The accelerated pace of digitization motivates the finance industry to bet big on natural language processing (NLP) to improve financial performance. An enormous increase in textual data that has become readily available in recent times has made NLP a crucial and strategic tool for financial analysis. Analysts spend considerable time and resources analyzing research reports, financial stats, company filings, and other related data obtained from print media and other sources.
The amount of financial data generated from various sources is accelerating at an unprecedented rate, resulting in vast unstructured data lakes, which often remain untapped for making critical business decisions. Harnessing such unstructured data can provide insights that result in valuable strategic decision-making. The unprecedented growth in the volume of data is making it difficult and at times impossible to spot the nuances of valuable key data insights that could lead to highly informed decisions. NLP can parse through this data offering opportunities to uncover unique and meaningful insights.
NLP is an interdisciplinary area that resulted from the groundwork of making computers and machines understand and use natural language derived from various fields, including linguistics, neuroscience, mathematics, and computer science. With the advent of machine learning and NLP, systems are becoming more scalable and reliable with increased computational capabilities. According to MarketWatch, the global market size of NLP is expected to reach USD 3748.9 million by 2027 (from USD 1020.4 million in 2020) with a CAGR of 20.1%.
NLP has transformed into a language-agnostic technique enabling near-perfect machine translation algorithms across platforms. The finance industry is now reaping the benefits from NLP including
Efficiency: NLP automates data (unstructured content) analysis to gain meaningful insights in real-time.
Consistency: NLP models deliver more consistent results than human analysts, who interpret and decipher the text.
Accuracy: NLP analyzes unstructured data with greater accuracy.
Speed: Unstructured data keeps changing swiftly, resulting in the decline of the value of such data. NLP processes enormous volumes of data within seconds.
Automation: NLP technology automates the entire process of scanning and extracting actionable insights from financial data.
NLP in finance – Use cases and applications
Data is driving all industries, including finance. The most critical piece of data can be found in analyst reports, financial press, forums, texts, transcripts, websites, etc. that financial professionals spend a considerable amount of time analyzing. Automated textual data processing decreases the amount of time invested in manual analysis significantly. The FinTech industry has always been an early adopter of new and promising technologies. Hence, AI, machine learning, and NLP have great potential to transform daily financial operations.
Credit risk evaluation and credit scoring
NLP technology uses multiple data points to assess credit risk by measuring those applying for loans’ attitudes and business mindsets. Evaluating the creditworthiness of the borrowers and understanding the risk profile is a challenge that the BFSI industry often faces. Credit risk models are fed with information gathered from personal loan documents for further analysis. NLP and machine learning algorithms help derive relevant data points extracted from such loan agreements facilitating the assessment of risks and creditworthiness of the borrowers.
It should be noted that financial sentiment analysis is different from routine sentiment analysis in both domain and purpose. The financial sentiment analysis powered by NLP deals with how the markets would react to news and if stock prices will rise or fall. Whereas, regular sentiment analysis finds whether the information given is inherently positive or negative. NLP helps identify potential signals of equity performance from various sources like research reports, social media, company transcripts, and many more by identifying the outlook changes over time.
Accounting and auditing
Many financial services organizations are now focused on providing actionable audits of their clients’ annual performances with efficient NLP applications. It helps to document reviews and long-term procurement agreements, especially with government data. NLP also helps identify potential risks and fraud, like money laundering.
Data science, machine learning, NLP, and non-parametric statistics help predict the investment strategies in financial stock markets. The historical data collected can be used to predict the beginning of the trade period and a portfolio. NLP helps in maximizing the possible growth rates when environmental factors are uncertain. It helps in portfolio selection by filtering out desirable and undesirable stocks.
Unstructured data utilization and labeling
The financial services industry is flooded with various types of data from different sources stored in other formats PDF, XML, HTML, web, feed, etc., – the analysis of which can be extremely time-consuming and tedious. The NLP models glean, analyze, and extract valuable information from such data to infer meaningful information that could influence financial markets. Labeling of unstructured data makes searching digital documents easier for compliance and evaluating for standards and regulatory compliance.
Content enrichment provides the completion and enrichment of enterprise data with extensive contextual information to obtain accurate, actionable insights. Content intelligence systems leverage NLP to boost content strategies by providing data-driven analytics of the content. NLP identifies and delivers personalized customer experiences via targeted client-specific content.
Omnichannel customer engagement and service
NLP enables information filtering, allowing financial institutions and analysts to obtain the most relevant timely information. Major financial institutions deploy NLP-powered chatbots as a powerful customer engagement strategy to understand client intentions. Apart from that, NLP also sifts through social media to find meaningful content that might help improve products and services. NLP is often considered a suitable technology for driving better results from customer service and support.
Intelligent document processing
The BFSI industry deals with numerous legal documents daily that need to be categorically processed. AI and NLP can help revamp the process of document search and processing. NLP-enabled solutions extract critical data automatically from various databases of structured and unstructured documents within seconds and classify them according to specified parameters. NLP drastically reduces the document processing time and increases information accessibility to help better understand regulations and compliance.
Leveraging AI-powered NLP provides a tremendous competitive advantage. It rapidly processes massive amounts of structured and unstructured data, freeing time for analysts to focus on the most relevant data to make the most informed decisions.
If you want to power up your financial products and services with NLP, send us a query to email@example.com. Intellect Data, Inc. is a software solutions company incorporating data science and artificial intelligence into modern digital products with Intellect2TM. IntellectDataTM develops and implements software, software components, and software as a service (SaaS) for enterprise, desktop, web, mobile, cloud, IoT, wearables, and AR/VR environments. Locate us on the web at www.intellectdata.com.