-based solutions can also automate data-related tasks from collation to audit trails to policy-driven deletion. Once this data processing is done, your compliance specialists will just have to conduct final contract reviews and validate the decisions.
Underwriting Automation
The next big thing in the InsurTech market are NLP-based solutions that automate insurance claims processing. It can be chatbots that roll out a simple claim approval in under a minute or ask some additional questions to select the most suitable insurance option for a particular client.
For more complex tasks like creditworthiness assessment of underbanked clients, you can use NLP coupled with machine learning. Such a powerful combination will enable you to analyze clients’ digital footprints and predict their behavior. These footprints may include data from a client’s social media profiles and other personal information available upon permission like internet browsing history or geolocation. An NLP-powered system will convert a fragmented customer profile into a credit score that will help you identify lending risks and accelerate underwriting.
This way NLP tech helps lighten insurance specialists’ overload, increase service accessibility and successfully serve those clients who have no previous credit history.
Advanced Brand Reputation Management
To gain a better understanding of how the financial market reacts to events and how your company is perceived by the public, you need to analyze relevant information from online and offline sources like news and social media. But since data volumes are too large to process manually, you can implement NLP to get a detailed summary of the media coverage.
This tech will help you identify hot topics, analyze upcoming trends, and assess potential risks in the finance industry. In particular, you will be able to forecast events that could affect your business — like a currency collapse, pandemic outbreaks, or armed conflicts — and mitigate their impact on your business.
Another perk NLP can bring to the table is sentiment analysis. A machine learning-fueled engine can comb through user-generated content to measure the level of customer satisfaction and find new points for improvement. Enhanced with an intelligent search feature, such a tool could track customer data and analyze their online behavior — to predict their needs and identify cross-selling opportunities.
Summing up
To get a competitive edge in the finance industry, leverage AI-powered NLP solutions. By rapidly processing massive amounts of text and speech data related to your business, such a system will help you revamp the regulatory compliance process, provide widely accessible online consultations for your clients, accelerate data search and automate client profiling. As a result, you’ll be able to accelerate decision-making, improve customer service and increase returns on investment.
Feature image via Pixabay.
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The post The Promising Duo: Five Use Cases for Natural Language Processing in FinTech appeared first on The New Stack.

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