Have you met Erica (Bank of America), EVA (HDFC), Amy (HSBC) or even Aida (SEB, Sweden)? If you have been interacting with banks and financial institutions in the past then you are likely to have communicated with these absolutely brilliant, human-like virtual assistants and chatbots. Natural Language Processing or NLP in the banking and finance sector has advanced to a global scale with more and more financial institutions leveraging the benefits of advanced technological innovation. Along with Artificial Intelligence and Machine Learning, NLP application is creating its footprints across operations, risk, sales, R&D, customer support and many other verticals in the financial sector, that’s in turn leading to greater efficiencies, productivity, cost savings and time and resource management.

The number of NLP use cases in finance is on the rise with financial institutions relying on it for data and business intelligence for making informed business decisions. Top AI companies in turn are relentlessly working on improved solutions involving AI technology to streamline processes and discover smarter solutions for AI in FinTech.

What is NLP in Finance?

NLP in financial services is expanding to beyond its usage in banking, insurance and hedge funds (especially for sentiment analysis). A core component in chatbots, voice assistants, text analytics, NLP technology is seen as the next disruptor in the finance sector. For instance, instead of logging into individual accounts for checking balance, users can simply check their account details through Chatbots and even voice assistants.

Application of Natural Language Processing in FinTech:

• Digital Financial Coach/Advisor:
  

NLP in the financial industry is being used to build transactional bots. These bots act as a digital financial advisor helping users deal with savings, investments and other financial plans. Services of this nature help with engagement and provide users with better user experience. Transactional bots are built using NLP technology to process data in the form of human language.

A good example of NLP and ML in FinTech is Cleo, a personal financial assistant. Cleo can set personal budgets, provide financial advice and help clients meet their personal financial goals.

Digital assistants are also being used in other scenarios such as dividend management, term life renewals, reminder of credit card payments and miscellaneous other notifications consumers generally need to keep a track of.

• Credit Scoring:

The everyday application of NLP technology is also being used by financial institutions in other innovative ways. For instance, evaluating the creditworthiness of an individual and understanding the risk profile is a vital challenge banks and insurance companies face regularly. With the advent of FinTech AI banking, advanced technologies like machine learning and NLP have contributed significantly in analyzing borrower data and facilitating assessing risks and creditworthiness of individuals.

LenddoEFL, a Singapore-based company offers a software called LenddoScore that uses advanced NLP and machine learning algorithms to assess creditworthiness of borrowers. The platform combs through the prospective borrower’s digital footprints across social media, browsing history, geo-location and other information on personal devices. The data is then converted into a credit score using machine learning algorithms that institutions can use to evaluate the creditworthiness of the individual.

• Customer Service:

Uses of Natural Language Processing have also made a major impact on customer service. NLP is being incorporated across chatbots and other software to enhance customer service and resolve queries. With features like sentiment analysis, NLP is often considered a suitable technology for driving better results from customer services and support.

We take the example of Ocado, an online supermarket based in the UK that has been using NLP and sentiment analysis to identify urgent queries. This has resulted in them replying to urgent queries 4x times faster bringing about efficiency and a happier lot of customers.

Automotive, insurance and leasing services are up for leveraging the benefits of AI in the Fintech market and adopting FinTech solutions that are aligned to their business interests.  For instance, Sigmoidal, a machine learning consultancy, has developed a trading software that automates and collects news and social media updates on market developments. Using NLP application the software identifies the most relevant information, while sentiment analysis helps the customer service teams to provide better answers to customer queries.

• Analyze contracts:

Contract analysis is an internal task that is repetitive, mundane and can easily be done with the help of NLP and machine learning. Borrowing a cue from the same  and the fact that AI and AR/VR technologies are likely to have a huge impact on how things will be done in the years to come, it can be estimated that digital contracts will be the norm.

Its occurrence is already in motion. Enterprises like JP Morgan have had high success using NLP powered software to analyze contracts, helping them to free up 360,000 man-hours, yearly.

In addition to using NLP in the banking industry, there has also been a rise in the use of related deep tech, such as Optical Character Recognition (OCR) to digitize hard copies of documents, scan to analyze and correct contracts etc. Similarly, software like Nuance Document Finance Solution Communication help finance companies automate and digitize their documentation using NLP.

• Market Data Collection:

Artificial intelligence development companies are relentlessly looking at innovating newer and better use of NLP and related tech to offer more to their users and help to adopt to a changing market needs.

AlphaSense, a company in New York, offers a market data collection software, which is updated periodically with millions of documents such as company filings and conference call scripts. The engine parses through documents, topics, concepts, and ideas to find relevant information.

Future of NLP: Where does it stand today and tomorrow?

It goes without saying, but the role and contribution of NLP in the banking and financial sector has grown by leaps and bounds, considering the wide array of services it supports. From generating real-time insights from call transcripts, to analyzing data by applying grammatical parsing and paragraph-level contextual analysis, there are numerous uses of NLP technology in the sector. NLP solutions can further extract and interpret data with detailed insights on profitability, trends, and can offer insights on how businesses are performing and will perform in a relative market.

In the years to come, it can be expected that NLP along with NLU and NLG will be more broadly applied to sentiment analysis and coherence resolution.