Win fintech customers with NLP

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Recent advances in Artificial Intelligence (AI), and particularly in its sub-areas – Machine Learning (ML) and Natural Language Processing (NLP) – bring us close to the moment when we no longer differentiate between the way people speak (human language). ) and the way machines interpret and reproduce them (machine language).

And we bet FinTech will give us some of the best examples here – the financial services industry has always been an early adopter of new technology.

Are you thinking about integrating NLP into your services? Here are some ideas on how to use mainstream NLP software today with a proven ROI, what is booming tomorrow – and how to use next-generation tools sooner than your competitors.

The Impact of NLP on FinTech: Overview

What do customers expect from their banks, insurance companies and credit unions today? Real-time transactions, supervised management of your assets and the ability to resolve any problem online.

To achieve this, financial services must be provided with cutting edge technologies that demonstrate speed, intelligence and autonomy.

AI, which turns machines into human-like units, lets them perform the same tasks as humans – but better and faster. This is achieved through a complex of tools and technical solutions made possible mainly by its main sub-domains – Machine Learning (ML) and Natural Language Processing (NLP).

Machine learning trains systems to learn from “experience”, i.e. incoming data, and to make data-driven decisions. NLP is trained like other systems, but has a specific goal: It must enable machines to interpret human speech both when speaking (automated speech) and when typing (automated text writing).

Natural language processing in Fintech (as in any other industry) has 2 main use cases:

  • Understand human language and extract its meaning. Recognize intention & initiate a corresponding reaction (request for help, application, etc.).
  • Conversion of unstructured data in databases and documents into structured data and acquisition of actionable insights through pattern recognition (text mining).

NLP in Fintech: Use Cases of Today and Tomorrow

We can highlight a few use cases where AI and NLP are affecting the FinTech world:

  • Turn chatbots into virtual assistants and consultants
  • Enrich it with advanced big data analytics
  • Communication with them indistinguishable from human communication
  • Using NLP for Fraud Detection
  • Segmentation of customers into groups and improvement of relevant product offerings
  • Reduction of administrative effort & automation of individual tasks and entire domains

Application areas:

Customer service

“Conversational Banking” is a new phenomenon and means a radical change from simple chatbots to full-fledged digital assistants. Natural language processing companies provide them with features that help translate user requests into information that can be used to provide appropriate responses.

What your competitors are using today: A chatbot available around the clock that simplifies communication between bank and customer, provides script-based help with minor problems and quickly resolves simple complaints.

To make your business stand out from them: Invest in virtual assistants with advanced capabilities that can handle context, analyze text sentiments, and perform predictive analysis.

  • Advice to consumers on account management
  • Send a notification when the spending limit is approached
  • Identification of payments in the event of an anomaly detection.

These properties are characteristic of the “Erica” ​​bot – and its success is incredible: the AI-powered virtual assistant helped Bank of America to attract more than 1 million new users within less than 2 months of the bot’s introduction in 2017 to win.

Another emerging tendency to be on the alert is the examination of voiceprints and voice biometrics, which are used to authenticate a user, complete a transaction, and prevent fraudulent activity.

What’s next: The development of machine learning algorithms and especially deep neural networks will soon enable the development of virtual assistants that are able to:

  • Maintain semantically consistent communication
  • Development of a persona-based neural conversation model
  • Various reactions in dialogue with a client.

InsurTech

Advanced digital agents and NLP-based customer service are the next big thing in the global insurance market as well.

What your competitors are using today: A chatbot that is based on predefined rules for selecting a risk profile and is able to:

  • Automatic selection of insurance products
  • Underwriting automation: A user submits an online claim for an insurance claim, receives a decision and an associated interest rate.
  • Submit claims by answering standard follow-up questions.

How To Make Your Business Stand Out From Them: If you decide to integrate a chatbot and turn to a FinTech software development company, consider adding more advanced features like:

  • Simple application approval. An AI chatbot developed by the New York insurance start-up Lemonade took 3 seconds to settle a simple insurance claim. As mentioned by Daniel Schreiber, startup CEO, such chatbots can drastically reduce costs, otherwise “11-13% of the premiums are consumed by the bureaucracy of claims processing”.
  • Fraud Control Algorithms. In this case, a chatbot passes the damage details through a fraud detection algorithm before paying for the claim settlement. For example, it can identify personal connections between people involved in a claim and, if necessary, flag it for further review.

What’s next: As in customer service, a chatbot in InsurTech becomes a virtual assistant that can do the following:

  • Personalized risk profile & scoring
  • Real-time processing of complex claims & calculations
  • Secure access to personal data.

RegTech

RegTech is an emerging FinTech segment in which new technologies are used to make it easier to comply with regulatory requirements.

The financial services industry is one of the most regulated industries, and financial institutions take thousands of man hours to ensure compliance with evolving and changing standards. When something is missing, a company pays incredible fines, not to mention reputational damage.

No wonder that the demand for new technologies in this sector is growing and that NLP is at the top of the list: 11% of the institutions working in the areas of financial risk, FCRM and GRC use natural language processing as a core component in their apps.

There are already some positive examples on the market. For example, Rabobank, a Dutch bank, and their compliance team have implemented an ingest-and-search platform that automatically indexes structured and unstructured data and makes it searchable. The result? Reduced compliance controls from 15 to 3 minutes.

What your competitors are using today: NLP and AI solutions, streamlining the review of new regulatory documents, highlighting the required obligations, validating front office decisions in real time, ensuring BSA / AML compliance and a growing number of industry standards such as MiFID II / MiFIR / EMIR.

How to stand out from them: The next generation of AI instruments with built-in NLP capabilities offers:

  • Contract review. JP Morgan’s program called COIN (Contract + Investigation) took a few seconds to complete a full document review that required 360,000 hours of routine work – sounds pretty nice, doesn’t it?
  • Regulatory investigations. To detect potential anti money laundering (AML) and combat terrorist financing violations (CFT), advanced AI-driven data analysis tools (NLP / ML) are required to detect networks of related transactions and identify abnormal behavior.

What’s next: RegTech is developing at an incredible rate, with no signs of slowing down (specialists even call the year 2020 of RegTech). What does this mean for developers?

  • Work on cross-institutional and cross-jurisdiction analyzes. Soon we will see RegTech grow from a small segment of the financial services market to a separate domain. It will look like an information framework, with contextualized obligations, precise definitions and clear data requirements. AI and NLP in particular will be the driving force behind this process – so it is of the utmost importance to prepare now for the RegTech future with its due diligence solutions, robust case management capabilities, automated regulatory reports and the ability to Share information across multiple channels.

Of course, this is not the complete list of NLP use cases applied to the FinTech industry. Trading, crowdfunding, P2P financing – these are just a few areas that can benefit from natural language processing.


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