NLP Use Cases in Fintech

Fintech is a rapidly growing industry that combines finance and technology to offer innovative financial services to consumers and businesses. With the rise of fintech, traditional financial institutions are encountering stiff competition, necessitating that they modernize their operations to stay current with technological advancement. One technology that has been particularly beneficial in fintech is Artificial Intelligence (AI), and more specifically, Natural Language Processing (NLP). 

NLP is a branch of AI that focuses on analyzing and understanding human language. Its applications can range from simple tasks like converting spoken words into text or vice versa to more complex tasks like sentiment analysis, translation, and even deception detection. NLP employs advanced machine learning techniques like neural networks and deep learning to enable these capabilities. 

In financial services, NLP can help firms become more efficient, profitable, and customer-centric by addressing some of the industry's biggest challenges. According to a report by Allied Market Research, the global natural language processing in the BFSI market, worth $3.2 billion in 2021, is estimated to reach $20.3 billion by 2031, with a CAGR of 20.5% from 2022 to 2031. Here are the top three factors that impact the growth of NLP in the finance industry:

• Rising automation in banking processes
• The higher usage of the internet and connected devices 
• Growing demand for predictive analytics to reduce risks in the BFSI industry.

In this article, let us focus on the principal use cases of NLP in the fintech industry.  

Use Cases of NLP in the Fintech Industry  â€

Voice-based Payments â€

The global voice payments market is growing rapidly, with various companies offering voice-enabled peer-to-peer payments to their customers. Voice-based payment solutions provide users with advanced experiences where the user's voice serves to authenticate transactions and authorize payments. The voice assistant leverages NLP to comprehend and process the spoken request when initiating a voice-based payment transaction. 

The NLP system scrutinizes the user's speech patterns, identifies key phrases, and extracts important information, such as the payment amount, recipient's name, and payment method. Subsequently, the voice assistant employs this data to initiate the payment process by accessing the user's account or connecting with a payment gateway. NLP is also used to verify the transaction details with the user, ensuring the transaction is accurate and authorized. 

Peer-to-peer Lending and Borrowing

P2P lending has been increasingly recognized globally, and the US is currently at the forefront of this market. P2P lending has established a robust mechanism for lending and borrowing while rendering banking functions accessible to the masses. NLP algorithms can pair borrowers with lenders based on their profiles, requirements, and preferences. These algorithms can scrutinize borrower and lender data, such as credit scores, income, occupation, and loan history, to identify the most suitable match for a specific loan application.

NLP can streamline the loan application procedure by extracting data from application forms and validating their accuracy. Moreover, NLP can examine borrower data to assess the risk associated with the loan application and recommend suitable loan terms.

Compliance Monitoring, Risk Assessment, and Regulatory Reporting

Regtech refers to using technology to help financial companies comply with regulations. This technology has rapidly emerged as a pivotal component of data management systems, offering a potent means to industrialize the conversion of unstructured data into structured data. NLP can benefit various Regtech domains like compliance monitoring, risk assessment, and regulatory reporting.

The technology can analyze regulatory documents like compliance manuals and guidance to identify relevant sections and highlight areas of non-compliance or potential compliance risks. It can monitor communication channels like emails and instant messages for potential compliance breaches. 

One example of a company applying NLP in RegTech is ComplyAdvantage. The firm applies machine learning and natural language processing techniques to aid regulated institutions in handling their risk-related responsibilities and prevent financial misconduct. The technology minimized the firm's dependency on manual assessment procedures and outdated databases by as much as 80%.

Insurance Claims Processing Workflow

NLP algorithms can analyze unstructured data, such as claim notes, damage reports, and medical records, to extract relevant information, including dates, names, and descriptions of injuries or damages. This has the potential to automate the claims processing workflow and mitigate the need for manual processing, thereby reducing the time and effort required to process claims.

Moreover, NLP algorithms can be leveraged to automatically adjudicate claims that meet specific criteria, such as low-value claims or claims that comply with particular policy provisions. By doing so, the workload of claims adjusters can be decreased, and the claims process for policyholders can be expedited. Additionally, NLP can analyze customer feedback, social media data, and other sources to gauge customer sentiment toward the claims process.

Lemonade, an insurance start-up, demonstrates how NLP and machine learning can be used to automate claims processing and improve customer experience. The company highlights that its customers' claims process starts by conversing with AI Jim through the app, which is the first point of contact. AI Jim can handle around 40% of claims without requiring any human intervention, indicating that AI Jim is capable of taking care of insurance claims management from start to finish.

Robot-based Financial Advice and Stock Trading 

NLP can automate trading decisions based on predetermined criteria such as sentiment analysis, news analysis, or technical indicators. This can lead to more efficient trading and reduce the potential for human error.

NLP-powered chatbots can offer customized financial advice to individual investors based on their goals, risk appetite, and investment background. This can increase customer engagement and give investors a more efficient and personalized experience.

NLP can be employed to analyze portfolio data and provide insights into portfolio performance, risk exposure, and potential areas for optimization. This can assist investors in making informed decisions about their portfolios and improving their overall performance.

Cross-border Money Transfers

Fintechs have strategically targeted the international payments industry, potentially capturing significant market share from traditional banks in recent years. This is due to their more agile, responsive, and customer-focused operations. Fintech products significantly reduce the time required to receive cross-border payments, from up to 30 days with a traditional bank to less than two days.

NLP technology can automatically translate user input and transaction details into the appropriate language for the receiving country, improving communication and reducing the potential for misunderstandings or errors.

In addition, NLP can be employed to monitor transaction data and ensure compliance with international regulations, including anti-money laundering (AML) and know-your-customer (KYC) requirements.

Financial Research and Theme Extraction

Financial analysts and investment managers can leverage AI and NLP to avoid overlooking crucial insights, underperforming relative to their benchmark, and falling behind their competitors. NLP technology can sift through unstructured data and identify market-moving trends and volatility that could impact financial markets.

Alphasense, a leading provider of AI-powered business insights and market intelligence solutions, features NLP-based theme extraction to help M&A bankers discover financial metrics unavailable in standard 10-Ks and 10-Qs. When evaluating the strengths and weaknesses of competitors or potential M&A targets, comprehending the context surrounding a theme or topic can be particularly beneficial. Additionally, they can search through alternative information sources for businesses that aren't covered or are part of an emerging industry. Using watchlists, they can stay current on businesses of interest, ensuring that their due diligence is comprehensive and that they never miss a critical update.

Future Possibilities of NLP

The future possibilities of NLP in fintech are vast and exciting, with the potential to transform the industry in numerous ways. The industry will likely see even more innovative use cases emerge as the technology develops. 

NLP can enable companies to personalize their services and products to a greater extent. It will allow them to better meet their customer's needs and provide a more satisfying experience. As NLP technology develops, it may become possible to automate more complex tasks. It could reduce the need for human intervention in certain areas of fintech, making processes faster and more efficient.

The subdivision of NLP, known as conversational banking, is poised to give rise to various chatbots in the financial industry, such as banking, wealth management, and mortgage chatbots. These chatbots hold the potential to achieve end-to-end automation, resulting in a substantial reduction in customer support costs. Institutions that commit resources towards these scalable solutions are expected to operate more cost-effectively, with reduced overhead expenses.

Conclusion

The fintech industry has been revolutionized by the integration of NLP, which has significantly improved the efficiency and accuracy of financial research and customer service. NLP-based applications have also facilitated the growth of voice-based solutions, enabling banks and other financial institutions to enhance customer engagement and satisfaction. 

As an organization embarking on digital transformation, it's crucial to have a trusted partner who can help you navigate the rapidly changing landscape of the fintech industry.

Cogent Consulting offers comprehensive consulting services to help clients leverage the power of NLP and other cutting-edge technologies to transform their businesses. Visit the official website and read other informational blogs on many trending topics. 

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