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The unprecedented revolution of NLP in finance

Trespassing the uncanny valley — that weird feeling we have when experiencing something that appears to be human, like photorealistic CGI characters — is a common occurrence for any technology attempting to mimic human behavior. And as anyone who has ever dealt with a poorly programmed virtual assistant knows, business-oriented chatbots aren’t an exception.

The fintech industry is well aware of this phenomenon, as they should: AI-related applications are projected to reach $447 billion by 2023 in operational cost savings for financial institutions. Chatbots currently save banks hundreds of millions of working hours every year, so investing in and adopting conversational tech is a no-brainer. The more comfortable clients feel about using them, the better.

Enter natural language processing (NLP). If for decades, the chatbots of yesteryear were limited to pre-programmed, fully scripted talking points that left no space for deviations (or doubts about their chatbotness), today a good NLP application might already legitimately pass as a human. Their increasingly complex, neural network-based AI algorithms allow for dynamic conversations, where the machine learns and evolves with each customer interaction.

Yet again, fintech knows it — as we do. Bank of America, HDFC, and Sweden’s SEB, to name a few, all have deployed NLP chatbots that surpassed customer expectations. And that’s just the part we see: NLP-powered software has vast administrative applications in fintech, being able to sift through millions of documents in record time to search for trends and discrepancies. Those working hours saved keep piling up.

And so do benefits. Let’s have an in-depth look as to where and how NLP technology can be tapped to provide invaluable competitive advantages for fintech companies willing to embrace it.

What's inside

The numerous benefits of NLP in fintech

Beyond savings, McKinsey Global Institute projects that NLP-powered tech has the potential to generate more than $250 billion in value across the banking industry. Here are some of the areas in fintech that stand to benefit the most.

Workflow automation

In its most basic iteration, every time chatbots solve a customer’s request without resorting to a flesh-and-bone operator, bingo. NLPs’ workflow automation, however, goes way beyond that: they also function as an important data gathering tool.

Advanced NLPs can detect a range of nuances in conversations, including mood, satisfaction levels, and overall sentiment analysis. Over time, this information can be consolidated into a customer’s profile to enable personalized financial services, products, and promotions that reflect that customer’s current situation.

Fraud prevention

Security, reliability, and data privacy are paramount to any fintech business. An area that stands to benefit the most from NLPs is risk management, particularly in credit underwriting and fraud prevention. A conversational chatbot can quickly evaluate a customer’s loan or credit card request by checking the individual’s digital footprint, including social media profiles, browsing history, and even geolocation-based travel history, and translate that data into an accurate credit score.

Moreover, a subset of NLP technology, named entity recognition (NER), goes beyond the semantic meaning of words and can detect real-life concepts, like a specific person or company, in-text — even if unstructured in a spreadsheet.

With this, NLP chatbots can effectively map the relationships between any stakeholders (or “entities”), compare them with their own database, and instantly sound an alert to the involved parties if it detects something amiss.

Voice recognition

Voice recognition can be an important asset in security through speech-based user authentication, but where it truly shines is, once again, in data gathering.

A huge part of the financial market is reactive to sudden announcements, news, global events, and politics in general. NLP-powered solutions can not only read, but listen to and analyze news, reports, and releases as they happen, and course-correct financial advice on the fly to both human and automated professional services.

HR management

On top of automating cumbersome, repetitive administrative tasks that no human ever loves working on, NLP-based systems play double duty in improving your employees’ office life by being able to better identify their states of mind.

Stress and burnout, for instance, are an inherent part of the corporate world. An NLP software can detect those and other traits in surveys and employee feedback for a better understanding of how your teams feel.

The same applies to recruitment: NLP systems can serve as a tool to help evaluate potential employees by generating insights that might have eluded human managers.

NLP use cases in finance

Let’s have a look into some practical use cases, current or in the near future, for NLPs in finance.

NLP use cases

Conversational AI

As aforementioned, a key benefit of NLPs is what happens on the back end, unseen by the customer. For instance, conversational AI can power customer relationship management (CRM) software, mitigating the need for manual entries and updates.

Every bit of usable information from each conversation is logged, parsed, and evaluated for trends. This can be used to notify financial institutions about who their competitors are, which regions are ripe for investment, which are their most profitable sectors, and how satisfied customers are with the financial services they receive.

In 2018, the market share for conversational AI tech was $3.2 billion. In 2019, that increased to $4.2 billion. By the end of 2024, the tech’s market value is projected to reach an astounding $15 billion. NLP for finance is going nowhere but up.

Intelligent document management

NLP-based management systems simplify administration by tackling the root of the problem: document generation itself.

A clear example is the area of insurance claims processing and policy approval. NLP software can lead clients through the claims process, often generating a simple claim approval in less than a minute. A properly-coded NLP solution will not only entirely simplify and streamline document generation — thus facilitating management — without needing human hands, but also prepare the land for whenever those documents need to be retrieved.

Smart search and document analysis

NLP-based document analysis goes way beyond mere keyword-based indexing. NLP-enabled computers can review a vast database of information, including interferences, implications, and connected material by analyzing the language’s structure to return comprehensive results.

Furthermore, when paired with optical character recognition technology, the system can also analyze scanned and handwritten documents. If desired, the system can even translate those documents into a new, streamlined version.

Content marketing creation

When an NLP-powered chatbot is savvy enough to talk to humans fluently, it doesn’t take a huge leap for that neural network to start creating marketable content.

Simple automated newsletters already are a reality in content marketing creation. Engaging, fully coherent articles are more of a challenge, but they are coming fast — as demonstrated by this AI-written article for UK’s The Guardian.

More importantly, NLP-based content creation further enhances personalization options. An NLP software can be given a set of instructions (“Write 3 paragraphs about 40 words each”), and also be told to utilize relevant data it knows about the client that will receive the content. This enables a whole new level of customized content at an enterprise-level scale for customers.

Investment and trading applications

NLP is an excellent sales tool that has already demonstrated the advantages it provides. For instance, banks that employ it have been able to increase their customer engagement scores and drive a higher number of leads — and, most importantly, conversions.

Beyond that, an aspect of NLP that can’t be underestimated here is the opportunity to conduct passive market research. Rather than commissioning expensive studies or pursuing customers with surveys that produce consistently low engagement rates, data instead is gathered with every call. If a specific type of information is needed, the database can be queried for that particular data — the algorithm will rapidly parse through its call history and produce a detailed report in a short period.

Suffice to say, having a lightning-fast, accurate assessment of data when it comes to investment decisions never hurts.

iTechArt’s experience

If you’re considering embarking on a project that calls for NLPs and looking for inspiration, here are some works we’ve developed for our partners:

For MSB.ai, an engineering workflow automation platform, we built an automatic system for machine learning training and a programmable synthesis model that required NLP to function.

With Equeum, the global platform that supports the financial industry with content creation, our developers paired machine learning with neural networks to build a predictive tool to power an AI platform for stock ticker index analysis.

And for Kids Academy, an edtech startup that develops mobile content to help kids learn better, we crafted neural networks that automated the analysis of the kids’ completed exercises.

An ounce of preparation is worth a pound of cure

Despite all the advantages natural language processing technology offers, any powerful tool comes with associated risks. With NLPs, the financial sector must evaluate a fundamental aspect: how machines learn.

When an algorithm is first being trained, a model developer feeds it large amounts of data, driven by the features it will encounter. However, if the data isn’t representative of the expected experience, the AI might learn the wrong lessons — and create undesirable outcomes. A model validation must scrutinize the training data for all variables to confirm that it matches what the AI can expect in real-world scenarios.

That said, NLPs offer unprecedented opportunities for increased efficiency and growth. Properly leveraging the technology for finance allows the industry to raise service standards, passively gain increased market insights, and efficiently ensure regulatory compliance.

In the end, fintech’s future is promised to be bright, smart, and quite talkative.

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