As more and more companies go through digital transformations, Natural Language Processing (NLP) is emerging as a game changer. As you may already know, NLP is a part of Artificial Intelligence (AI) that uses Machine Learning (ML) to enable a computer system to interpret and understand human language.
With NLP, human-to-machine interactions can be simplified, and software systems can comprehend how users feel and what users are trying to convey.
NLP and AI can already be seen in software innovations such as chatbots and virtual assistants.
However, when it comes to fintech, things change a little. Given that well over 80% of data created today is unstructured data–words or text–it’s crucial for software to interpret this data to quantify what people are saying so that numbers can be crunched accurately, and the right decisions can be taken.
Let’s discuss some ways in which NLP can be useful in the world of financial technology.
Use Case 1: NLP in Hedge Funds
According to a 2020 analysis, AI – which makes predictions based on historical data – has a huge potential in fund management, even when unexpected events like pandemics or wars happen. BThe research found that returns from AI-powered hedge funds were three times higher than other overall hedge funds.
AI, and in particular Machine Learning (ML) and NLP, can help hedge funds in analyzing large volumes of financial information and making the right decisions for top portfolio performance.
In the following sections, let’s discuss three key ways in which NLP can be used in hedge funds:
- Algorithmic Trading
- Volatility Forecasting
- Signal Monitoring
Traders have to consider several independent variables that affect the value of investments and assets. Traditionally, financial analysts have used quantitative data from company regulatory filings, earnings calls and analyst forecasts to assess investments.
Today, there are more places where people are talking about the economy, the market, an industry or a company, and text data is being generated and distributed via websites, social media, discussion forums, emails, etc.
These alternative data sources are new sources of alpha and important indicators of changes that can affect company performance and here are some ways that NLP can help:
- NLP models can enhance fundamental analysis and enrich quantitative models so that better informed data-driven decisions can be taken in real time.
- With NLP based algorithmic trading, NLP models can be used to structure or quantify unstructured text documents, news and commentary into quantitative scores. These scores can then be fed into predictive analytics and machine learning models that fuel automated trading.
- NLP can harness new insights that are missed by humans. In fact, AI-based trading can be implemented to need no human intervention or ask for human approval and then execute investment decisions.
The pandemic, natural disasters, and the war in Ukraine are only the latest events generating a lot of volatility in the market.
When it comes to fund management, it is crucial to make accurate volatility predictions. However, it’s not always entirely possible to do that. If a trader understands the nature of volatility and takes it into account while making investments, they will get better returns.
There are complex nonlinear factors that impact volatility. But, with machine learning and natural language processing, volatility predictions can be more precise. NLP can be used to quantify the latest market conditions, financial news and investor sentiment – all key factors that impact volatility.
Research shows that alpha on new investments decays in about one year.
Trading decisions are based on signals and predictive relationships, which makes it important to review high-quality signals.
When trading positions overlap, it can lead to erroneous decisions; however, when NLP is incorporated into signal monitoring, hedge fund investors can get insights from alternative data to identify uncommon signals or confirm signals from other sources.
Watch this 3-min video to see how asset managers can leverage the Accern NoCodeNLP Platform to receive fast and accurate insights from text data to fuel timely investment decisions.
Use Case 2: NLP in the Banking Sector
There is so much more to banking than just bank account management. Banks have several roles to play from keeping your money safe to investing and paying you interest to providing loans.
While modern banking has embraced computer technology, there is still high demand for artificial intelligence in the banking sector.
Here are some ways in which NLP can help in banking.
With NLP, data can be collected from both historical results and the latest news to help asset managers at banks to create the right portfolio.
NLP can be used to go through the immense velocity and volume of information on the internet. NLP models can be used to identify information that is relevant to an asset manager’s investment thesis – for example, NLP can flag ESG-related articles for sustainable investment fund managers or monitor cryptocurrency related regulations for crypto sector fund managers or automatically filter out undesirable investment options.
NLP can then be used to monitor equities in a portfolio for risk or upside opportunities. This helps in maximizing the growth rate in what is an increasingly volatile and uncertain environment.
Lending Risk Assessment
When it comes to issuing commercial loans or lines of credits to companies, Banks have to perform due diligence on the company, it’s ability to pay back those loans. They have to assess their risk as well as comply with a variety of regulations including KYC.
This process requires a lot of manual work reviewing loan documents, financial statements and external factors, such as market conditions, industry dynamics, competitor actions and customer sentiment.
Here are three keys in which NLP can be helpful:
- NLP automates the initial tagging and scoring of internal documents and open source alternative data such as earnings calls, analyst reports and other news and reviews.
- Results from the NLP process can be used to route documents to different departments and to fuel additional risk scoring algorithms.
- Once a loan has been approved, banks have to continue to monitor the latest news. They can use NLP to automate updates to the risk ratings on their loan portfolio.
Watch this 3-min video to see how commercial banks can leverage the Accern NoCodeNLP Platform to receive fast and accurate insights on credit, liquidity, and lending risk.
Use Case 3: NLP in the Insurance Sector
With insurance policies, insurance claims, and other documents, the insurance industry is heavy with textual data. So much unstructured text data and even structured data cannot be analyzed efficiently by humans using manual techniques.
There is a great opportunity for insurance companies to leverage NLP to automate insurance processes and enhance operational efficiency.
Here are three key ways in which NLP can help in the insurance sector:
- Processing Insurance Claims
- Underwriting Insurance Policies
- Detecting Insurance Fraud
Processing Insurance Claims
One example of fintech NLP is in the use of chatbots to enhance customer satisfaction.
When customers file for a claim, their enquiries can be handled by chatbots that quickly give them answers based on their questions and this builds trust.
Chatbots can direct customers to submit written descriptions, photos and videos of the damage. All this can be handled quickly and without human interaction. When users get a quick response, they are happy to deal with the company.
Another example of fintech NLP is to first use Optical Recognition (OCR) to quickly read large documents and translate images of paper or scanned documents into text files.
These files are then further classified, categorized and scored using sentiment analysis. The time spent in reading and analyzing claims documents is cut short and processes are sped up so that claims adjusters can examine claims applications faster.
Underwriting Insurance Policies
The insurance underwriting process often requires customers to fill out a number of forms and attach related documents. It requires underwriters to assess risk by manually going through the documents making up the application and performing their own due diligence using alternative and open source data from news feeds and social media.
NLP can be used to quickly process both sets of documents, tag or categorize documents appropriately and even assign sentiment scores, for example to news and reviews.
Results of this pre-processing can be fed into predictive models and automated tagging and flagging of key elements in the application make it easier and faster for manual application reviewers.
Detecting Insurance Fraud
According to the FBI, 7,000 companies collect over $1 Trillion in premiums and non-health insurance fraud costs an estimated $40B per year. This puts a lot of pressure on insurance companies as they struggle to avoid fraud. As a result, customers need to pay higher premiums, and this increases costs for everyone.
NLP can be used to flag phrases or descriptions of losses that are repeated – the same words are used in multiple claims applications – a likely indicator of organized fraud. These applications can then be prioritized for deeper investigations.
How Accern Can Help
Whether you want to analyze the financial records and internal documents of a company for audit or compliance purposes or find red flags in loan applications, the Accern NoCodeNLP platform makes it easy to process text.
The best part of Accern solutions is that you don’t have to be an engineer or data scientist to use Accern. Our NLP software takes you through building an NLP workflow and seeing results on a dashboard with just a few clicks.
Schedule a demo to learn more about the Accern NoCodeNLP Platform and how it can drive business ROI for hedge funds, commercial banks, and insurance companies.Share this Post!