Natural language processing is transforming the financial services industry with banks using NLP for evaluating performance drivers and forecasting the market.

From market analysis, content reviews, and risk management, NLP is accelerating changes in the financial industry¹. The traction towards NLP in financial services is increasing with demand for BERT NLP growing among financial institutions.

NLP can be utilized to assess a wide range of speech and text data from different contexts. Additionally, NLP enables banks to automate and optimize tasks including amassing customer information and searching documents.

Banks can expect NLP solutions from AI vendors to extract data from both structured and unstructured documents with a reasonable level of accuracy. Accordingly, financial institutions need to be aware of the fact that collected data from transactions and loan documents in the past, might not be useful for training #machinelearning models unless it is cleaned.

Overview of Natural Language Processing

The Bank of America is using natural language processing by leveraging this technology to become competitive in the market. Other banks including HSBC are following suit by using natural language processing to streamline operations and gain market insights.

According to Yahoo Finance, the natural language processing market will expand in 2020 with a growth rate of 19% totaling to $14B. Alchemy Data tool from IBM² is changing the financial services experience by converting large information sets into insights used for decision-making.

Companies such as Green Key Technologies¹¹ have developed NLP solutions for the financial industry with their latest innovation around trading desks. Financial institutions use their tools in voice information and analysis of trading processes.

Why #naturallanguageprocessing in the financial services industry?

The answer is simple. Retrieving information from unstructured resources that financial institutions have problems accessing.

Banks need accurate information about their operations and NLP tools are changing the landscape by helping them make decisions based on customer and market trends.

1. Customer Management and Predictions

Financial institutions must deliver quality services to their clients and this means going the extra mile to understand customer information.

NLP is reviewing customer data³ including social interactions and cultures which helps them to customize services. For instance, NLP filters through social media information and detects conversations that may help them offer better services.

Stripe¹² is using NLP to explore customer information to identify interest areas that influence customers positively. Predicting customer needs is critical in the financial industry and Stripe is deploying #artificialintelligence and natural language processing to deliver better services.

2. Market Evaluation and Monitoring

One challenge facing banks is the lack of tools for reporting market conditions such as company news posted online or mentioned in business news. NLP is bridging this gap by supporting real-time dissemination of information about their services from customers and business partners.

A company with a bad reputation performs poorly in the market and NLP assists to anticipate these problems and address them.

The Alchemy language tool enables financial institutions to track information about their operations in the market and make decisions. Developed by Watson, Alchemy Language assists banks to explore market trends⁴ and interactions around their services, which further supports the management process.

Unlike the past when banks took long to get the whole market view, NLP is streamlining the process through #data extraction tools.

3. Compiling Financial Reports

The financial services sector consists of volumes of information that pose challenges when reviewing transactions. Natural language processing⁵ is making the process easy through information filtering that helps financial analysts to access the right information.

JP Morgan adopted NLP with much success after the company faced problems in identifying key areas of their market operations.

Client communication in financial services is critical and NLP tools offer vital information to banks as they engage with customers. NLP systems predict and identify problem areas facing customers and this helps banks to develop policies around these challenges and serve them.

Banks make decisions based on NLP tools, which further accelerates the preparation of financial reports⁶.

4. Automatic Updates on Company Operations

Enterprises operating in the financial services experience market changes because of new hires and key people exiting the company and NLP is managing these responses by telling banks on market ramifications.

The stock market fluctuates or rises depending on company departures and NLP tools⁷ relay information to management for further action.

Banks look at the effects of staff reorganization on their share price and use NLP to facilitate the internal evaluation of operations to align with market expectations.

5. Risk Management

The success of companies in the financial industry depends on risk management procedures adopted and NLP is supporting in this area.

Fraud management is the first advantage of using NLP in financial services where banks monitor suspicious financial transactions⁸ and develop tools for addressing this problem.

NLP systems point to the risk areas and support communication across the financial organization about the impending risk. This further reduces the chances of incurring losses.

Chime¹³ is one banking institution with success in using NLP for fraud detection where the bank utilizes these tools in all transactions. According to the CEO of Chime, natural language processing is making transforming financial services by reducing customer risks and offering value to investors.

Cases of fraud in the financial industry rose by 60% in 2019 alone according to a Pew Research poll and Chime is taking advantage of NLP tools.

Insider trading⁹ in financial services remains a major risk with banks losing revenues because of financial misconduct. Natural language processing offers an ideal platform for the management of trading activities by relaying updates based on company operations.

NLP pinpoints instances of insider trading before losses occur and safeguards the image of the business.

6. Stock Market Forecasting and Management

The stock market matters in the financial services and NLP tools are offering information about the behavior of stocks. For example, a bank can understand the current stock performance, forecast risks, and respond to market forces.

The Alchemy Data from IBM develops responses that enable banks to determine the performance of their stock.

A company needs to figure out ways of improving stock performance and through NLP News API¹⁰, this becomes easy because of accessing accurate information about the market.

Trading in the stock market fluctuates and responding to the problem requires technology solutions such as natural language processing which interpret data.

Natural language processing automation is helping banks and other financial institutions explore effective ways of managing their stocks with HSBC implementing across all its operations.

By using NLP for market forecasting, HSBC explores stock market performance and offers recommendations based on prevailing market conditions.

7. Sentiment Analysis

Banks need information about their operations to remain competitive and reduce losses. Natural language processing reviews complex information within the financial services and offer accurate information including inconsistent data.

Unstructured information within a bank poses challenges when it comes to extracting insights and this is where NLP comes in. Equity performance is one area of that banks need attention and NLP tools provide a clear analysis of operations.

The categorization of financial data by NLP is what makes this technology vital for banks in the current digital age. Overall, banks use NLP to measure and understand their operations based on variables such as customer demand and stock market performance.

8. Financial Variable Relationships

The #financialsector is adopting natural language processing because of determining relationships including revenues, stock earnings, value, and competition.

Graphical representations of these variables become easier by using NLP as banks can monitor and compare with the previous financial performance.

Regression analysis on financial graphs is one area benefiting from NLP as companies use the technology to determine the success rate in the market and detect financial misconduct as well.

By using NLP, banks establish connections between variables and use them to make strategic decisions. The entity modeling system from NLP has made relationships between variables convenient as banks can determine major areas affecting their operations.

The Future of Financial Services is Natural Language Processing

Advancements in natural language processing such as voice solutions are streamlining operations in the financial industry as banks use NLP tools to capture voice and text information, to convert data.

The same applies to the customer service department where financial institutions rely on NLP to track and understand customer insights. The ability to search through loads of financial information within a short time and with high accuracy makes NLP an important tool for the banking world.

#Textanalytics and voice recognition solutions by NLP have created new opportunities for banks to improve their services and offer value to the market. Before, banks incurred costs for mining data and was tedious because of searching through large data sets.

In this era of COVID-19, financial institutions are using information generated from NLP systems to evaluate the market and estimate risks. Natural language processing systems are assisting bank managers to measure the implications of the pandemic to their operations and support decision-making.

Because of information misinterpretation, natural language processing is improving this by scanning large volumes of data and interpreting them accurately. Unlike humans, NLP technology scans large information sets within a short time and increases efficiency for players in the financial industry.

Works Cited

¹Financial Industry, ²Alchemy Data Tool from IBM, ³Customer Data, ⁴Market Trends, ⁵Natural Language Processing, ⁶Financial Reports, ⁷NLP Tools, ⁸Financial Transactions, ⁹Insider Trading, ¹⁰News API

Companies Cited

¹¹Green Key Technologies, ¹²Stripe, ¹³Chime