The financial sector is no stranger to the rapidly-changing advancements in technology. With the rise of artificial intelligence and machine learning, the financial industry has seen an influx of new tools and techniques designed to simplify processes and improve the customer experience. One of the most promising innovations in this space is natural language generation (NLG).

NLG is used to create automated customer service responses, generate personalized financial reports, and create investment advice. The NLG technologies enable financial services companies to increase efficiency, reduce costs, and better serve customers.

According to Grand View Research, in 2018, the natural language generation market had a value of over US$ 336 million. It is predicted to have a yearly growth rate of 19.8% from 2019 to 2025.

A fascinating insight from this report is that the banking and financial services sector accounted for 22% of the market in 2018 and is likely to have the greatest share of the NLG market by 2025.

This article talks about some of the popular business applications of NLG in the finance sector. Let's first understand what NLG is and how it differs from NLP. 

What is NLG?

Natural Language Generation (NLG), a subfield of artificial intelligence (AI), uses structured data and unstructured data sources to generate natural language text that can be used in documents, reports, and other written materials.

NLG systems are used in many applications, including chatbots, summarization, automated writing, and question-answering systems. NLG systems can generate personalized content, such as news articles or product descriptions, or create interactive conversations with customers.

The global NLG market size was valued at US$ 567.7 million in 2021 and is projected to reach US$ 2574.6 million by 2029, growing at a CAGR of 20.8% from 2022 to 2029, as per the latest market reports

How is NLG Different from NLP?

Natural language processing (NLP) and NLG are two rapidly growing fields in artificial intelligence. While both technologies involve the analysis of natural language, there are some important differences between them.

NLP analyzes and interprets natural language, such as spoken or written language, to derive meaning and extract useful information. It is often used to extract insights from large volumes of text, such as customer feedback, to understand customer sentiment better or to uncover product trends.

NLG, on the other hand, is the process of automatically creating natural language text from structured data and unstructured data. It generates more natural-sounding reports, summaries, and other documents and creates more natural-sounding conversations.

The key difference between NLP and NLG is that NLP analyzes natural language and extracts meaning from it, while NLG generates natural language. NLP involves understanding the context and meaning of a given text, while NLG involves generating a text that is both accurate and natural-sounding.

How does the Financial Sector use NLG? 

The financial sector is increasingly turning to NLG to simplify and automate complex processes. For instance, financial institutions are increasingly using NLG to automate report generation and provide automated customer support. 

Banks are increasingly using NLG to generate customer profiles based on their financial data and then use this data to provide customers with tailored advice. As NLG can generate customer insights, this technology can identify customer needs and create personalized experiences, such as providing customers with customized offers and promotions.

This technology can be used to advise customers on how to manage their finances best, as well as provide them with personalized investment recommendations. 

Here are the top business applications of NLG in finance:

Improved conversion rates

NLG can improve conversion rates in finance by providing customers with personalized, tailored content to encourage them to take action. For instance, NLG can generate customer-specific emails highlighting the benefits of certain financial products, such as investments, mortgages, and loans. 

NLG can also generate tailored ads that target customers based on their past financial activities, providing them with more relevant and timely content. By leveraging NLG, financial institutions can provide customers with more personalized content, which can help to increase conversion rates.

The most commonly used algorithm for NLG to increase conversion rates is a deep learning-based algorithm called sequence-to-sequence (seq2seq) modeling. This algorithm uses recurrent neural networks (RNNs) and long short-term memory (LSTM) networks to generate conversational texts and emails. 

Real-time market insights

NLG allows financial analysts and traders to quickly and accurately generate insights from large amounts of data. Using NLG, analysts can quickly identify emerging trends, opportunities, and risks. NLG can also be used to create automated reports that provide timely and accurate updates on market conditions. 

Various algorithms can be used to collect market insights, such as sentiment analysis, topic modeling, and phrase extraction. For instance, sentiment analysis measures customer sentiment towards a product or service and can provide useful market insights. 

Topic modeling can be used to discover topics of interest from customer feedback and reviews, providing insights into customer preferences. Phrase extraction can extract important phrases from customer reviews and feedback, providing further insights into customer feelings and opinions.

Strategic decision making

With NLG, a finance team can quickly generate reports that summarize key financial information and clearly communicate it to stakeholders. The reports can be tailored to the specific needs of the stakeholders, allowing them to make informed decisions faster. This can be especially helpful when dealing with complex financial data and making decisions involving a high degree of risk. 

A commonly used algorithm in NLG for decision-making in finance is the Monte Carlo Simulation algorithm. It uses random sampling to simulate a financial system's behavior over time, allowing decisions to be made based on the probability of different outcomes. This algorithm can be used to test different scenarios and optimize portfolios, allowing for more informed decisions to be made.

Automated alerts and notifications

NLG can generate alerts or notifications, such as text-based notifications (email or SMS notifications). Automated alerts and notifications can be created based on financial data, such as stock prices, market trends, and other analytics. This would enable companies to quickly and accurately deliver detailed updates to their customers, investors, and other stakeholders. 

Depending on the specific application and the alerts or notifications generated, different algorithms may be used. BERT is the most common algorithms for alert generation in financial sector.

Detect anomalies or potential fraud

NLG can detect anomalies or potential financial fraud by analyzing and summarizing large datasets to identify patterns, relationships, or outliers that may indicate fraudulent activity.

It can also generate reports that provide an overview of the current state of financial transactions, including any suspicious activity. This can help financial institutions quickly identify potential fraud and take action.

An NLG algorithm that can detect anomalies or potential fraud in finance is the Deep Learning Anomaly Detection (DLAD) algorithm. This algorithm uses deep learning to detect anomalies and potential fraud in financial transactions by learning patterns in the dataset and using the learned patterns to detect anomalous behavior.

The DLAD algorithm has been used in several financial applications, including the detection of credit card fraud and the detection of fraudulent transactions.

The Next Step

Companies should explore the potential use cases of NLG in their financial operations. This can involve researching the available tools and technologies, understanding the various use cases, and evaluating the risks and rewards associated with implementing NLG. 

Additionally, it is important to consider the ethical implications of using NLG in financial operations, such as data privacy, fairness, and transparency issues. After understanding the potential and ethics of NLG in finance, the next step is to develop an implementation plan.

This will involve identifying the data sources, desired outputs, and the processes and technologies needed to achieve them. Finally, the plan should be evaluated and tested before it is implemented in a production environment.

Conclusion

The future of NLG in finance is promising. Technology will become more prominent in financial services as it becomes more sophisticated. Ultimately, NLG is likely to become an integral part of the financial services industry, providing more efficient and cost-effective solutions for financial organizations.

Cogent is a tech consulting company that leverages high-grade technology to solve complex problems for its clients. For more information on NLG, please visit the Cogent inCights page.

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