Businesses across sectors are moving towards building a data-driven enterprise, where all the decisions across the company are made based on data, and not necessarily by the gut-feeling of their teams. They are on a mission to collect data that is valuable and use it effectively to automate processes and save time.
The financial services industry generates huge amounts of data. The value of this data is highly dependent on how it is gathered, interpreted, analyzed and stored. Financial organizations have started adopting emerging technologies and solutions to leverage the capabilities of data and drive new business opportunities.
Technologies such as RPA, AI, Machine Learning and Big Data Analytics help in monitoring large chunks of data generated, uncover patterns and establish predictions out of them. These technologies utilize data to increase operational efficiency, enhance security and deliver better services.
Natural Language Generation (NLG) is one such AI technology being used to interpret data and derive useful insights and understanding from it. NLG interprets structured data and turns it into written or spoken language, just the way a human would. It can study the input data and generate summaries and insights out of it in the form of narratives, explanations and suggestions. It enables data storytelling in plain language, that makes the data penetrable for everyone in the organization.
NLG for Financial Services Firms
Financial institutions have to deal with lots of structured as well as unstructured data. While structured data provide organizations with key decision-making insights, unstructured data offer remarkable analytical opportunities.
Natural Language Processing (NLP) is an AI technology that converts textual data into predictions and classifications in the form of numbers. Natural Language Generation (NLG) is a part of NLP that turns structured data into written or spoken language. Both these technologies when combined have the capabilities to analyze the collected data, interact with this data and extract the underlying meaning and insights to describe it in the form of written narratives.
With every area of financial services having to analyze and report some sort of data, NLG can be put to work to automate repetitive, time-consuming workflows and increase the quality, speed and consistency of analytics and reporting. The narratives generated by NLG can be used by the CIOs, data analysts, portfolio managers and compliance teams to gain an advantage over their competitors. This way, the data analysts and executives can devote their time on other value-added tasks that affect the firm’s bottom line.
As per a recent research, the global natural language generation market was valued at over USD 336 million in 2018 and is expected to rise at an annual rate of 19.8% from the year 2019 to 2025. Out of the total market share, the financial services, insurance and banking sectors accounted for nearly 22% of the market in 2018. The report also states that, by 2025, the banking and financial services segments are expected to dominate in terms of the overall NLG market share.
Use Cases of NLG in Financial Services
Deploying NLG solution can deliver short and long-term benefits to financial institutions and enable them to:
Generate Real-time, Strategic Data-driven Decisions
The financial services industry constantly generates data in the form of live data feeds. Companies spend millions of dollars to analyze the data feeds and gauge insights from them. NLG helps financial firms to explain the analyzed data in simple language, which is saved in the form of written reports into the company’s internal tools or dashboards. The internal users of the firm such as traders, analysts and fund managers can use the information generated to make informed decisions in real-time.
Cut-down Time on Report Generation
NLG enables data storytelling, that allows businesses to create personalized reports for each customer. It automates the generation of information such as the forces that led the firm to its current position, what it means for the stakeholders concerned, and what the next course of action must be. For example, it can be used to generate automated customized insights on the portfolio performance of individuals. This saves a lot of time for teams working hours on producing monthly insights and reports. It cuts down the report generation time from days to seconds.
Prevent Data-driven Frauds
With financial criminals coming up with advanced technologies to carry out more sophisticated attacks, it has become crucial for financial services companies to take measures to prevent money laundering and fraud. Natural language generation tools, with the help of AI technology, can help compliance teams to better understand large and complex data by highlighting alerts and potential disparities and anomalies in the form of human language. It can thus help them analyze confidential data and information extracted from various intermediaries in an efficient manner.
Apart from these, NLG has also proven to help manage the profit and loss calculations, liquidity reporting, cash flow, balance sheets, KYC, case management and much more for financial firms.
Key Takeaways
The adoption of NLG is on a rise in the financial services industry mainly because of the nature of data that it deals with. Transitioning to NLG can benefit financial services players by improving the quality of reporting, saving cost and time, generating personalized and engaging analysis, and enabling data standardisation.
There is a huge opportunity to automate recurring processes while matching the level of sophistication of data reporting and analysis. The narratives generated by NLG tools also have the ability to pinpoint errors and identify issues even before they occur, thereby saving a lot of time, effort and money for the finance players.
All these factors have spurred the adoption of NLG in the financial services sector. However, for enterprises that have not leveraged the potential of this technology yet, it is high time to start using it now to amplify data analytics and reporting.
About the Author
Neerav Parekh is the founder and CEO of vPhrase. vPhrase helps enterprises make better sense of their data by explaining the insights in natural language using their self-service Business Intelligence and Natural Language Generation tool Phrazor. It uses Augmented Analytics and data-driven storytelling to help enterprises make better-informed decisions.
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In a world filled with data, it is critical for financial institutions to ensure those that need. And much of this growth is likely to be driven by financial services. The financial services industry constantly generates data in the form of live data feeds.