NLP in finance: Text analysis is making investment decisions easier

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  Quantumrun Foresight
• October 10, 2022

Insight summary

Natural language processing (NLP) and its companion technology, natural language generation (NLG), are transforming the financial industry by automating data analysis and report generation. These technologies not only streamline tasks like due diligence and pre-trade analysis but also offer new capabilities, such as sentiment analysis and fraud detection. However, as they become more integrated into financial systems, there's a growing need for ethical guidelines and human oversight to ensure accuracy and data privacy.

NLP in finance context

Natural language processing (NLP) has the ability to sift through vast amounts of text to create data-backed narratives that offer valuable insights for investors and companies in the financial services sector. By doing so, it helps guide decisions on where to allocate capital for maximum returns. As a specialized branch of artificial intelligence, NLP employs various linguistic elements such as words, phrases, and sentence structures to discern themes or patterns in both structured and unstructured data. Structured data refers to information that is organized in a specific, consistent format, like portfolio performance metrics, while unstructured data encompasses a variety of media formats, including videos, images, and podcasts.

Building on its AI foundations, NLP uses algorithms to organize this data into structured patterns. These patterns are then interpreted by natural language generation (NLG) systems, which convert the data into narratives for reporting or storytelling. This synergy between NLP and NLG technologies allows for a comprehensive analysis of a wide range of materials in the financial sector. These materials can include annual reports, videos, press releases, interviews, and historical performance data from companies. By analyzing these diverse sources, the technology can offer investment advice, such as suggesting which stocks may be worth buying or selling.

The application of NLP and NLG in the financial services industry has significant implications for the future of investment and decision-making. For instance, the technology can automate the time-consuming process of data collection and analysis, thereby allowing financial analysts to focus on more strategic tasks. Moreover, the technology can offer more personalized investment advice by taking into account a broader range of data sources. However, it's important to note that while these technologies offer many advantages, they are not without limitations, such as the potential for algorithmic bias or errors in data interpretation. Therefore, human oversight may still be needed to ensure the most accurate and reliable outcomes.

Disruptive impact

J.P. Morgan & Chase, a US-based bank, used to spend approximately 360,000 hours annually on manual due diligence reviews for potential clients. The implementation of NLP systems has automated a large part of this process, significantly reducing the time spent and minimizing clerical errors. In the pre-trade phase, financial analysts used to spend about two-thirds of their time gathering data, often without knowing if that data would even be relevant to their projects. NLP has automated this data collection and organization, allowing analysts to focus on more valuable information and optimizing the time spent within the financial services industry.

Sentiment analysis is another domain where NLP is making a substantial impact. By analyzing keywords and tone in press releases and social media, AI can assess public sentiment toward events or news items, such as a bank CEO's resignation. This analysis can then be used to predict how such events may influence the bank's stock price. Beyond sentiment analysis, NLP also supports essential services like fraud detection, identifying cybersecurity risks, and generating performance reports. These capabilities can be particularly useful for insurance companies, which could deploy NLP systems to scrutinize client submissions for inconsistencies or inaccuracies when claiming a policy.

For governments and regulatory bodies, the long-term implications of NLP in financial services are also noteworthy. The technology can assist in monitoring compliance and enforcing financial regulations more efficiently. For example, NLP could automatically scan and analyze financial transactions to flag suspicious activities, aiding in the fight against money laundering or tax evasion. However, as these technologies become more prevalent, there may be a need for new regulations to ensure ethical use and data privacy. 

Implications of NLP applied within the financial services industry

Wider implications of NLP being leveraged by financial services companies may include:

• NLP and NLG systems working together to collate data and write reports on annual reviews, performance and even thought leadership pieces.
• More fintech firms using NLP to perform sentiment analysis on existing products and services, future offerings, and organizational changes.
• Fewer analysts needed to conduct pre-trade analysis, and instead, more portfolio managers being hired for investment decision processes.
• Fraud detection and auditing activities of various forms will become more comprehensive and effective.
• Investments becoming victims to a “herd mentality” if too much input data uses similar data sources. 
• Increased risks for internal data manipulation and cyberattacks, particularly installing erroneous training data.

Questions to consider

• If you work in finance, is your firm using NLP to automate some processes? 
• If you work outside of financial services, how might NLP be applied in your industry?
• How do you think banking and finance roles will change because of NLP?