Where does Artificial Intelligence fit in FinTech?
6 April 2020
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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
Better
Decision-Making
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.
Insurance
Management
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.
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