3 most important NLP methods for FinTech

The 3 NLP Approaches for FinTech Use Cases

Fintech, or financial technology, is a growing implementation across the financial industry that represents the intersection between technology solutions and financial use cases.

Originally, it referred to technology used on the back end of the established consumer and business financial institution, categorized across the core banking systems, ATMs and credit card payment processors. Despite, especially in recent years, fintech has come to describe a much wider range of innovative products and services, such as mobile payments (e.g. Apple Pay), robo-advice or digital investment platforms (e.g. Wealthfront), peer-to-peer lending (eg Lending Club), digital currencies (eg Bitcoin) and others yet to be discovered, defined or developed.

— Robinhood: a stock trading platform that also offers cryptocurrency trading;

— Acorns: an app that rounds up spare cash from daily purchases and invests it in a diversified portfolio of ETFs; and

— Betterment: an online investment platform that provides personalized advice and automated portfolio management solutions.

— Mobile payment apps. One area of ​​focus is to simplify how you send money to friends and family, pay bills and make purchases, while reducing your reliance on credit cards, cash and other forms of payment.

— Another representative illustration is online personal finance tools, which include Mint or Personal Capital. These websites help people manage their finances by tracking expenses or creating budgets.

— Robo-advisors are an emerging field, mainly over the last few years as they have become popular. Robo-advisors use algorithms to automatically orient clients’ funds according to their goals and risk tolerance levels.

Since the deployment of BERT and the rise of large language models (LLM), organizations are now considering use cases for experimentation and enablement. At the intersection of fintech, some implementations across NLP model pipelines may include the following:

1. Predictive Modeling: NLP can analyze customer sentiment to better understand how users feel about a fintech product or service. A typical situation might be to assess whether feedback from users is positive or negative, information that can potentially inform how to further adjust marketing or targeted information sharing (with new or existing users).

2. Automated sharing of financial information: Using NLP, fintech organizations can offer automated financial insights by understanding user intent and providing personalized recommendations. A representative case could be for a chatbot, which can help users choose a financial product based on their goals and risk tolerance.

3. Fraud detection: NLP can be used to detect fraud in financial transactions by identifying suspicious behavior patterns. Namely, NLP can identify irregularities in loan applications or suspect transaction activity on credit card accounts.

NLP implementations can include use cases for predicting which users are likely to default on a loan or credit card payment. In addition, financial institutions can use this information to target marketing and fundraising efforts and know the technical features (functional engineering) to inform the prices and terms of loans and other products.

Predictive modeling approach that considers multiple unique features (eg demographics, search engine queries) when building models rather than relying on historical transaction data alone to inform predictions across future user activities.

NLP can help develop other predictive models for financial data to identify trends and make predictions about future market movements. Particularly in the context of LLM, insights, such as chatter collected in conversations from tools or websites, can be adapted to learn or fine-tune NLP models, and ultimately provide the ability to receive summaries or descriptions of trends (from those conversations). Furthermore, the LLM is currently distributed, available to organizations and private users.

NLP can analyze customer reviews, social media posts and other unstructured data sources to identify trends in the wider economy that may affect demand for financial products and services. As an example, if NLP reveals that more consumers are worried about job security, this could lead to banks offering more attractive interest rates on savings accounts to inform users of the opportunities that currently exist (such as, based on the current climate, in the week, and so further) to save money.

NLP-based chatbots or virtual assistants can provide 24/7 assistance with basic tasks, such as checking balances, transferring money between accounts and answering common questions about products and services. The opportunity is to give the teams extra time to focus on other tasks while providing high-quality customer service to the user.

Because NLP could collect, understand and summarize insights based on how the user interacts with the tools deployed by organizations, the opportunity for use case enablement can be to explicitly build solutions that are user-centric based on the insights the user thinks the organization should collect (about their needs and wishes).

NLP can contribute to quantitative modeling for banks and other financial institutions by automatically analyzing customer sentiment from call center discussions and surveys to understand the moments or issues that matter to users. The analysis from these insights can be applied across product development and organizational strategy decision-making, and even more specifically, cover marketing cases or improve the overall user experience.

Especially with the capabilities now possible with LLM, NLP can gather insights about unusually large or frequent transactions and potentially fraudulent activities across many illegal or illegal actions performed by end users. For example, in one case, NLP can detect fraudulent activity in financial transactions, helping to prevent losses for businesses and consumers.

Syntactic analysis [1] be able to detect fraud by looking for unusual patterns in the text that may indicate fraudulent activity. Specifically, syntactic analysis can be used to identify emails that are likely to be spoofed or phishing attempts. In terms of semantic analysis [2], it can detect fraud by identifying words and phrases that may be associated with fraudulent activity. Based on semantic analysis, users’ insights shared to review financial products can be analyzed to contain language that indicates the reviewer has never used the product.