5 Natural Language Processing (NLP) Applications In Finance

Understanding NLP

The groundwork on how to make computers understand and use natural language derives from various fields including linguistics, neuroscience, mathematics and computer science, and results in an interdisciplinary area called NLP.  NLP is a subfield of artificial intelligence (AI) and with the advent of machine learning (ML) algorithms and increased computational abilities, NLP has become much more scalable and reliable.

As stated in TechCrunch, in the last three years, NLP has made more progress than any other subfield of AI. NLP-based applications are everywhere, starting from home assistants like Amazon Echo or Alexa, to chatbots and so on. The catalyst of the NLP revolution has been the open, attainable datasets, as opposed to the limited datasets available only to a few organizations.

In addition, the viability of NLP models has broadened to many languages, apart from English, enabling near-to-perfect machine translation algorithms on different platforms. NLP tools, techniques and APIs (Application Programming Interfaces) are now all-pervading different industries, including finance. There has never been a better time to build NLP solutions for finance, then now.

→ How can businesses leverage NLP? What are the main areas of natural language processing applications? Having first-hand experience in utilizing NLP for the healthcare field, Avenga can share its insight on the topic.

Nowadays, data is driving finance and the most weighty piece of data can be found in written form in documents, texts, websites, forums, and so on. Finance professionals spend a considerable amount of time reading the analyst reports, financial press, etc. The automatic textual data processing can significantly decrease the amount of manual routine work and accelerate the trades.

NLP techniques and algorithms help to translate the raw textual data into meaningful insights across several areas in finance. Traders, portfolio managers, analysts, banks and other financial organizations strive to improve their financial analysis, and NLP and ML have become the technologies of choice. NLP is used across the financial industry, from retail banking to hedge fund investing. Such NLP techniques as sentiment analysis, question-answering (chatbots), document classification and topic clustering are used to work with unstructured financial data.

NLP and ML techniques can be used to design a financial infrastructure that can make informed decisions on a real-time basis. NLP can help with designing such systems that can enrich financial flows by tracking a company’s changing nature. For example, NLP can improve the operation of a bank as follows:

• Better personalized experience to customers
• Better equipped to deal with fraud and money laundering activities
• Improved operational efficiency
• Better compliance with policy norms
• New analytical insights
• Innovative product offerings

NLP in finance use cases

NLP has specific finance applications, including loan risk assessments, auditing and accounting, sentiment analysis and portfolio selection. Below are 4 examples of how NLP transforms the financial services field:

1. Risk assessments

Banks can quantify the chances of a successful loan payment based on a credit risk assessment. Usually, the payment capacity is calculated based on previous spending patterns and past loan payment history data. But this information is not available in several cases, especially in the case of poorer people. According to an estimate, almost a half of the world population does not use financial services due to poverty.

NLP is there to solve this problem. NLP techniques use multiple data points to assess credit risk. For instance, NLP can measure attitude and an entrepreneurial mindset in business loans. Similarly, it can also point out incoherent data and take it up for more scrutiny. Even more, the subtle aspects like lender’s and borrower’s emotions during a loan process can be incorporated with the help of NLP.

Usually, companies capture a lot of information from personal loan documents and feed it into credit risk models for further analysis. Although the collected information helps assess credit risk, mistakes in data extraction can lead to the wrong assessments. Named entity recognition (NER), an NLP technique, is useful in such situations. NER helps to derive the relevant entities extracted from the loan agreement, including the date, location, and details of parties involved.

→ Read how NLP social graph technique helps to assess patient databases can help clinical research organizations succeed with clinical trial analysis.

2. Financial sentiment

Successful trading in the stock market depends upon information about select stocks. Based on this knowledge, traders can decide whether to buy, hold, or sell a stock. Besides analyzing quarterly financial statements, it’s essential to know what analysts are saying about those companies, and this information can be found on social media.

Social media analysis involves monitoring such information within social media posts and selecting potential opportunities for trading. For example, news of a CEO resignation usually conveys a negative sentiment and can affect the stock price negatively. But if the CEO was not performing well, the stock market takes resignation news positively and it may potentially increase the stock price.

DataMinr and Bloomberg are some of the companies that provide such information for help in trading. For example, DataMinr has provided stock-specific alerts and news about Dell to its users on its terminals that potentially affect the market.

The financial sentiment analysis is different from routine sentiment analysis. It’s different in both the domain and its purpose. In regular sentiment analysis, the objective is to find whether the information is inherently positive or not. However, in financial sentiment analysis based on NLP, the purpose is to see if the how the market will react to the news and whether the stock price will fall or rise.

BioBERT, a pre-trained biomedical language representation model for biomedical text mining, has been quite useful for healthcare and now researchers are working on adapting BERT into the financial domain. FinBERT is one of those models developed for the financial services sector. FinBERT operates on a dataset that contains financial news from Reuters. To assign sentiment a Phrase Bank was utilized. It consists of about 4,000 sentences labeled by different people of business or finance backgrounds.

In usual sentiment analysis, a positive statement implies a positive emotion. But in Financial Phrase Bank, negative sentiment implies that the company’s stock price may fall because of the published news. FinBERT has been quite successful with an accuracy of 0.97 and a F1 of 0.95, significantly improved compared to other available tools. The FinBERT library is open on GitHub with the relevant data. This robust language model for economic sentiment classification can be used for different purposes.

→ Discover the sentiment analysis algorithm built from the ground up by our data science team.

3. Accounting and auditing

Deloitte, Ernst & Young, and PwC are focused on providing meaningful actionable audits of a company’s annual performance. For instance, Deloitte has evolved its Audit Command Language into a more efficient NLP application. It has applied NLP techniques to contract document reviews and long term procurement agreements, especially with government data.

Companies now realize NLP’s importance in gaining a significant advantage in the audit process especially after dealing with endless daily transactions and invoice-like papers for decades. NLP enables financial professionals to directly identify, focus, and visualize anomalies in the day-to-day transactions. With the right technology, less time and effort is spent to find out irregularities in the transactions and its causes. NLP can aid with the identification of significant potential risks and possible fraud, like money laundering. This helps to increase value-generating activities in order to disseminate them across the organization.

4. Portfolio selection and optimization

The main goal of every investor is to maximize its capital in the long-term without knowledge of the underlying distribution generated by stock prices. Investment strategies in financial stock markets can be predicted with data science, machine learning and nonparametric statistics. The collected data from the past can be used to predict the beginning of the trade period and a portfolio. Thanks to this data, investors can distribute their current capital among the available assets.

NLP can be utilized for semi-log-optimal portfolio optimization. Semi-log-optimal portfolio selection is a computational alternative to the log-optimal portfolio selection. With its help, the maximum possible growth rate is achieved when the environmental factors are uncertain. Data envelopment analysis can be utilized for portfolio selection by filtering out desirable and undesirable stocks.

5. Stock behavior predictions

Predicting time series for financial analysis is a complicated task because of the fluctuating and irregular data as well as the long-term and seasonal variations that can cause large errors in the analysis. However, deep learning combined with NLP outmatches previous methodologies working with financial time series to a great extent. These two technologies combined effectively deal with large amounts of information.

Deep learning by itself is not a brand new notion. In the last 5 years, a great number of deep learning algorithms have started to perform better than humans at a number of tasks, such as speech recognition and medical image analysis. Within the financial domain, recurrent neural networks (RNN) are a very effective method of predicting time series, like stock prices. RNNs have inherent capabilities to determine complex nonlinear relationships present in financial time series data and approximate any nonlinear function with a high degree of accuracy. These methods are viable alternatives to existing conventional techniques of stock indices prediction because of the high-level of precision they offer. NLP and deep learning techniques are useful to predict the volatility of stock prices and trends, and also is a valuable tool for making stock trading decisions.

NLP in financial practice

NLP techniques are used to transform the unstructured text information into insightful analytics. NLP algorithms have become much more reliable and scalable in recent years and are equipping financial decision makers with a comprehensive understanding of the market

The financial industry is utilizing NLP to decrease the amount of manual routine work and to accelerate the trades, assess the risks, understand the financial sentiment, and construct portfolios while automating auditing and accounting. These advances are achieved with the help of sentiment analysis, question-answering (chatbots), topic clustering and document classification.

NLP and ML have become the technologies of choice for financial analysts, traders and portfolio managers. Insurance organizations utilize NLP to process claims automatically, while retail banks are streamlining their customer support, offering better financial products to their clients and are better equipped to deal with fraud and money laundering activities.

The year 2021 is the most exciting time to adopt the disruptive technology of NLP that will transform how everyone invests for generations. Get in touch with Avenga if you’d like to have an NLP or ML solution developed for your organization.