editorial
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The term natural language processing (NLP) describes the computer's ability to understand, interpret, or generate human language. This is a type of artificial intelligence (AI) that uses algorithm-supported machine learning (ML) to recognize both written and spoken words.
In 2023, which is a testament to its usefulness, NLP's market size in finance was valued at $5.5 billion. It is projected to swell to $40 billion by 2032, with a combined annual growth rate (CAGR) of over 25%.
In this article from FineXtra's Explainer The series discusses the features of NLP, the different types and benefits, and potential applications within financial services.
Three types of NLP
There are three major families of NLP technology:
- Rule-based NLP
It analyzes and processes text using predefined rules. Rules are created by humans and are designed to handle specific structures and patterns of language. Rule-based NLP is best deployed in control domains, such as legal documents and technical manuals. Applying it to more dynamic or creative works, it's not so reliable.
- Statistics NLP
Instead of predefined rules, Statistics NLP uses ML to find patterns and relationships in datasets, allowing for automated extraction and classification of language elements. This helps statistical NLPs with spell checkers, text summaries, and chatbots, but can be struggling with context-sensitive language nuances.
- Deep Learning NLP
This method leans towards certain types of ML (artificial neural networks) in order to understand language. The advantage of neural networks is that they are adaptive and allow very accurate tasks such as text classification, translation, and answering questions. Deep Learning NLP can help you interpret emotional analysis, machine translation, speech recognition, text generation, and even the emotional tone of text.
advantage
Because of the great benefits of NLP, this technology is already being deployed by several industries. Generally speaking, NLP has the following capabilities:
- Enhance human computer interactions. In other words, humans do not need to study complex computer languages to obtain output from the machines they need. This democratization information technology (IT)
- Automating repetitive tasks – By automating document processing and data entry, staff are freed to focus on more complex, revenue-driven tasks
- Improved Data Analytics and Insights – NLP can quickly extract valuable information from unstructured datasets such as customer reviews. These learning can be used to create new products, for example, or to improve the customer experience.
- Enhanced Search – Alongside search engines, NLP technology can better understand the intent behind users' queries and thus expand more relevant results and increase satisfaction
application
Here are some examples of how these benefits can be exploited in the financial services industry.
- Voice Recognition – Improve security through inbound customer call identity checks
- Chatbot – Helps triage customer queries in the front office
- Fraud detection – Explore transaction references and other communications to identify suspicious patterns or fraud schemes
- Market Analysis – Call traditional media, social media, or transcripts and invoke transcripts to measure emotions. Foresee the market movement. Notify us of investment decisions
- Document Processing – Automate regulatory obligations such as the Know Customer (KYC) process by extracting important data from unstructured files
- Legal Analysis – Legal Costs and Interpretation of Contracts to Save HR
The financial services industry has moved from weeks and days to seconds at the speed of today's fast-paced digital age. Certainly, mere nanoseconds can be the difference between catching a fraudulent transaction and missing it, or running and losing a critical transaction. NLP allows agencies to significantly accelerate the mining of information from multiple media, and use it to benefit their businesses and their customers.
Countless banks are already deploying NLP technology to this effect. For example, HSBC was recently released AI Market – A digital service that utilizes NLP to support institutional investors. The proprietary NLP engine generates bespoke financial market analysis that allows you to access the bank's real-time historic cross-asset datasets.
assignment
When implementing and deploying NLP technology, banks need to be aware of the challenges surrounding it. One of the most widely covered issues is that some NLP tools carry bias in the datasets that are used by programmers or training them. This means that social bias may be strengthened when NLP is applied. You need to create a system that provides fair service to all your customers.
Like all languages, meaning comes not only by the words themselves, but also by tone and context. NLP technology must catch these nuances and proceed well enough to ensure that the accurate output is rendered.
Language and dialects are constantly changing to make the problem even more challenging. New accents and vocabulary constantly enter the foundation. The NLP model must have the flexibility to handle these dynamics and provide a consistent and reliable service to the company.
Integrate NLP with next-generation technology
Financial services are experiencing significant growth thanks to the ability of NLP technology to automate daily tasks, interpret vast data sets, and provide actionable insights to product teams. Certainly, its location in the bank's back office is only more embedded as efficiency is accelerated, data analytics is enhanced, customer experiences become more personal, and algorithmic trading becomes more common.
Ultimately, NLP will integrate with other next-generation technologies such as Quantum Computing and Generic AI (Genai) to unlock earthquake opportunities across all business lines of the agency. At each stage of development, it is mandatory for those deploying technology to find and suppress data bias, to closely understand how models make decisions, and to keep information accurate as clear as glacial runoff.
