Usage of Natural Language Processing in Finance

In an increasingly digital world, artificial intelligence (AI) has come to play a huge role in how companies are using technology. AI itself is relying on natural language processing (NLP) for greater efficiency and efficacy.

Natural Language Processing is an integral part of the AI ecosystem. It helps comprehend and interpret human language. This is done using machine learning algorithms that help computers make sense of the human language.

In the world of finance, data is sacred. Finance professionals are continuously analyzing data in various forms. Critical data in finance also exists in text form – in records, on websites and on other related forums. Analyst reports, information memorandums, balance sheets and executive summaries are all important sources of financial information and data.

It aids the processing of all this data, in numerical and text form in real time – effectively and quickly. It offers a range of applications in the finance sector, a few of which are more in demand and impactful.

Efficient text analytics

The ability of an application to extract data in a structured form from unstructured text is called efficient text analytics. The extracted data is delivered in an insightful, useful, value-adding format.

Natural Language Processing is so advanced that it can analyze the sentiment contained in a large body of text and draw out key financial data that can help with informed decision-making. It can understand the context in the text and cull out numerical information relevantly and authentically. This feature of NLP is also called sentiment analysis.

When used in text analysis, the following outcomes are possible:

•     Data can be generated in a format that is useful for financial analytics.
•     This aids data visualization and further leads to accurate decisions being made.

Text analysis is a boon for risk management practices in large financial services corporations. Investors, data scientists and risk managers benefit from using it.

Unstructured data utilization

The financial sector often deals with an ocean of data available in myriad formats: .xlsx, PDF, XML, HTML, web, newsfeeds and .docx. Analyst reports, investor concall transcripts, corporate filings, research and analyses reports and quarterly earnings announcements are mostly generated in these formats .

Making sense of all this information through sharp, accurate, data-driven analysis is a huge challenge for finance professionals. If this task were to be attempted manually, it would demand a colossal investment of time. Also, there will be no guarantee that the data generated would be qualitative and accurate, which will impair decision-making.

Natural Language Processing is an invaluable resource that not only helps analyze unstructured data, it also helps its instant utilization. When deployed efficiently, NLP models can help project market trends with a great deal of accuracy.

Further, it can help with the compliance and regulatory processes in financial institutions. It can capture relevant and significant minutiae from tons of digital information, be it text or data.

Analyzing financial documentation

Finance professionals can deploy these models to analyze financial documentation effortlessly. This can be done with prevalent legacy systems without affecting current workflows and processes.

Through sharp programming, NLP can quickly pore over large volumes of information contained in hundreds of thousands of documents. The other advantage is that it can help build a search engine for financial institutions. This means information can be retrieved from historical data and documents almost instantaneously. This is a boon for financial analysts because it saves them time and makes their role more meaningful.

Qualitative customer engagement

 Much of the data in the finance sector are contained in huge volumes of customer transactions. There’s a possibility that key metrics may be lost in all this information. Natural Language Processing makes it possible to finely extract data from such large volumes of customer transactions. This, in turn, makes it possible for the financial institutions to have qualitative engagements with their customers.

Typically, specific customer challenges can be isolated from all the information using Natural Language Processing. This helps banks and other financial services companies develop better, efficient, customer-friendly policies and products. It is also deployed in analyzing social media conversations – this again offers invaluable insights for finance professionals to service customer segments better. In effect, NLP can help with delivering sharp customization and personalized experiences.

Content intelligence

This is an emerging technology feature of the NLP space. Using content intelligence, financial services companies can analyze the impact of content on customers. They do this by using data analytics on all the content deployed. This helps with devising better content strategies to woo and service customers.

Importantly, this feature is used to spot patterns in content, even analyze the tone of content and helps take data-driven decisions. This analysis helps decide what kind of content will work for a particular platform or customer segment. This enriches the customer experience and helps the financial services company grow their brand value.

Conclusion

NLP is a growing domain within AI. Its impact on the finance sector is profound. Specifically, it is efficient, consistent, accurate, scalable and fully automated.

For instance, a bank may be able to achieve the following results using NLP:

•     Offer personalized experiences for their customers.
•     Prepare better and secure themselves against fraud.
•     Be operationally efficient, thereby increasing profits.
•     Ensure compliance and adherence to policies.
•     Use analytics to develop breakthrough products and services.
•     Make decision-making sharper, quicker and accurate.

Simply, when used and leveraged effectively, Natural Language Processing can be a game changer. 

People Also Ask

1. What is Natural Language Processing ? 

Natural Language Processing is a specialised area in Artificial Intelligence. It aids machines in processing and grasping the human language and helps in automation of routine tasks.

2. What are the five phases of NLP?

The five phases of NLP are lexical analysis, semantic analysis, parsing, discourse integration and pragmatic analysis.

3. What are some other application areas of NLP?

Major application areas of NLP are chatbots, speech recognition, machine translation and optical character recognition.