In business, when a trend is forecast to grow by more than 3000% and generate cost savings of $7.3 billion globally over a five-year period, it gets noticed. Those numbers represent the projected growth of chatbot interactions among banking customers between 2019 to 2023 and the cost savings from 862 hours less work by support personnel, according to research by Juniper Research. The same study estimated that chatbots would lead to $1.3 billion in cost savings for the insurance industry as well during the same period.
Chatbots are just one application of natural language processing (NLP), a type of artificial intelligence (AI) that is already having a major impact in financial services, among other industries.
Why NLP? Why Now?
NLP became a subfield of linguistics, computer science, and AI more than 50 years ago. But only in recent years, with the growth of the web, cloud computing, hyperscale data centers, machine learning, neural networks, deep learning, and powerful servers with blazing fast processors, has it been possible for NLP algorithms to thrive in business environments. Human-machine interactions are now commonplace, from queries to Siri or Alexa to voice verification and call routing, text autocomplete, and language translation.
For brokers, insurance companies, fintech firms, and other services organizations, NLP is banks, financial being seen as a solution to too much data and too few employees. Aside from handling simple customer service queries or routing customers to the right department, it’s being used to uncover bias and fraud, ensure stringent regulations are adhered to, and to provide competitive differentiation.
An Industry Redefining Itself
The move to remote work and the surge in online everything during the COVID-19 pandemic have led many companies that provide financial services to rethink their business models to accommodate the changing needs of employees and customers. Even before the pandemic, the financial services industry was being disrupted by fintech companies with mobile applications and technologies like Blockchain and cryptocurrency. While each firm’s situation and market challenges may be unique, a majority see AI as a vital tool they can’t afford to ignore. Research by the Economist Intelligence Unit found that 86% of financial services firms plan to increase their AI-related investments through 2025.
NLP is expected to dominate the projected $120 billion in yearly investment in AI in the US by 2025, according to IDC. The Financial Services industry is projected to be a major source of this spending. NLP will account for $35.1 billion of global investments in AI by 2026, according to Markets and Markets.
Putting NLP to Work
NLP solutions can be used to analyze the mountains of structured and unstructured data within companies. In large financial services organizations, this data includes everything from earnings reports to projections, contracts, social media, marketing, and investments. NLP solutions comb through the voice and transcription data to provide actionable insights, help assess risk, better understand competitors, comply with regulatory requirements, and much more.
Banks are using NLP to automate commercial loan applications, with some companies reporting that the technology has allowed them to trim human workloads for the process by up to 60%. As one of the most heavily regulated industries, financial services teams are also using NLP to speed up routine operations that are part of compliance, such as information gathering and reporting.
Another use of NLP tools is to investigate and combat fraud. One example is the ability to identify words or phrases used by malware bots. While most red flag warnings turn out to be false positives, all must be investigated and NLP automates the process.
By using NLP to handle low-level tasks, such as routing callers to the right department and obtaining their name, account information, and reason for calling, firms can free up personnel to have more time-efficient and more personalized interactions when they engage directly with customers.
Yet another focus is competitiveness. A new cohort of technology firms and fintech startups that rely heavily on mobile, cloud, and software features―along with some of the largest and established technology companies like Google and Apple―are looking to disrupt the financial services industry. Using NLP, an analyst can get details of an earnings report long before the data makes its way into a database of a data provider that must then structure it. That’s an example of NLP providing a potentially significant competitive advantage.
From automating manual processes to turning unstructured data into more usable form, NLP has emerged as an indispensable tool in the complex, fast-moving, highly competitive world of services. As Mikey Shulman of MIT’s Sloan School of Management said of NLP, “As more and more people see it work and understand the lingo, they see that it’s not a dark art―it’s math.”
For more on the power of AI for financial services, read “How NLP Helps Financial Service Companies Overcome 7 of Their Biggest Challengesin the Dell Technologies Perspective Series.
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