By now the global FinTech ecosystem has accepted several innovative directions for the development and enhancement of FinTech as an industry, and Artificial Intelligence (AI) might be the most adopted concept as of yet, even if its implementation still needs fine-tuning and optimization.
Its benefits are clear; AI solves human problems by utilizing methods that are derived from human intelligence and increasing these methods’ efficiency and output. It achieves this by saving time, effort and human intervention efforts across a wide array of automated services. Emerging technologies like Machine Learning (ML), Artificial Intelligence (AI), Neural Networks and Big Data Analytics have enabled computers to crunch and analyze huge and diversified datasets like never before.
It’s ironic that the pre-digital-age banking industry used to boast about having personal connections with their customers and be proud of the fact that they provide personalized support to any customer in need of it, and then the digital age came to take away that aspect of personal care. The irony is that AI as a concept actually uses more technology to bridge that gap of lost personalized attention and human-to-human interaction, by providing efficient customer support and problem-solving skills that no human could achieve in so little time; a perfect example is Chatbots that are used in customer support to achieve quick and consistent customer satisfaction.
Let’s define the specific areas in which AI can greatly enhance FinTech products & services.
Potential AI Use Cases in FinTech
Top-level management decisions are made safer & easier when they are data-driven; add lower costs to these decisions and you have a winning formula. AI helps empower these decisions based on agreed-upon processes and robust architectures, allowing top management to use their time in other areas more efficiently. In the future, questions will be asked to intelligent machines, rather than humans, and in turn will analyze the data fed to them and return with proper, reliable answers in little to no time. This will guarantee faster and more efficient decision-making across the board.
Automated Customer Support
Customer support has always been a focus of banks and financial institutions in order to set themselves apart from the competition. Customers are always looking to get fast, reliable answers on demand, and AI-based software like Chatbots or Voice assistants do just that. Standard inquiries by customers are answered almost instantaneously, while more complex questions are also slowly but surely being handled by automation software, resulting in saving time & money for FinTech companies while keeping customers satisfied at most levels. AI today is still being supported by humans when needed, but the future of customer service will definitely be fully automated at some point.
Fraud Detection & Claims Management
Perhaps one of the most tedious aspects of digital banking, fraud detection and claims management are as essential to any tech platform as they are time-consuming; but much less so with AI. While analytical tools are used to collect evidence and break down data needed for conviction, AI tools teach themselves and learn user behavioral patterns to identify warning signs and flag suspicious activity like fraud and theft attempts.
Machine Learning (ML) can greatly help out in various stages of claim handling processes, like fastening certain claims and reducing the processing time and cost of claim management. Over time, AI will learn to adapt to new undiscovered cases and increase detection capabilities.
Automation of the underwriting process and the transformation of crude data into meaningful information can be handled by smart AI processes. AI can also gather insurance requirements from users online, speeding up the process and eliminating expensive tests. Since insurance is usually triggered after the loss has occurred, AI can help prevent that by better detecting risks and diseases to prevent them from occurring in the firsts help place, through extensive data collection and complicated algorithms. This data greatly helps in lowering the probability of damages to the insured and thus helping out the insurer as well.
Automated Virtual Financial Assistants
Virtual financial assistants based on automation can really help out users to make important financial decisions, including event monitoring like stock and bond price fluctuations and trends. According to users’ financial goals and portfolios, recommendations can be delivered for buying & selling stocks or bonds. These ‘Robo-advisors”, as they are known, are being provided more and more by financial institutions and FinTech startups alike.
Predictive Analysis in Financial Services
Predictive analytics in financial services is a crucial part of any FI’s strategy, affecting revenue generation, resource optimization and sales. It can help deliver personalized experiences, calculate credit scores and help prevent risky loans. Analytical processes gather data from a wide range of sectors and organize this data with state-of-the-art algorithms and deep data analysis to provide customized solutions that are unique to every customer.
Wealth Management for the Masses
Wealth management advisory services are offered to lower net worth market segments, leading to the provision of lower fee-based commissions. When AI is applied to smart wallets and digital wealth services, the result is a self-learning algorithm that monitors and learns from user behavior to guide users on personal finance management like spending to save their expenses and ultimately their wealth.
The potential benefits of applying Artificial Intelligence are becoming clearer as the medium progresses. High automation levels are taking over and machines are slowly enhancing basic processes already. FinTech is no exception, with a thriving industry that is hungry for reliability, ease of access and speed waiting to see what’s next. This entails that FinTech companies should adjust their strategies to better encompass AI and its corresponding requirements, in anticipation of machine-leveraged future.