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May 12, 2020 01:03 PM EDT

How Natural Language Processing Can be Used in Finance

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How Natural Language Processing Can be Used in Finance

(Photo : How Natural Language Processing Can be Used in Finance)

Unstructured data is proliferating in the form of social media posts, images, text messages, audio files, email, PDFs and more. To extract value from this data, companies across industries, including financial institutions, are turning to Natural Language Processing (NLP), a component of Artificial Intelligence. 

NLP enables computer programs to understand unstructured text, make inferences and provide the context in the same way as the human brain. Financial institutions have access to many data sources they can mine for actionable insights to improve business practices. Valuable information is being used to augment a wide range of processes including managing client relationships, making market predictions and detecting fraud. 

The finance industry is also using NLP and other AI-driven solutions to personalize communication, increase workforce productivity and identify opportunities in data that may otherwise have been missed. 

In the near future, we are likely to see an increase in technologies based on NLP, such as robo-advisors and chatbots. Bank of America currently has over 6 million customers using its AI-driven virtual financial assistant. Clients can chat via audio or text and have their queries answered by "Erica" who provides them with relevant guidance and makes managing their finances easier. Well-trained chatbots can provide great savings by automating time-consuming tasks. 

There are many examples of great natural language processing applications in business and especially in finance. Here are the most important ones:

Sentiment analysis

Sentiment analysis is one of the most common applications in the financial world. Banks and other financial institutions using NLP are able to analyze client sentiment by monitoring various data sources. 

A very simple example of how this is applied in the financial sector is assigning positive, neutral or negative sentiment values to words. Words like 'benefit,' 'profit' and 'good' are tagged as positive, whilst words like 'loss,' 'risk' and 'bankruptcy' are tagged as negative.

Improved response to client complaints: Sentiment analysis can help service agents to deal with service tickets more effectively. By organizing tickets according to the sentiment attached to them, prioritization can take place based on specific needs and the level of client dissatisfaction.

Personalized investment advice: Investment firms are purchasing software that uses machine learning to track patterns of customer spending, investing, and making financial decisions from their transaction history. Mining of information on market developments is also automated by such software which then uses NLP to find the most relevant information to investors' needs. This enables firms to offer clients personalized investment advice. 

Document Search for Business Intelligence

Documents used in the financial world are often very complex and natural language processing can help machines to understand what they are saying.

Financial services companies can automate and digitize their documentation processes by using NLP. It is possible to integrate a document finance solution into existing workflows. The software uses NLP to automatically read and understand documents, such as those involving loans or mortgages.

Historical documentation records can be used to train an NLP solution. NLP can go through several thousand documents speedily to extract and summarize the most relevant information.

Credit scoring

Banks and financial institutions are able to use NLP to assess whether an individual is creditworthy. LenddoEFL is a Singapore-based company that offers software called The LenddoScore. In developing countries, potential clients may have little or no credit history and this software helps banks to understand their lending risks based on their digital footprints.

Users download the Lenddo application on their smartphones, where it goes through social media account use etc. and uses machine learning algorithms to convert data into a credit score.

Market activity predictions

Professionals involved in the stock market dissect plenty of financial data every day and the use of NLP-based techniques can significantly improve stock price prediction and quantitative investing models. Predictive capabilities are being used to default risk, estimate profitability, etc.

Algorithmic trading is a trading method that executes orders according to pre-programmed instructions based on metrics such as time, volume and price.

More broadly, NLP techniques can identify new predictive variables from unstructured data that are then used to enhance analytics processes.

Fraud detection

When deceptive statements are made, they often contain certain language patterns, such as reduced usage of first-person pronouns. Systems may flag sentences where the main verb indicates illegal use of a financial product.

Using NLP can help financial institutions to detect fraudulent statements in any corporate communications, such as emails. Lloyds Banking Group has made use of NLP to identify fraudulent phone calls. Identifying possible illegal insider trading is also possible through using NLP techniques.

Risk Management

NLP can help financial institutions with legal and regulatory compliance. By extracting metadata and "understanding" content, changes to regulatory requirements can be tracked and costs related to compliance determined.

Institutions, therefore, have the information they need to take a risk-based view of regulatory compliance. When data is aggregated efficiently, the resources required for auditing and risk recording processes can be reduced.

Financial research

Financial analysts who have to look at market reports and keep up to date with news are often unable to keep up and feel overwhelmed. Summarization systems tailored to financial documents like financial news and earning reports allow analysts to derive market signals quickly from the content.

Conclusion

The customer-centric and data-driven nature of financial institutions means NLP presents them with many opportunities to enhance their business practices and create value. Many of them are turning to NLP to access and analyze relevant data in a world where unstructured data abounds. This enables them to make more informed decisions and provide good service and quality products to clients.

Financial institutions that don't take advantage of the improved operational efficiency and cost-saving that NLP provides will find it increasingly difficult to remain competitive. In contrast, those that do invest in NLP will experience unprecedented opportunities and insights that will help them to flourish in the future.

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