Computing power has grown exponentially in the last decade, unprecedented quantum of data is generated in each passing second, and some of the big boys of technology are already leveraging this to make people’s lives better by redefining how we communicate (chatbots, Spam filter), how we travel (GPS, self-driving cars), how we live and heal (medical imaging, painless diagnostics) but when it comes to FinTech, usage of emerging technology is a double-edged sword. Not using tech makes a brand fuddy-duddy, hasty implementations of it earns customers’ wrath.
Is the lure and promise of Deep Tech like AI and ML a passing fad or can it redefine how people deal with money? Here’s a perspective.
Deep Tech, state of the union in FinTech
The biggest usage of technology in FinTech happens to the data processing and not necessarily in monetary transactions. From hastening KYC documentations to presenting smart bank statements to customer support through smarter chatbots, Deep Tech is permeating slowing in entire BFSI industry; but when it comes to money, core banking software seems to be the last big thing that happened; many banks and insurance players are still are in legacy systems built in the mainframe era, they have miles to go before they match the auto industry who’s trying to ply driverless cars within this decade.
While banks may be left behind, FinTech startups in the space of alternate lending are harnessing the full potential of Deep Tech. Startups like Flexiloans underwrite and discount negotiable instruments (like a Purchase Order) and lend unsecured loans based on their proprietary alternate lending model built on cutting edge technologies.
In this context, Rajat Deshpande, the CEO and Co-founder of Finbox said, “In the context of FinTech there has been major excitement on part of our customers to try and adopt the AI, Deep Tech to solve some important problems such as customer identification, fraud and most importantly underwriting. Are we “there” yet? Not really, AI remains the highest potential solution to these problems, but we can hardly consider these problems solved. For example, Face recognition tries to solve the customer Identification problem in a way, but it is easy to fool. Also there is regulatory uncertainty, how is fraud defined when you game the system using AI to identify you? AI holds the potential to solve multiple FinTech use cases, but it is yet far away from the kind of accuracy that is needed to consider these use cases solved.”
Ok Google, get me a home loan
ML powered, natural language driven voice assistants like Alexa and Google are now capable of summoning you a taxi or place an order for pizza, but how long until they can get you a home loan?
It’s not impossible, but many clogs in the wheel have to spin together for this to happen, say your bank account has to be linked to your voice assistant, which is also connected to a credit rating agency, which is also connected to your home builder, who’s property is prequalified for securing a loan. Going by this example, it is not just Deep Tech (or lack of it) that’s stopping from many innovations in FinTech, but rather about integrating various processes and organisations on a level playing field.
Like Nimesh Mehta the Founder of Rockmetric says, “AI and ML applications need to deliver simpler and familiar user experiences that can drive transformative economic value to drive adoption. Along with technology, we also need to address the challenge of change management.”
Who’s guarding the guards?
Historically, technology companies like Apple and Google have been ruthless about protecting their customers’ privacy by safeguarding data; but banking and FinTech operations are not confined to just one or two party but involves a labyrinth of nodes and networks. For instance, a simple money transfer with a Smartphone involves at least 5 parties – your phone, your bank, a common interface (UPI, Visa), recipient’s bank account, recipient’s phone; in this scenario, who owns the data pertaining to entire transaction? Is it fair if the other parties ‘borrow’ your data to understand more about you to tailor better financial solutions? What choices and options would customers have to opt out?
Security and privacy in FinTech opens more questions than answers.
However, AI is increasingly used in banks and financial institutions to detect fraudulent transactions, smarter financial statement, saving and wealth creation recommendations, share trading tips and more. But that’s only scratching the surface and we have a long way to go. As rightly pointed out by Neerav Parekh the Founder & CEO of VPhrase, “Artificial intelligence has a huge potential to change things for better and as far as Natural Language Generation goes, it has already started delivering. Companies like ours have demonstrated that by automating a lot of data analysis and reporting tasks that enterprises were required to do manually earlier. But still, we are just scratching the surface with these technologies. There is a lot more yet to be done.”
Come to Fintegrate Zone 3.0 to hear from industry practitioners and thought leaders to learn more about usage of ML and AI in FinTech
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