2020-10-14 00:00:00
10 min read
As we move into a more digitally-driven world, the use of data plays a major role in navigating businesses across sectors. Companies are adapting to emerging technologies such as Artificial Intelligence, RPA, Machine Learning, Big Data Analytics, etc. to transform their processes and keep up with the changing trends.
Data is the lifeblood of every business- whether small, mid, or large, and irrespective of the industry it operates in. But considering the explosion of data that needs to be interpreted and analyzed, enterprises are in a need to invest more in innovative solutions and services that help them make better data-driven business decisions.
The finance sector is one of the frontrunners in the digital marathon. Financial players have adopted AI technology to enhance processes such as fraud detection and management, risk assessment, trading, financial advisory services, and finance management. What started with the internet and mobile tech integration has now reached the core modernization and intelligent automation stage of digitalization for the financial sector.
Many companies have started implementing intelligent solutions such as Process Automation, Advanced Analytics, Robo Advisors, etc. and a lot more is yet to come as technologies are democratized and put to use. As per a study by Econsultancy and Adobe, the finance department is using AI as an automation and analytics tool to differentiate themselves from the competition. 43% of the respondents said the main use case of AI is for analysis of data.
Natural Language Generation is one such AI technology being adopted by businesses to make sense of their data and derive clear insights and information from it. In this blog, we will explain the NLG technology in detail with its role in finance.
Firstly, what is NLG?
Natural Language Generation (NLG) is a subset of AI that produces meaningful insights from data in the form of natural language. It has the ability to interpret, analyze, and summarize complex data into easy-to-understand content, in a human-like manner.
NLG makes working with data easy by communicating insights at extraordinary levels and accuracy. This leads to an automated analysis of business data and increases the overall productivity of the employees, thereby letting them focus on more high-value tasks.
How is NLG benefitting Finance?
The finance sector deals with vast amounts of data regularly. With every area of the industry having to analyze data and generate reports out of them, Natural Language Generation in finance automates time-consuming and repetitive workflows and increases the speed and quality of analytics and reporting.
NLG generates narratives that can be used by data analysts, CIOs, and compliance teams to gain competitive advantage and devote their time to other important tasks that affect the company’s bottom line.
Let us consider an example to understand the role of NLG in finance better.
While analytics tools generate reports in the form of bar graphs, charts, and tables from the large chunks of financial data, NLG in finance enables businesses to dig deeper into the data and generate detailed insights. For example, NLG can give you answers to specific queries such as:
- What are the risks associated with the business in this financial year?
- Which market segment is expected to bring more profit in the future?
- What factors are going to affect the future of the business?
- Are the investments made in-line with the growth path of the business?
- Which were the top 3 contributing factors in business growth in the past quarter?
A Business Intelligence tool that operates on Machine Learning and Natural Language Generation can conduct an in-depth analysis of data and present detailed summaries of insights in plain language. The answers to these above questions can be generated in the form of narratives that can be easily skimmed through to draw conclusions and make business decisions.
Popular use cases of NLG in Finance
NLG is being widely adopted by finance sectors for the following key use cases.
Strategic Decision Making
The division generates real-time data in the form of live feeds, and companies spend millions of dollars to leverage these live data feeds and draw insights out of it. Natural Language Generation helps these companies to make the most out of the live data feeds by explaining them in simple language, which is saved to the company’s dashboards or internal tools in the form of reports. These reports can be used by analysts and internal users to make informed decisions in real-time.
Report Generation
NLG enables businesses to create personalized reports for each customer by using data storytelling. The reports generated using Natural Language Generation gives detailed information such as the performance of certain areas of the business, the forces that led the business to its current state, how it impacts the overall business goals, what it means to the employees, customers, and other stakeholders, and what can be the next best steps.
BI and analytics tools allow businesses to automatically generate these detailed insights in just a few clicks. This saves a lot of time and effort of teams working on generating reports by drastically cutting down the report generation time.
Prevention of Frauds
The finance sector is most prone to attacks and data leaks. Financial criminals constantly upgrade and adopt technologies to carry out more sophisticated attacks. It is therefore important for companies to invest in fraud detection and prevention methods. Natural Language Generation in finance plays a remarkable role by highlighting potential anomalies and disparities in the data and writing it in natural language.
This helps internal compliance teams to better understand complex data sets and prevent fraud and money laundering. It further analyzes confidential data easily by effectively extracting data from various intermediaries and explaining it in the form of narratives.
Combining NLG with other tools
NLG and Robotic Process Automation
By pairing NLG with Robotic Process Automation, it is possible to improve the accuracy and speed of data and reduce the efforts and time in discovering the details and insights from data.
NLG and Predictive Analytics
Predictive analytics tools enable businesses to foresee future performance and business opportunities. When combined with NLG, it enables financial businesses to draw insights about future events and take the desired actions.
NLG and Business Intelligence
One of the most widely used applications of NLG is converting business insights into stories. Integrating NLG into BI tools and visualization platforms can help financial players by explaining the charts and numbers in the form of stories, and give them a clear picture of the insights.
Is your firm ready for NLG?
If your organization deals with structural data and spends significant time in producing any sorts of reports, then you might want to consider NLG. Here’s how to go about adopting NLG for your organization's finance division.
- To explore opportunities, begin with your business needs and specific business problems that you are facing, and determine how detailed narratives from data can support your business goals.
- Next, invest in a good BI-tool with NLG, that can be configured to support the objectives and processes of your business.
- Once set up, the tool can be trained to apply intelligence along the way and automatically understand and communicate relevant good insights.
- Self-service BI tools help employees with very little exposure to data analysis use the tool to generate insights. It can help data analysts, power users as well as business users to get detailed insights at the click of a button.
- NLG can further be integrated with visualization tools and cognitive tools to get better insights and help everyone across the organization to utilize the insights more efficiently.
Redefine your Financial data with Phrazor
Basic NLG, which simply converts numbers into text has evolved into user intent-driven advanced NLG, which provides insightful and relevant information in a conversational language. Phrazor, a self-service Business Intelligence platform, uses advanced Natural Language Generation to turn complex data into easy-to-understand reports with narratives accompanying them. It’s a scalable and user-friendly product that derives deep insights from data in multiple languages.
Phrazor uses intelligent automation to regulate day-to-day labor-intensive processes and helps financial departments to save a lot of time and efforts. It analyzes data and gives a consistent view of the performance of the various data points using Natural Language Generation.
Major companies like Barclays, Fidelity International, Dun & Bradstreet, Wipro, etc. use Phrazor to:
- carry out financial performance reporting,
- make data-driven decisions using business intelligence,
- improve customer engagement by generating personalized reports, and
- analyze complex financial data to highlight potential risks and get meaningful insights.
With Phrazor, companies can scale their financial reporting with personalized insights and commentaries. Whether it is for managers, analysts, C-level executives, planning and analysis leaders, or executives, the reports generated using NLG can be tailored to the needs of specific users.
It completes thousands of hours of manual work in just minutes and improves the TAT for financial reporting. It also eliminates the risk of errors involved in manual writing, thereby increasing the accuracy, consistency, and overall operational efficiency of your organization.
In a nutshell, adopting NLG in finance can improve the quality of reporting, generate personalized insights and reports, enable data standardization, pinpoint errors and issues in the financial data, and save a lot of time and costs.
If you are yet to leverage NLG for your business, now is the right time to start. Get in touch with us to know more about the technology and our tool, and how it can benefit your business.
About Phrazor
Phrazor empowers business users to effortlessly access their data and derive insights in language via no-code querying
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