Natural Language Processing in Fintech world

Natural Language Processing in Fintech world

Technology in Banking and Finance space has given us some of the optimistic examples of evolving technologies being adopted in financial industry and has always been an early adopter of emerging and disruptive technologies. 

A short time ago, improvements in Artificial Intelligence is bringing us close to the time when we won’t make a distinction between the way people talk and the way machines interpret and understand it. 

If you are thoughtful about Natural Learning Processing (NLP) integration into your service, here are some thoughts on how to get use of the mainstream natural language programming software with a proven envisioned savings today, what will it can bang tomorrow – and how to leverage next-gen tools earlier than your competitors.

An Overview: Natural Language Processing impact on Finance World

What customers want today from their financial institutions like banks, insurance companies or credit unions? Real-time transactions, supervised management of their assets, and opportunity to settle any issue online and on quick note.

To make that happen, financial services must be provided with cutting edge technologies, demonstrating speed, intelligence and autonomy. 

Artificial Intelligence, turning machines into human-like entities, makes them perform the same tasks as people do – better and quicker. This is achieved via a complex of tools and tech solutions which are endowed mainly by its major sub-domains – Machine Learning and Natural Language Processing.

Machine Learning trains systems to learn from “experience”, i.e. incoming data, and make data driven decisions. Natural Language Processing is trained the same way as other systems, but has a specific aim: it must empower machines to interpret human speech both as it is spoken (Automated Speech) and typed (Automated Text Writing).

Natural language Processing in Fintech (like in any other sector), have 2 major use cases:

• Understanding human speech and mining its meaning. Recognizing intent & coming up with a relevant response (request for help, passing a claim, etc.).
• Turning unstructured data in databases and documents into structured data and mining actionable insights through pattern recognition (text mining). 

Natural Language Processing in Fintech: Use Cases from Today & Tomorrow

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

• Turning chatbots into virtual assistants and counselors
• Enriching them with advanced Data analytics
• Making communication with them indistinguishable from human communication
• Using NLP for fraud detection
• Segmenting customers into groups & improving relevant product offerings
• Reducing administrative work & automating separate tasks and whole domains

The areas where it can be applied:

Customer Service

“Conversational banking” is a new phenomenon, and it means radical shifting from simple chat bots to full-fledged digital assistants. NLP companies provide them with functionality, helping translate user queries into information that can be used for appropriate responses. 

What your competitors use today: The 24/7 available chat bot, which simplifies communication between a bank and its client, provides script-based assistance with trivial issues and quickly resolves simple complaints.

How to set your business apart from them: Invest into virtual assistants with advanced capabilities, able to process context, analyze text sentiment, and perform predictive analysis. 

• Counselling consumers on bank account management
• Triggering an alert when approaching spending limit
• Flagging payments in case of abnormality detection

These features are characteristic of the “Erica” bot – and its success has been incredible: the AI-powered virtual assistant helped the Bank of America attract more than 1 million new users within less than 2 months after the bot rollout in 2017.

Another emerging tendency to watch out is voiceprint investigation and voice biometrics, used to authenticate a user, help complete a transaction and prevent fraudulent activities. 

What is next: Evolving machine learning algorithms and deep neural networks in particular will soon enable creation of the virtual assistants capable of:

• Keeping semantically consistent communication
• Building a persona-based neural conversation model
• Diverse reactions in dialogue with a client.

InsurTech

Advanced digital agents and Natural Language Processing based customer service is the next big thing in the global insurance market. 

What your competitors use today: A chat bot based on predefined rules of selecting a risk profile, capable of:

• Automatic selection of insurance products
• Underwriting automation: a user files an online application for an insurance claim, receives a decision and an accompanying interest rate.
• Submitting claims, by answering standard follow-up questions.

How to set your business apart from them: Once you decide to integrate a chat bot and turn to a FinTech software development company, think about adding advanced functionality like:

• Simple claim approval. It took an AI chatbot, developed by New York based insurance start-up called Lemonade, 3 seconds to settle a simple insurance claim. As mentioned by Daniel Schreiber, startup CEO, such chatbots allow to cut down on costs dramatically, otherwise “11-13% of premiums are consumed by the bureaucracy of handling claims”.  
• Anti-fraud algorithms. In this case, a chatbot passes the claim details through a fraud detection algorithm before paying for the claim settlement. For example, it can detect personal links between people involved in a claim, and flag it for further examination if necessary.

What is next: Like in customer service, a chatbot in InsurTech is turning into a virtual assistant, which can perform:

• Personalized risk profile & scoring 
• Real-time processing of complex claims & calculations
• Secure personal information retrieval.

RegTech

RegTech is an emerging FinTech segment, where new technologies are used to facilitate compliance with regulatory requirements. 

The Financial Services industry is one of the most heavily regulated ones, and it takes financial institutions thousands of hours of mundane work to ensure adherence to evolving and changing standards. If something is missing – a company will pay incredible fines, not to mention reputational damage.

No wonder that demand for new technologies in this sector is growing, and NLP is on the top of the list: 11% of institutions working across 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 its Compliance team implemented an ingest-and-search platform, where structured and unstructured data is automatically indexed and made searchable. The result is reduced compliance controls from 15 to almost 3 minutes.

What your competitors use today: Natural Language Processing and Artificial Intelligence solutions, streamlining the examination of new regulation documents, highlighting the required obligations, validating front office decisions in real-time, ensuring BSA/AML compliance, and a growing number of the industry’s standards, like MiFID II/MiFIR/EMIR. 

How to set your business apart from them: The next generation of AI instruments with integrated NLP features performs:

• Contract review. It took JP Morgan’s program named COIN (Contract + Investigation) some seconds to perform full-scale documents review, which was taking 360,000 hours of routine work – sounds quite appealing, doesn’t it? 
• Regulatory investigations. Detecting potential anti-money laundering (AML) and combating the financing of terrorism (CFT) violations requires advanced AI-driven data analytics tools (NLP/ML) to detect networks of related transactions and identify abnormal behavior.

RegTech is developing at an incredible speed, with no signs of slowing down (specialists even call 2020 Year of RegTech). What does it mean for IT professionals?

• Working on cross-institutional and cross-jurisdictional analysis. Soon we will see RegTech growing from a minor segment of the financial services market into a separate domain. It will look like an information framework, with contextualised obligations, precise definitions, and clear data requirements. AI, and NLP in particular, will be the driving force behind this process – that’s why it is of utmost importance to get ready for the RegTech future now, with its due diligence solutions, robust case management functionality, automated regulatory reporting, and the ability to share information across multiple channels.

To conclude, it is not the full list of Natural Language Processing use cases applied to the FinTech world. Trading, crowdfunding, P2P financing – these are but a few areas which can win from Natural Language Processing.