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Fintech Disruption in the New Age of Incredible Intelligence
By Guru Bhat, GM Technology and Head of Engineering, PayPal
The Fintech revolution has been a long time coming. With the advent of e-commerce, the initial buzz around Fintech was primarily around the use of Digital Payments to streamline the online shopping experience and render it more convenient, fast and safe. Over the years, Digital Payments have moved from being a novel idea to being an essential part of life and Fintech has grown to encompass other areas in financial services. The underlying premise for Fintech progress is the same as in other areas: the democratization of access to data and processing power, advances in technology that make the processing of that data fairly cheap and advances in the science of automating the gathering of insights and patterns from large data sets.
Credit Decisioning on Steroids
If there is a holy grail in banking, it has to be a mechanism for credit decisioning that makes it fast for the borrower and safer for the lender. The problem boils down to the availability of sufficient data for the lender to quickly take a call on the borrower’s credit-worthiness and customize terms that would lead to the best possible match between borrower requirements and lender value. Traditional banking has relied less on technology and more on a process-driven approach of a data broker gathering relevant (but unreliable and possibly out of date) financial data from disparate sources and basing decisions on this data. This approach in addition to being slow and rigid also made it difficult for new entrants and competitors to enter the credit market because entering into agreements with data brokers to gain access to data was expensive and fraught with complications. In today’s world, we leave a trail of data with every act we perform–paying for a ticket as we hop onto a train or bus to go to work, our subscriptions to various services, our shopping habits online, how often we eat out, the kinds of restaurants we eat in–each of these is a data point that can paint a better picture of us as people and consequently, our ability to service a loan if it were extended to us! As a result, traditional banks are forced to play catch up as Fintech companies (both behemoths and start-ups) begin to build credit into their offerings. With learning capabilities built into decision-making, each decision, its outcome as well as any new data further refines the algorithm and its ability to take better decisions. Ever-improving credit-decisioning at scale, in milliseconds, would be impossible if it were not for the fact that data science and AI is beginning to make the impossible, possible.
Traditional banks are forced to play catch up as Fintech companies (both behemoths and start-ups) begin to build credit into their offerings
Is the Insurance Ecosystem in Need of Disruption Insurance?
Any ecosystem where the consumers of its core services feel dissatisfied while the main players rake in profits is an ecosystem ripe for disruption. The Insurance industry is one such. The current system relies on templatized one-size-fits-all offerings that are difficult to understand, difficult to analyze/compare, difficult to claim against, etc. With AI-enabled insurance screening, each individual’s needs (and risk) can be assessed in a customized way in seconds allowing an ecosystem of insurers to compete for business and offer the best plan possible for that individual without compromising the ability of the entity offering the insurance to run a successful business in the process. In the current system, the unreliability of assessing risk using traditional methods means the ecosystem has to always hedge for the possibility of catastrophic loss-inducing claims–which drives up the cost of premiums for everybody participating in the system. However, with better decisioning support on assessing insurance-risk with the current ability crunch data, the entire system can be made much more efficient and customer-centric. The same holds true for the claims process as well. Thanks to the unreliability of information related to the claim and the very real possibility of fraud, quite a bit of manual effort is expended in making up for the trust deficit in the relationship between insurer and insured. All this time and cost can be saved if decisioning systems that support the claims process could use past history and peripheral information related to the insured, the event, etc. to automate the detection of fraud and thereby let good claims through quickly while rejecting fraudulent ones. As 2019 progresses, look out for massive advances on these fronts to bring about huge disruption in the Insurance ecosystem.
Going from Financial Services to True Financial Inclusion
The wealth management industry has always suffered from a serious flaw–its services have been relevant only for those with wealth, not those looking to start on the journey of creating wealth. With products like algorithm investing and zero-commission trading gaining prevalence, the ability of the common man to participate more fully in the financial system and partake in its growth has gone up. Many people were previously intimidated by the barrier to entry to the wealth management universe and the cost of these services. With technology taking the place of human advisors for most of the simple investment use cases, the cost has been lowered substantially–and when the participant base increases dramatically, requirements such as minimum investment ticket size no longer make sense because volumes can compensate for low individual investment sizes. Again, this is possible only because of advances in Machine learning and AI because algorithmic investing relies heavily on machines being able to detect and react to market patterns much more reliably, accurately and efficiently than human fund managers can. Of course, carefree participation in a market-linked system comes with its own set of risks to the consumer which need to be carefully understood and mitigated with governance and controls.
From the time the wheel was invented, to the more recent examples during the industrial revolution to the even more recent examples around supply-chain optimization, humans have always demonstrated a hunger to wring inefficiencies out of systems as technology makes it possible to invent such inefficiency busting devices. While AI is not a new field, it is only now that we are beginning to see its potential in being able to destroy inefficiencies in decision-making–which forms a huge part of the business of managing money and associated risks. The above examples are just a few of what’s in store for us as this story unfolds. I can’t wait to see what the new age of incredible intelligence holds for us.
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