The Revolutionary evolution of AI and NLP in fintech
Elon Musk has often remarked on the limits of human-machine interfaces, and how this is currently the limiting factor in many technological advances. The financial tech industry couldn't agree more. The introduction of conversational artificial intelligence (AI), natural language processing (NLP) capabilities, and advanced chatbots is fundamentally transforming the banking industry in many ways.
How AI is currently being applied in the financial industry
The precursor to modern conversational banking technology was script-based chatbots. These were designed to facilitate customer access to commonly requested features and provide answers to frequently asked questions. During the honeymoon phase, traditional chatbots increased customer engagement, leads, and conversions, clearly indicating a desire for this kind of interaction. Unfortunately, customers soon grew frustrated with the limits of script-based tech, and satisfaction surveys conveyed that chatbots weren't living up to expectations.
Recently, AI has been paired with NLP to create a new dynamic: conversational chatbots that interact with customers dynamically. These algorithms permit the machine to learn from each customer interaction and discover which cues indicate the person's actual needs. Conversational AI actively evolves with each interaction. Bank of America ("Erica"), HDFC ("EVA"), and SEB in Sweden ("Aida") have successfully rolled out conversational chatbots that not only meet customer expectations but also exceed them.
However, AI is hardly limited to the front office. One of the back-office AI applications is found in the arena of contract analysis. The banking industry must comply with continually changing legal requirements and ensure they're applied accurately across millions of contracts and billions of transactions. The sheer amount of document review is intimidating. NLP-powered software can rapidly scan through millions of documents in little time, detecting trends and notifying bank personnel about anomalies or discrepancies.
The numerous benefits of AI and NLP in fintech
The possibility of cost savings and revenue generation is astonishing. The cost savings for financial institutions due to AI applications is projected to reach $447 billion by 2023. In terms of labor hours, conversational AI should save banks 862 million working hours in that time frame, or approximately half a million working years.
Beyond savings, McKinsey Global Institute projects that artificial intelligence has the potential to generate more than $250 billion in value across the banking industry. One area that stands to benefit most is risk management, particularly in credit underwriting and fraud prevention. Evaluating creditworthiness provides an excellent example of the raw power AI holds. A customer would call a bank and speak to a conversational chatbot, requesting a loan or credit card. The software would evaluate the individual's digital footprint rapidly, including social media profiles, browsing history, and even travel history, as recorded through geolocation. All this data would be converted into a credit score that would be exponentially more accurate than current algorithms.
Conversational AI use cases: The next evolutionary stage in fintech
One of the benefits of conversational AI is what happens on the back end, unseen by the customer. 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 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, this technology's market value is projected to reach an astounding $15 billion. NLP and conversational AI in financial services are going nowhere but up.
Arguably, the most important advantage this technology offers doesn't affect what it's currently doing, but where it can allow banks to go. Conversational AI offers a much more natural interface between humans and machines than typing does. It doesn't require any training, such as how to use a keyboard or operate a PC; it doesn't even need the customer to be able to read.
The only thing a human must know is how to speak in his or her language. NLP can be applied in multiple dialects, opening banking opportunities to the "next billion users" that comprise the non-banked portion of the world's population. These people often lack formal education, live in rural areas, and have never opened a checking or savings account. Increasing market access to that degree represents an unprecedented leap forward.
eChatbots use cases: The bright future in fintech goes without saying
By 2023, the fintech industry expects a 3,150% growth in chatbot interactions. Researchers estimate that 95% of all client interactions in the banking arena will be conducted by artificial intelligence within the next 5-10 years.
Traditional, script-based chatbots were limited to essentially acting as an advanced site map. Conversational chatbots go far beyond this, and a clear example is in the area of insurance claims processing and policy approval. Conversational AI can lead clients through the claims process, often generating a simple claim approval in less than a minute. Regarding policy application, next-gen chatbots interact with customers and suggest targeted policies specifically suited to that individual's needs. The cost savings in terms of decreased working hours are impressive, but arguably more so is the opportunity for banks to increase sales with tremendously accurate policy recommendations and risk analysis.
Conversational AI is an excellent sales tool that has already clearly demonstrated the advantages it provides. Banks that employ these types of chatbots have been able to increase their customer engagement scores, driving higher numbers of leads and, most importantly, conversions.
The opportunity to conduct passive market research shouldn't be underestimated. Rather than commissioning expensive qualitative and quantitative studies or pursuing customers with surveys that produce consistently low engagement rates, data 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.
NLP use cases: A surefire way to beat the competition in fintech
NLP for fintech affects the industry positively in two primary ways. The first is in front-office customer interactions. Rather than relying upon a fixed, script-based response, NLP uses efficient voice-to-text and text-to-voice conversions, which allow a computer to understand dialects, accents, and phrasing. This creates a natural, intuitive experience for the customer: needs are met, expectations are exceeded, and targeted service is delivered rapidly.
The other area where NLP for finance is a natural fit is in a back-office application. NLP-enabled computers can review a vast database of information and go far beyond searching for keywords or phrases. It can also search for inferences, implications, and connected material by analyzing the language's structure, returning comprehensive results. When paired with optical character recognition technology, the system can also analyze scanned and handwritten documents, including those in its analysis.
An ounce of preparation is worth a pound of cure
Despite all the advantages conversational AI and NLP offer, any powerful tool comes with associated risks. With AI, financial institutions must evaluate two primary areas. The first revolves around how artificial intelligence learns. When an algorithm is first being trained, a model developer feeds it large amounts of data driven by features it will encounter. However, if the data isn't representative of the expected experience, the AI might learn the wrong lessons, creating undesirable outputs. A model validator must scrutinize the training data for all variables to confirm that it matches what AI can expect in real-world scenarios.
The banking industry also deals with substantial government oversight. To ensure that mortgages, credit applications, and other loans are issued lawfully, auditors must examine the decision-making process to confirm that the bank is abiding by state and federal regulations. In this case, the evolving nature of AI can become a problem. AI doesn't use a fixed set of programmed variables; it adjusts its criteria and processes based on its experiences. This can result in a situation where bank personnel is asked about why someone was turned down for a loan, and the honest answer is, "We don't really know." Having a firm grasp on how and why AI arrives at its decisions calls for regular internal audits and human oversight to ensure legal compliance.
Conversational AI and NLP offer unprecedented opportunities for increased efficiency and growth. Properly leveraging these technologies can allow banks to raise service standards, passively gain increased market insights, and efficiently ensure regulatory compliance. Some areas need to be carefully observed and managed, but these technologies promise a bright future in fintech.