Moving Beyond the Hype: Six Real-Life Applications of AI in FinTech
Artificial intelligence (AI) may well be at the peak of its hype cycle now. The World Economic Forum dubbed AI — and a suit of other fast-evolving novel technologies — a herald of the Fourth Industrial Revolution, acknowledging the tech’s potential to drive transformative changes across economies and impact society at scale.
From advanced computational power to understanding natural language to recognizing visual objects, AI brings a multitude of capabilities and augments human intelligence. Industries are leveraging AI to automate complex workflows, create new efficiencies, surface underlying patterns, and support decision making.
As an industry that relies heavily on technology and innovation, FinTech became one of the first to move past the buzz and start reaping real-world benefits brought by artificial intelligence. In this article, we’ll explore six practical ways how forward-thinking financial companies can implement AI tools and get a jump on the competition.
Better Customer Service
Customer centricity is not only a growing trend but
a winning strategy for businesses that try to attract and retain prospects, foster loyalty, and improve their bottom line. And a customer-focused approach gains even more prominence as tech-savvy Millennials and Generation Z flood the consumer market craving convenient, accessible, and customized products.
To better serve their customers, FinTech companies employ a new type of assistants — conversational chatbots. Powered by AI, these tools can easily automate repetitive tasks, provide 24/7 customer support, and instantly inform users on their balance, credit limit, or transaction history — all without human intervention.
To further enhance customer satisfaction, AI chatbots leverage Natural Language Processing (NLP). This tech enables chatbots to understand the sentiment — tone and emotions — behind a user’s input and to tailor their response accordingly.
Another way that AI helps financial firms put their customers at the heart of their business is through personalization. By leveraging the tech, financial service providers are able to shift from “one-size-fits-all” products to personalized offers aligned with customers’ needs and expectations.
To hit a customer’s sweet spot with a tailored product, FinTech firms invest in powerful machine learning solutions that automatically collect and analyze loads of data including financial transactions and account details, real-time and historical market data, as well as nuggets of information mined from social media for a 360-degree view of a prospect.
Based on this input, AI-powered tools recognize behavior patterns and return clues in the form of actionable insights and highly accurate predictions. Suppose your users do grocery shopping twice a month — a smart virtual financial assistant can send them timely personalized offers with discounts and promo campaigns to save money. Or, if a user sets a savings goal, develop a personal spending plan based on a user’s budget, which would not send them into overdraft.
Informed Wealth Management
In a $79 trillion wealth management industry, artificial intelligence is gaining traction as both wealth advisors and tech-savvy investors acknowledge AI’s potential in delivering better business results. In fact, 99% of wealth managers are planning to implement AI in the next three years, and 84% of high-net-worth individuals (HNWI) are very optimistic about the technology.
With hundreds of accounts under management, a wealth advisor might find it difficult not to lose human touch with their clients through all the repetitive, time-consuming operations. AI does not only take mundane tasks off a wealth manager’s hands but also delivers data-driven insights tied directly to a client’s goals. AI-powered algorithms identify market trends, monitor stock prices, recognize correlations, and empower wealth advisors with optimized recommendations for risk management and asset allocation.
Another example of implementing AI in wealth management services is robo-advisors. With higher availability and fees lower than those of a financial advisor, these AI-supported solutions open up investment opportunities to larger population groups. Robo-advisors help construct, manage and optimize an investment portfolio in line with a client’s needs and risk tolerance.
AI capabilities to unlock insights and inform decisions underpin transformations in the insurance sector. In particular, insurers tap into the power of machine learning to streamline the underwriting process and better serve their clients. ML-enabled solutions analyze thousands of data points from diverse sources and deliver accurate risk assessment, resulting in intelligent and automated underwriting.
This intelligent automation is also bringing new efficiencies to сlaim settlement. Tractable has rolled out its new AI solution for motor claims handling, which leverages deep learning and computer vision to analyze a policyholder’s pictures, assess vehicle damage and provide a repair estimate — in seconds.
Ubiquitous connectivity and the Internet of Things encourage insurers across industries to diversify their offerings with usage-based insurance plans. In health insurance, client data collected from fitness trackers and smartphones enable insurance service providers to fine-tune their pricing policies and allow people with healthier lifestyles to pay less.
Global Financial Inclusion
The World Bank estimates that globally 1.7 billion adults have no bank account and, as a result, no access to basic banking products — credit, savings, insurance, or money transfer services. Excluded from the financial system, the unbanked households struggle to take control over their financial health.
The financial sector is embracing AI and machine learning to bring the underserved segment back into the economy. The proliferation of mobile phones enables FinTech firms to use both traditional and non-traditional data to determine a user’s creditworthiness.
One example includes Lenddo’s sophisticated ML algorithms that analyze a person’s digital footprint — social media, browsing history, online transactions, etc. — and deliver a credit score. By using these scores, financial institutions can distinguish good and bad borrowers and significantly improve their loan approval rate.
Better Fraud Detection
Machine learning technology lends itself well to detecting fraud — another pressing issue for financial institutions. According to The Nilson Report, by 2020 worldwide losses from payment card fraud will amount to $35,4 billion.
To stay on top of any fraudulent activity, banks are increasingly adding ML solutions to their arsenal. The pre-trained algorithms analyze a multitude of data points and compare the result against global sets of fraud data and an individual’s historical data to spot any outlier activity in real time. Likewise, insurance companies boost their toolkit with advanced AI tools to detect fake claims that cost industry billions of dollars.
The buzz around artificial intelligence has been going on for decades now. And while the technology still has a lot of maturing to do, it has advanced to the point of delivering real-world value. In a data-intensive financial sector, AI emerges as a key enabler of a front-to-back digital transformation as FinTech companies leverage the tech to enhance customer service with personalized offerings, automate business operations, inform decision making, and combat fraud.
About the author: Olga Ezzheva is a Technology Writer at www.oxagile.com, a provider of software engineering and IT consulting services for SMEs and Fortune 500 companies alike. You can reach Olga at [email protected] or connect via LinkedIn.