With constantly improving machine learning algorithms and computational skills, the significance of NLP in finance has become much more reliable and scalable. NLP tools, technologies, and APIs (Application Programming Interfaces) are now ubiquitous in all sectors, especially in the financial realm.
More and more businesses and industries around the world are vouching for the benefits of NLP in finance, and the stats also reflect this. For instance, according to Mordor Intelligence:
The market value of AI in fintech was USD 7.91 billion in 2020. It is expected to surpass USD 26.67 billion by 2026, boasting a CAGR (compound annual growth rate) of 23.17%.
What is NLP in Finance?
The financial industry relies on data to predict economic changes and market movements. NLP helps the financial sector analyze text data, opening a new front line for asset managers, central banks, and financial professionals. Basically, this technology allows companies to gain insights from written information such as ads, social media, news articles, and reports.
Natural language processing tech counts on neural network-based artificial intelligence algorithms specializing in complex language analysis. NLP chatbots learn and evolve with each user interaction through dynamic conversations, unlike previous generations of chatbots that relied on pre-programmed, entirely scripted discussion points that limited their usability.
Bank of America, HDFC of India, and Garanti BBVA Turkey are banking titans that have already implemented innovative NLP chatbots for their customers.
Interestingly, this is the only area of natural language processing in finance visible to customers because NLP-powered software also has huge administrative applications in the financial world. Just think of the ability of technology to scan millions of documents in record time for patterns and anomalies and how it efficiently contributes to saving millions of hours of human labor.
Benefits of NLP in the Finance Sector
Tools that use NLP already have a lot to offer the fintech industry, including:
1. Workflow Automation
Money is saved every time the simplest chatbot solves the user’s problem without needing a physical agent. Now imagine powering workflow automation with natural language processing, and envision the kind of value proposition it can bring along.
Advanced NLP can detect a range of nuances in conversations, including mood and satisfaction level, and then generate emotion analysis. At the same time, this information can be combined into a customer profile to provide personalized financial services, products, and promotions catering to changing customer needs.
2. Intelligent Search and Document Analysis
NLP-driven management systems identify and simplify document handling by solving the root of the problem, i.e., document generation. For example, in the insurance industry, NLP software can guide customers through the claims process and generate a simple policy approval in minutes.
A correctly coded NLP solution will drastically simplify document generation without human intervention and prepare the system to retrieve those documents when needed.
NLP-based document analysis goes far beyond keyword-based indexing and can evaluate massive databases, including interference, implication, and related material, using linguistic structure analysis to provide complete results.
Moreover, combined with optical character recognition (OCR) technology, an NLP solution can analyze scanned and handwritten documents and even convert these documents into new and clean versions.
3. Streamlined Credit Scoring
The more substantial data sources a financial institution can analyze, the better its risk assessment will be. It also means improved personalized credit underwriting while minimizing potential biases like age, gender, and race.
Conversational chatbots can quickly assess a person’s lending or credit card application by reviewing their credentials. They may include fingerprint records, social media profiles, browser history, and travel-based history, interpreting this data into a precise credit score.
4. Fraud Detection and Prevention
NLP technology can be used as a risk management tool to augment the security, reliability, and data protection of modern fintech companies. A descendant of NLP, Named Entity Recognition (NER), goes beyond the semantic meaning of words and can detect actual concepts in the text, such as a specific person or company, even if it encounters unstructured text in images and spreadsheets.
Employing NER, NLP software can efficiently map relationships between all stakeholders (or “entities”), compare them with a database, and immediately alert stakeholders if something is wrong.
5. Voice Recognition
While voice recognition serves as a viable security asset, it surprises us in data gathering. For example, AI-based voice recognition applications can automatically search company presentations and titles to identify and catalog relevant information. Advanced NLP can also distinguish difficult-to-measure variables such as tone and mood, profiling those as text.
Once this data is recorded, it can be analyzed – by humans or by machines, lending even more data that highlights the company’s performance.
Retail consumers also receive value from NLP voice recognition algorithms with evolving technology and increasing awareness.
According to Statista, the global voice recognition tech market reached nearly 10 billion U.S. dollars in 2020 and is expected to grow to almost 30 billion U.S. dollars in 2026.
Top NLP Use Cases in Finance
Some of the most common NLP use cases in finance, now and in the foreseeable future, are:
1. Sales and CRM Optimization
NLP is a great sales tool. For example, banks using it have increased customer engagement and acquisition.
In addition, Conversational AI can power customer relationship management (CRM) software, eliminating the need for manual input and updates. Each piece of helpful information from each interaction can be captured, analyzed, and evaluated to identify patterns.
In turn, these models can inform financial institutions about which regions are most mature for investment, their most profitable sectors, and how satisfied customers are with the financial services they receive.
2. Investment and Trading Applications
In addition to sales, NLP can also be used for passive market research. Instead of conducting costly surveys or harassing customers with surveys that don’t incentivize them, NLP allows companies to collect data with every call.
The database can be searched for that specific data if a particular type of information is required. The algorithm quickly analyzes your call history and creates a detailed report.
It would be justified to say that a quick and accurate data review is never wrong when it comes to investment decisions.
3. Content Marketing Creation
When an NLP-powered chatbot is intelligent enough to speak fluently to humans, the neural network doesn’t have to make a giant leap to create marketable content. Simple automated newsletters are already a reality in content creation.
AI technology requires a lot of attention to detail and a customized approach to ensure reliable results, but once correctly configured and validated, the model continues to learn from its successes.
Top NLP Applications in Finance
Every passing day brings forth exciting and beneficial applications of natural language processing. Be it NLP in healthcare, education, marketing, or HR, NLP continues to make our businesses and lives convenient. That’s why we have compiled a collection of top NLP applications for you.
Solvency is a natural and obvious use of machine learning for process automation. For decades, banks have used rudimentary logistic regression models with inputs such as 30-60-90-day payment history to determine the probability of default or the payment and interest terms of a loan.
The logistic model can be problematic because it can penalize individuals with shorter credit histories or those working outside traditional banking systems. Banks also lack additional sources of revenue from rejected borrowers who would likely be able to pay.
With the increasing number of alternative data points on individuals related to their financial history (such as rent and utility bill payments or social media actions), lenders are able to use more resources to make more personalized credit decisions.
Despite optimism around increased fairness for a broader customer base for banks, there is still some apprehension about using black-box algorithms to make lending decisions. Regulations, including the Fair Credit Reporting Act, require creditors to give individuals specific reasons for a result.
However, when implemented well, credit-determining algorithms can provide a considerable advantage to financial institutions while simultaneously improving the customer experience.
2. Detection and Prevention of Financial Crimes
Financial crime prevention is another NLP application for process automation with growing demand. With the introduction of natural language processing algorithms, organizations can now improve financial crime detection with sophisticated models instead of manual processes. It also results in reducing processing latency to catch problems faster. The main methods include clustering and classification.
How does it work? Let’s take fraud detection as a key example.
Most fraud prevention models rely on a set of human-created rules that result in a binary classification of “fraud” or “no fraud.”
There is a problem with these patterns. They can create a high number of false positives. It’s terrible for businesses when customers receive an unusually high number of unnecessary fraud notifications. Trust is lost, and actual fraud can continue to proceed undetected.
NLP clustering and classification algorithms can help reduce the problem of false positives. They continually change a customer’s profile each time they take a new action. With these multiple data points, the machine can take a nuanced approach to differentiate between normal and abnormal behavior.
By leveraging a modern natural language processing strategy, they can go from traditional batch processing, which can take days or weeks, to real-time scoring to get results in seconds.
3. Algorithmic Trading
Algorithmic trading is automated trading using a defined set of rules. A basic example would be a trader setting up automatic buy and sell rules when a stock falls below or rises above a particular price level.
More sophisticated algorithms exploit arbitrage opportunities or predict stock price fluctuations based on real-world events such as mergers or regulatory approvals.
The conventional models require thousands of lines of human-written code and have become increasingly unwieldy. Resorting to NLP models makes trading more efficient and less error-prone.
It is particularly advantageous in high-frequency trading when large volumes of orders need to be placed as quickly as possible.
Automated trading has been around since the 1970s, but it’s only recently that companies have had access to the technological capabilities that can handle advanced algorithms. Many banks are investing heavily in machine learning-based trading.
4. Customer Experience Innovations
Many businesses are realizing the potential of machine learning to improve customer experience, helping them attract, convert, engage and retain more customers more effectively. Chatbots and robo-advisors are just a few of the many ways they are innovating.
Chatbots are a common NLP application, but that doesn’t make them any less effective. Chatbots serve as a great example of natural language processing and sentiment analysis algorithms at work, and they offer an efficient way to help customers faster and more efficiently.
Another strategy for using ML to improve customer experience that can leverage similar NLP techniques is to augment customer service agents. By understanding customer behavior and sentiment over time, NLP-powered customer agents can better help solve their problems and offer new products and services that better match the customer’s profile.
Robo-advisors have made investment and financial decision-making more accessible to the average person. Their investment strategies are derived from an algorithm based on the client’s age, income, expected retirement date, financial goals, and risk tolerance.
They generally follow traditional investment strategies and asset allocation based on this information. Because robo-advisors automate processes, they also eliminate the conflict of financial advisors who don’t always work in the client’s best interest.
5. Cybersecurity and Threat Detection
While not unique to the financial services industry, robust cybersecurity protocols are vital to demonstrating asset security to customers. Leveraging anomaly detection on massive datasets is a job for which NLP is particularly suited. Traditional analytical systems are simply not designed to meet the real-time requirements of modern information security to deal with an ever-growing threat landscape. Specific examples of how machine learning is used in cybersecurity include: