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October 6, 2023

3 Ways NLP Wins Customers for Fintech

October 6, 2023
Read 8 min

Recent advances in Artificial Intelligence (AI), specifically in its subdomains – Machine Learning (ML) and Natural Language Processing (NLP), are bringing us closer to a point where we won’t be able to distinguish between human speech and machine speech in terms of interpretation and reproduction.

And we believe that FinTech will provide us with some of the best examples in this regard. The financial services industry has always been quick to adopt new technologies.

Thinking about leveraging natural language processing services? Here are some ideas on how to make use of mainstream NLP software with a proven ROI today, what will become popular tomorrow – and how to leverage next-gen tools earlier than your competitors.

natural language processing services

NLP’s impact on fintech: overview

What do customers expect today from their banks, insurance companies, and credit unions? They expect real-time transactions, supervised management of their assets, and the ability to resolve any issues online.

To achieve this, financial services must be equipped with state-of-the-art technologies that showcase speed, intelligence, and autonomy.

AI, which turns machines into human-like entities, enables them to perform tasks that people do, but in a better and faster way. This is achieved through a combination of tools and technological solutions, primarily powered by its major subdomains: machine learning and natural language processing.

Machine Learning trains systems to learn from “experience”, which is incoming data, and enables them to make data-driven decisions. NLP is trained in the same manner as other systems, but with a specific objective: it must enable machines to interpret human speech, both when spoken (Automated Speech) and when typed (Automated Text Writing).

Natural language processing in finance (like in any other industry), has 2 major use cases:

  • Comprehending human speech and extracting its meaning and recognizing intent & coming up with a relevant reaction (request for help, passing a claim, etc.).
  • Turning unstructured data in databases and documents into structured data and extracting actionable insights through pattern recognition (text mining).

Natural language processing in finance: 3 use cases

At Itexus, we can highlight a few use cases where AI and NLP are influencing the FinTech world:

  • Turning chatbots into virtual assistants and counselors
  • Enriching them with advanced Big Data analytics
  • Making communication with them indistinguishable from human communication
  • Using NLP for fraud detection
  • Segmenting customers into groups & improving relevant product offerings
  • Reducing administrative work & automating separate tasks and whole domains

Below, we will explore in detail the areas of fintech where NLP strategies can be applied.

Customer service

“Conversational banking” is a new phenomenon that represents a significant departure from basic chatbots to more advanced digital assistants. Natural language processing companies play a crucial role in providing the functionality for these assistants by translating user queries into actionable information for generating appropriate responses.

What your competitors use today: A 24/7 available chatbot that simplifies communication between a bank and its clients. It provides script-based assistance for minor issues and quickly resolves simple complaints.

How to differentiate your business: Invest in virtual assistants with advanced capabilities that can process context, analyze text sentiment, and perform predictive analysis.

  • Counseling consumers on bank account management
  • Sending an alert when approaching the spending limit
  • Flagging payments in case of anomaly detection.

These features are characteristic of the “Erica” bot, and its success has been incredible. The AI-powered virtual assistant helped the Bank of America attract more than 1 million new users within less than 2 months after the bot rollout.

Another emerging trend to watch out for is voiceprint investigation and voice biometrics. These technologies are used to authenticate a user, facilitate transactions, and prevent fraudulent activities.

What’s next: The evolution of machine learning algorithms, especially deep neural networks, will soon enable the creation of virtual assistants that are capable of:

  • Keeping semantically consistent communication
  • Building a persona-based neural conversation model
  • Diverse reactions in dialogue with a client.

InsurTech

Advanced digital agents and NLP-based customer service are also becoming prominent in the global insurance market.

What your competitors use today: A chatbot based on predefined rules for selecting a risk profile. It is capable of:

  • Automatic selection of insurance products
  • Underwriting automation: a user files an online application for an insurance claim, and receives a decision and an accompanying interest rate.
  • Submitting claims, by answering standard follow-up questions.

How to differentiate your business: When you decide to integrate a chatbot and seek assistance from a fintech software development company, consider adding advanced functionality such as:

  • Simple claim approval. It took an AI chatbot, developed by New York-based insurance start-up called Lemonade, 3 seconds to settle a simple insurance claim. As mentioned by Daniel Schreiber, startup CEO, such chatbots allow to cut down on costs dramatically, otherwise “11-13% of premiums are consumed by the bureaucracy of handling claims”.
  • Anti-fraud algorithms. In this scenario, a chatbot utilizes a fraud detection algorithm to assess the claim details before approving the claim settlement. For instance, it can identify any personal connections between individuals associated with the claim and raise a flag for additional scrutiny, if required.

What’s next: Just like in customer service, a chatbot in insurtech is evolving into a virtual assistant that can perform the following tasks:

  • Personalized risk profile & scoring
  • Real-time processing of complex claims & calculations
  • Secure personal information retrieval.

Any questions? Drop us a line.

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RegTech

Regtech is a growing segment of fintech that utilizes new technologies to simplify compliance with regulatory requirements.

The financial services industry is highly regulated, requiring financial institutions to invest thousands of hours in mundane tasks to ensure compliance with evolving standards. Failure to do so can result in significant fines and damage to the company’s reputation.

It is not surprising that the demand for new technologies in this sector is growing, with NLP being at the top of the list. Approximately 11% of institutions working in Financial Risk, FCRM, and GRC incorporate NLP as a core component in their applications.

There are already some positive examples in the market. For instance, Rabobank, a Dutch bank, and its Compliance team implemented an ingest-and-search platform. This platform automatically indexes and makes structured and unstructured data searchable. The outcome? Compliance controls reduced from 15 to 3 minutes.

What your competitors use today: NLP and AI solutions streamline the examination of new regulation documents. They highlight the required obligations, validate front office decisions in real-time, ensure BSA/AML compliance, and adhere to a growing number of industry standards, such as MiFID II/MiFIR/EMIR.

How to differentiate your business: The next generation of AI tools with integrated NLP features offers the following capabilities:

  • Contract review. It took JP Morgan’s program named COIN (Contract + Investigation) some seconds to perform a full-scale documents review, which was taking 360,000 hours of routine work – sounds quite appealing, doesn’t it?
  • Regulatory investigations. To detect potential anti-money laundering (AML) and combat the financing of terrorism (CFT) violations, advanced AI-driven data analytics tools (NLP/ML) are needed. These tools can help detect networks of related transactions and identify abnormal behavior.

What’s next: Regtech is rapidly evolving, with no signs of slowing down. So, what does this mean for developers?

  • Working on cross-institutional and cross-jurisdictional analysis. Soon, RegTech is expected to grow from a minor segment of the financial services market into a separate domain. It will evolve into an information framework, encompassing contextualized obligations, precise definitions, and clear data requirements. AI, particularly NLP, will drive this process. Therefore, it is crucial to prepare for the future of RegTech now. This involves implementing due diligence solutions, robust case management functionality, automated regulatory reporting, and the ability to share information across multiple channels.

Of course, this is not an exhaustive list of NLP use cases applied to the fintech industry. Trading, crowdfunding, and P2P financing are just a few areas that can benefit from Natural Language Processing. A software development company with specialized expertise in financial services, such as Itexus, can guide you towards effective solutions, whether you are a daring startup or an established banking institution.

Challenges of introducing NLP in finance

When attempting to leverage NLP for business transformation, you have to prepare for challenges that may arise. These include issues with data quality, privacy, and security.

  • Data reliability. One of the main challenges in using NLP product is to ensure that the data is of good quality. Best NLP algorithms rely on well-organized and accurate data to get the best results. So, it is important to have systems in place to make sure the data is clean and valid.
  • Privacy. When dealing with sensitive financial data (which is implied by the very concept of fintech), it is of utmost importance to implement robust privacy measures. This includes ensuring compliance with data protection regulations and implementing encryption and access control mechanisms to safeguard user information.
  • Transparency. Trust between the business and its customers is crucial in the finance industry. To build it, you should prioritize transparency and explainability in your NLP solutions with such techniques as feature importance analysis and model visualization.

Summary

Investing in NLP for finance can bring multiple benefits to your business, including workflow automation, smart research and document analysis, more efficient fraud prevention and credit scoring, and more. To make the most out of this investment, you need a reliable tech partner – such as Itexus. For over a decade, we have been assisting financial services companies in transforming their boldest ideas into reality. We are ready to contribute our knowledge and technical proficiency to help you realize your goals. Contact us to learn how your business can benefit from NLP and how we can assist you in harnessing its capabilities.

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