Natural language processing in finance
But as fintech industry investment trends make clear, advances in technology and routine exposure to human-like technology among consumers are making the uncanny valley less and less treacherous all of the time: AI-powered chatbots alone are projected to save 862 million hours in 2023 for financial institutions, so investing in and adopting AI-related fintech applications is a no-brainer.
That's where natural language processing (NLP – a form of artificial intelligence) comes in, with chatbots so smart and easy to interact with that they could be mistaken for a human operator, thus minimizing the uncanny valley's effect. The more comfortable clients feel about using conversational tech, the better it is for all parties involved in terms of time, money, and energy saved.
How is NLP used in finance?
Natural language processing software relies on neural network-based AI algorithms specialized in complex language analysis. NLP chatbots learn and evolve with each customer interaction through dynamic conversation, unlike previous generations of chatbots which depended upon pre-programmed, fully scripted talking points that limited their usefulness.
Bank of America, India's HDFC, and Turkey's Garanti BBVA are banking titans that have already deployed innovative NLP chatbots to service their customers. And that’s just the part the customers see, given that NLP-powered software has vast administrative applications in finance. Think for a moment about the technology's ability to sift through millions of documents in record time to uncover patterns and anomalies and how that only adds those millions of hours saved.
The numerous benefits of NLP in fintech
NLP-powered tools already have plenty to offer to the fintech industry, including:
Every time even the most basic chatbot resolves a customer issue without intervention from a flesh-and-blood agent, money is saved. But power workflow automation with NLP, and you've got an exponentially more valuable proposition: NLPs can gather data, the lifeblood of business today.
Advanced NLPs can detect a range of nuances in conversations, including mood and satisfaction levels, and then generate 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 evolving situation.
Smart search and document analysis
NLP-based management systems simplify administration by tackling the root of the problem: document generation itself. In insurance, for example, NLP software can lead clients through the claims process and generate a simple policy approval in minutes.
A properly-coded NLP solution will not only dramatically simplify and streamline document generation without needing human intervention, but also prepare the system to retrieve those documents whenever needed. NLP-based document analysis goes way beyond keyword-based indexing and can review vast databases, including interferences, implications, and connected material, by analyzing the language's structure to return comprehensive results.
Yet another bonus: When paired with optical character recognition technology, an NLP solution can analyze scanned and handwritten documents and even transform those documents into new, clean versions.
NLP technology can be employed as a risk management tool to improve the security, reliability, and data privacy of modern fintech businesses. An NLP offspring, Named Entity Recognition (NER), goes beyond the semantic meaning of words and can detect real-life concepts in text, like a specific person or company, even if that text is unstructured in images or spreadsheets.
Through NER, NLP software can effectively map the relationships between any stakeholders (or "entities"), compare them with its database, and instantly alert the involved parties if it detects something amiss.
The more practical data sources a financial institution can analyze, the better its risk assessment will be — and the more personalized credit underwriting and services become while minimizing potential biases such as race, age, or gender.
Conversational chatbots 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 geolocation-based travel history, to translate that data into an accurate credit score.
Voice recognition can be an important asset in security through speech-based user authentication, but where it truly shines, once again, is in data gathering.
For instance, NLP applications can go through company presentations and keynote addresses to automatically identify and catalog relevant information. Advanced NLPs may discern even hard-to-measure variables, like tone and mood, and profile those as text. Once in written form, this data can be analyzed — by humans or machines — to extract still more data that sheds light on an enterprise's performance.
Retail consumers also get value from voice recognition NLP algorithms: As the tech evolves and the general public gets used to it — 45 million consumers in the US have already had at least one experience with voice shopping — speech-based NLP chatbots are bound to become as common as the text-based ones are today.
NLP use cases in finance
Some of the most widespread use cases, current and near-future, for NLP in finance include:
Sales and CRM optimization
NLP is an excellent sales tool: Banks that use it, for example, have increased their customer engagement scores and customer acquisition.
Moreover, conversational AI can power customer relationship management (CRM) software, mitigating the need for manual entries and updates. Every bit of usable information from each interaction can be logged, parsed, and evaluated for patterns to surface. In turn, those patterns can notify financial institutions which regions are ripe for investment, what their most profitable sectors are, and how satisfied customers are with the financial services they receive.
Investment and trading applications
Beyond sales, NLP can be unleashed to conduct passive market research. Rather than commissioning expensive studies or pursuing customers with surveys that fail to engage them, with NLP, companies can gather data 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 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.
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 piece for The Guardian.
It doesn't stop there: NLP-based content creation further enhances personalization options. NLP software can be given a set of instructions (e.g., with The Guardian article, "Please write a short op-ed around 500 words. Keep the language simple and concise. Focus on why humans have nothing to fear from AI…") and also incorporate relevant data it has about the client receiving the content.
This could theoretically enable a whole new level of customized content at an enterprise-level for customers, such as identifying which wealth management prospects respond better to campaigns created in a formal language style, and which prefer their content casually delivered.
Customization is key
Despite all the advantages natural language processing technology offers, any powerful tool comes with associated issues. With NLPs, the financial sector must evaluate a risk: Machines can learn, but that doesn't mean that what they learn is perfectly sound.
For instance, when an algorithm is first being trained, developers feed it large amounts of data driven by the features they expect the artificial intelligence to encounter in real-world scenarios. However, if that data doesn't truly represent the experience, the AI might learn the wrong lessons — and the result might be undesirable. A model validation must scrutinize the training data for all variables to confirm that it matches what the AI can expect in reality.
AI engineering demands great attention to detail and a custom approach to ensure reliable results, but once it's properly set up and validated, the machine keeps learning from its successes.
If you're considering embarking on a project that calls for NLPs and looking for inspiration, here are some real use cases and solutions we've developed for our clients:
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.