Faster, Accurate, Impactful Decisions in Finance — Driven by NLP

Reduce risks and time-to-insights by adding natural language processing to your financial research tools

Financial companies and institutions are facing an ever-increasing volume of text documents instrumental to the decision-making process. Reports, earning call transcripts, client profiles — all of these have to be processed, and fast. To stay competitive and still deliver what their customers are expecting, more and more companies in the financial industry are now looking into the latest, already proven technologies to revamp their research and data processing tools.

Financial managers have a difficult and demanding job because they have to make fast and very important decisions affecting the lives of their customers. The importance and urgency of these decisions can’t be overestimated. Making an informed call requires an analysis of both structured and unstructured textual data. Analyzing it is often complicated because financial applications have to pull data from multiple sources. The users are mostly accustomed to the legacy way of querying the information — often a very cumbersome and inefficient keyword-based interface.

Following the latest industry trends, many companies have built completely new applications to produce financial insights. These modern insights applications focus on increasing the speed and efficiency of accessing the right information at the right time, which dramatically improves the process of research and discovery. While the automation of the legacy processes is part of the equation, it is first and foremost about transitioning to an intelligent, ML-driven, Google-like search experience for the financial industry applications.

The ML technology at play here is natural language processing (NLP). Its latest iteration, introduced by Google in 2018, has brought it to the forefront of the most impactful and practical innovations of the past decade. The new open source models and algorithms allowed millions of developers to just build what was previously available only to the industry giants like Google or Facebook. The current state of applied NLP and the way it’s used in application development makes it possible for enterprises to use NLP game-changing capabilities for text data analysis. In turn, the opportunity cost and the implications of not implementing NLP can be enormous.

The following text analysis and discovery use cases are the most common targets for NLP:

  • Reports, disclosure, and regulatory document analysis
  • Earnings call transcripts mining
  • Financial and economic news and publications monitoring
  • Organization and client documentation analysis
  • Fund prospectus analysis
  • Qualitative market research
  • Aggregate report generation
  • Tax compliance analysis

Modern NLP includes common techniques, such as semantic search, question answering, document ranking and classification, summarization, information extraction, and more — all of which could help financial organizations find, identify, classify, and process key information faster and more efficiently.

NLP-augmented business applications enable the end users in the financial industry to query the information using their own natural human language — and receive precise answers and summaries from the documents instead of a mere list of documents to check manually. The latest achievements in the field of NLP also streamline the use of the existing language models without the need to train their own model or develop a new algorithm from scratch.

By adding NLP to their applications, financial organizations can benefit from the following — all without incurring unnecessary costs in time or capital:

  • Scaling up research and discovery processes to eliminate 80% of irrelevant information
  • Extracting previously hidden valuable information from the vast trove of financial data
  • Aligning the decision-making process with the ever-increasing external and internal challenges
  • Identifying and eliminating bias in the decisions
  • Improving proactive risk management
  • Eliminating hidden risks from the incomplete screening of the full document base
  • Ensuring uninterrupted compliance processes
  • Faster adaptation to regulatory changes

If you want to have more detailed information about the state of modern NLP and how financial enterprises are building powerful, NLP-driven solutions, please visit our website