What is natural language processing?

Natural language processing (NLP) is the ability of a computer program to understand human language as it is spoken and written — referred to as natural language. It is a component of artificial intelligence (AI).

NLP has existed for more than 50 years and has roots in the field of linguistics. It has a variety of real-world applications in a number of fields, including medical research, search engines and business intelligence.

How does natural language processing work?

NLP enables computers to understand natural language as humans do. Whether the language is spoken or written, natural language processing uses artificial intelligence to take real-world input, process it, and make sense of it in a way a computer can understand. Just as humans have different sensors — such as ears to hear and eyes to see — computers have programs to read and microphones to collect audio. And just as humans have a brain to process that input, computers have a program to process their respective inputs. At some point in processing, the input is converted to code that the computer can understand.

There are two main phases to natural language processing: data preprocessing and algorithm development.

Data preprocessing involves preparing and “cleaning” text data for machines to be able to analyze it. preprocessing puts data in workable form and highlights features in the text that an algorithm can work with. There are several ways this can be done, including:

• Tokenization. This is when text is broken down into smaller units to work with.
• Stop word removal. This is when common words are removed from text so unique words that offer the most information about the text remain.
• Lemmatization and stemming. This is when words are reduced to their root forms to process.
• Part-of-speech tagging. This is when words are marked based on the part-of speech they are — such as nouns, verbs and adjectives.

Once the data has been preprocessed, an algorithm is developed to process it. There are many different natural language processing algorithms, but two main types are commonly used:

• Rules-based system. This system uses carefully designed linguistic rules. This approach was used early on in the development of natural language processing, and is still used.
• Machine learning-based system. Machine learning algorithms use statistical methods. They learn to perform tasks based on training data they are fed, and adjust their methods as more data is processed. Using a combination of machine learning, deep learning and neural networks, natural language processing algorithms hone their own rules through repeated processing and learning.
  

  Ways NLP is Changing the Face of Financial Services NLP

  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.

  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.

  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.

  6. Coherent Data Representation

  It’s standard practice in the financial services industry to deal with excessive data. For their research and analytics, finance professionals go through various documents and financial resources daily.

  This growth of unstructured data has complicated the analysis process and increased its time and labor requirements. Because of this, critical financial data that may provide an in-depth understanding to construct plans may be underused and affect decision-making.

  With NLP, one can extract information that might be otherwise underutilized. They can train NLP models to inspect data and trends that might impact the financial markets.

  7. Investor Sentiment

  Trading of any form depends on the information on the subject of investment. This knowledge can help traders decide whether this particular investment is worth it. Let’s talk about stocks, for example. It is essential to know not only about stocks but also what the analysts are saying about the specific company one is planning on investing, and NLP can find this information.

  The financial or investor sentiment analysis stands different from the routine analysis. For the standard analysis, the purpose is to find if the information shared is positive or negative. Meanwhile, in financial analysis based on NLP, one can see how the market reacts to that particular information.

  NLP can analyze social media and monitor this information creating potential opportunities for trading. An example of such a situation will be if a person of authority makes a negative statement. This would severely affect the stocks of the company negatively.

  8. Customer Relations

  With so much data to take in regularly, tracking these transactions can be particularly challenging. Since customer interaction is crucial in this field, analyzing customer pain points becomes an integral part of financial sectors, which is where integration of NLP comes in handy.

  The entire financial sector needs to provide excellent customer service and go above and beyond to understand the customer to serve them better. NLP plays a crucial role here by gathering information like social interactions and its customers’ cultural backgrounds to customize their service.

   

  9. Supporting Compliance Processes

  Much of the data being handled in the financial services sector is private and as a result compliance processes are a must. NLP solutions help to enforce a rigorous approach to compliance, limiting the chances of fraud and malicious attacks.  By labelling data from interactions (language, sentiment and other information), analysing it using bespoke fraud dictionaries, comparing it to previous interactions and evaluating the outcomes, potentially fraudulent activities can be flagged and investigated further, keeping customers’ data in the right hands.

  10. Improving CX

  Of course, if sales and marketing see benefits from the deployment of NLP across the financial services sector, customers are likely to see them too. Improving the customers’ experience is a win-win for customers and agents, reducing churn, improving sales lead times and ensuring the fair and consistent treatment of customers. A great example of this is Amazon. They have used NLP to drive better customer engagement through their product Alexa. Voice assistants are being used to process orders for products, perform actions such as play music or simply start a phone conversation with a contact. The fundamentals of this technology is currently being implemented, but in the next few years we will see the AI software go even further and help assistants with more complex tasks. This adds true value to the customer journey as there is better customer support, as well as helps the customer to save time doing certain tasks, making their everyday lives more enjoyable.