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Natural Language Processing

Matchi Trend Report - Digital Account Origination

Fintechs that use Natural Language Processing aims to understand unstructured text by and voice using machine learning and artificial intelligence respond appropriately – like chatbots, extracting information from conversations, sentiment analysis and automatic summarisation

In its simplest form, Natural Language Processing (“NLP”) is the ability of a computer program to understand human speech as it is usually spoken. It is different to the older versions of speech recognition and text to speech programs that have been well known for some years but these were essentially rules based where either text or speech are compared to libraries of text or speech wave patterns.

Computers have used formal languages such as XML, PHP, SQL that have no ambiguity possible. Development of linguistic frameworks like Meaning-Text Theory, distributed computing and neural networks allow computers to process natural language and start understanding the meaning underneath the human language.

NLP enables computer programs to understand unstructured text by using machine learning and artificial intelligence to make inferences and provide context to language, just as human brains do. It is a tool for uncovering and analyzing the "signals" buried in unstructured data. Companies can then gain a deeper understanding of public perception around their products, services and brand—as well as those of their competitors.

There are many obvious use cases like voice activated search or translation of languages but far fewer have been used in financial services.

The main use cases in financial services are generally:

  1. Gather real-time intelligence on specific stocks- create a real-time alert if analysts upgrade or downgrade a stock, thereby offering clients a valuable trading edge
  2. Response handling – computer programs that interact with clients to listen and understand queries and suggest appropriate answers
  3. Anticipate client concerns- discover and parse customer sentiment by monitoring social media and analyzing conversations about their services and policies.
  4. Sentiment analysis – analyse conversations (e.g. from social media) and extract sentiment around stock prices, for example
  5. Automatic summarisation – ability for a program to analyse monthly fund performance, for example – and speak the commentaries in a natural manner
  6. Information extraction – analyse information from voice or text sources and provide context for quicker resolution of issues

Some solutions

Response handling

Sentiment analysis

Automatic summarisation

Anticipate client concerns

Information extraction






More on Matchi: Matchi is a global fintech innovation match-making firm, since 2013. Matchi has worked with over 100 leading banks and insurance companies (FIs) around the world, and has a database of over 2,500 fintech firms. Matchi provides both a highly curated portal of fintech solutions, as well as bespoke projects for FI clients to source targeted fintech solutions aimed at the FIs focus areas / pain points. The global Matchi team has run Innovation Challenges and Market Scans for multiple FIs around the world, in markets as diverse as Canada, Japan and India, as well as searches in other geographies.

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