What is natural language processing?

The term natural language processing, or NLP, describes a computer’s ability to understand, interpret, or generate human language. It is a type of artificial intelligence (AI) which uses algorithm-backed machine learning (ML) to recognise both the written and spoken word.

Testament to its utility, in 2023 the market size of NLP in finance was valued at $5.5 billion. It is projected to swell to $40 billion by 2032, with a compound annual growth rate (CAGR) of over 25%.

In this instalment of Finextra’s Explainer series, we look at the capabilities of NLP, the various types and benefits, as well as the potential applications within financial services.

Three kinds of NLP

There are three main families of NLP technology:

1. Rules-based NLP

This uses pre-defined rules to analyse and process text. The rules are created by humans and designed to handle specific structures and patterns in language. Rules-based NLP is best deployed in controlled domains – such as legal documents or technical manuals. It is not so reliable when applied to more dynamic or creative works.

2. Statistical NLP

Instead of pre-defined rules, statistical NLP uses ML to spot patterns and relationships in datasets – enabling automated extraction and classification of linguistic elements. This makes statistical NLP useful for spell checkers, text summaries, and chatbots, though it can struggle with the context-dependent nuances of language.

3. Deep learning NLP

This method leans on a specific type of ML – artificial neural networks – to understand language. The benefit of neural networks is that they are adaptive, enabling tasks like text classification, translation, and question answering, with great accuracy. Deep learning NLP is useful for sentiment analysis, machine translation, speech recognition, text generation, and even interpreting the emotional tone of text.

The benefits

The benefits of NLP are so numerous that the technology is already being rolled out by several industries. Generally speaking, NLP has the ability to:

• Enhance human-computer interaction – meaning humans need not study complex computer languages to get the output from machines they require. This democratises information technology (IT)
  
  
• Automate repetitive tasks – by automating document handling or data entry, staff are freed up to focus on more complex, revenue-driving tasks
  
  
• Improve data analysis and insights – NLP can rapidly extract valuable information from unstructured datasets, like customer reviews. These learnings can then be leveraged to, for instance, create new products or improve customer experiences
  
  
• Enhance searches – alongside search engines, NLP technology can better understand the intent behind users’ queries and therefore extend more relevant results and boost satisfaction

The applications

Here are some examples of the ways these benefits can be exploited by the financial services industry:

1. Speech recognition – improving security through identity checks for in-bound customer calls
   
   
2. Chatbots – helping to triage customer queries at the front office
   
   
3. Fraud detection – examining transaction references and other communications to identify suspicious patterns or fraud schemes
   
   
4. Market analysis – trawling traditional media, social media, or call transcripts to gauge sentiment; foresee market movements; and inform investment decisions
   
   
5. Document processing – automating regulatory obligations like Know Your Customer (KYC) processes, by extracting key data from unstructured files
   
   
6. Legal analysis – interpreting contracts to save on legal costs and human resources

In today’s fast-paced, digital zeitgeist, the financial services industry has moved from measuring time in weeks and days to seconds. Indeed, a mere nanosecond can be the difference between catching and missing a fraudulent transaction, or executing and losing a vital trade. NLP enables institutions to greatly accelerate the mining of information, from multiple mediums, and use it to the advantage of the business and its customers.

Countless banks are already deploying NLP technology to this effect. HSBC, for example, recently launched AI Markets – a digital service that leverages NLP to support institutional investors. The proprietary NLP engine generates bespoke financial market analytics, with access to the bank’s real-time and historic cross-asset data sets.

The challenges

When implementing and deploying NLP technology, banks should be aware of the challenges that surround it. One of the most widely covered issues is that some NLP tools carry the biases of their programmers or the datasets used to train them. This means that NLP has the potential to reinforce societal biases when applied. Systems must be created that offer a fair service to all customers.

As with all languages, meaning is delivered not just by words themselves, but by the tone and context. NLP technologies must be advanced enough to catch these nuances and ensure an accurate output is rendered.

To make matters even more challenging, languages and dialects are constantly shifting – with new accents and vocabularies constantly entering the vernacular. NLP models must have the flexibility to shoulder these dynamics and deliver a consistently reliable service to companies.  

Integrating NLP with next-generation tech

Thanks to the ability of NLP technology to automate routine tasks, interpret sprawling datasets, and offer actionable insights to product teams, it is poised for massive growth in financial services. Indeed, its place in banks’ back offices will only become more embedded, as efficiencies are driven, data analysis is enhanced, customer experiences become hyper-personalised, and algorithmic trading becomes commonplace.

Eventually, NLP will be integrated with other next-generation technologies, like quantum computing and generative AI (GenAI), to unlock seismic opportunities across an institution’s every business line. At each stage of development, it is incumbent on those deploying the technology to spot and restrain data bias, develop an intimate understanding of how the models make decisions, and ensure information accuracy remains as clear as glacial runoff.