One of the biggest hurdles of such an ideal scenario is the lack of trust in financial institutions. Below are graphs from Edelman’s 2017 and 2018 trust barometers, and it's interesting that there has been no progress in the trust scores between the two years when we compare it with Edelman’s 2018 trust barometer.
Edelman’s 2017 trust barometer
Edelman’s 2018 trust barometerBut, that’s slowly reversing with the financial sectors own unencumbered upstarts or Fintech companies. The latter is the reason for the traditional bank’s unbundling because the Fintechs focus on 'serving specific customer needs and serving it better' by providing clear transparency of fee charged for the services offered. And, this regained trust within the financial sector has resulted in a new kind of customer, the Prosumer who is willing to share their information to gain better services.
And with numbers like 40% of GenerationY poised to Bank with the likes of Amazon and Google, it’s no wonder that financial incumbents have begun shifting their strategies. For example, instead of making early-stage bets in new Fintech, they shifted their focus to beefing up their acquisitions.
Incumbents across business sectors are fighting their own digital disruptors. And, I believe the financial sector can learn a thing or two from a particular strategy that’s in play in the health sector - precision healthcare.
The segment of one, a missed opportunity?Professors C. K. Prahalad and M. S. Krishnan described the Segment of One as N organizations coming together to solve the need of 1 customer. Every individual customer is recognized as a unique (segment of one) whose environment, lifestyle, personality, preferences, needs and wants are appreciated as different. Current banking solutions are overwhelmingly generic and lack relevance and context for intended customers.
I believe precision banking will help traditional banks stay relevant in this digital era because it will force the incumbents to develop intuitive and contextual solutions. For example, effective data analytics could help a financial service provider learn to predict the propensity of purchases, and accordingly get banks to offer a relevant financial product at the right moment.
Here are a few examples of a relevance-filled financial reality made possible through the use of customer data along with the needed consent to use the same. Or in other words, examples that bring to fore the saying, "Timing, it's often assumed, is an art":
- You are browsing through Amazon.com, looking for an expensive pair of Bose speakers. While scouring the features of your top choices, you receive a text message on your phone. The message has a limited time (5-10 minutes) offer of not just pre-approved credit, but an additional discount (offered only to you) when purchasing the product. Additionally, the message also has some additional advice on the best way to finance your purchase based on your personal spending patterns.
- Late one afternoon, you are quite interested in a particular car in the showroom. A message pops up on your phone offering personalized payment options to purchase cars from the specific showroom/dealer. The message prompts you to send a picture/scan a QR code (and a little more information) of the car in question for the complete range of financial advice for purchasing the car of your dreams. There is a pre-approved, discounted personalized offer that is based on your financial history with your bank. The offer also provides you options for an exchange/resale of that particular car model. A sense of urgency is created with the limited time offer, that’s valid for about 1-2 hours after visiting the car showroom.
What would it take to design such an intelligent solution system?Such solutions call for a perceptive data pipeline, that will not only consume from innumerable data sources but will feed germane data elements into complex Artificial Intelligence (AI) and Machine Learning (ML) based decision engines.
Beyond this data pipeline, a robust digital platform will become the foundation on which creative data science and data engineering teams will build, and deploy complex algorithms at pace. The platform will also lend itself to support the extension of these unique algorithms as services for downstream consumption.
A precision banking engine
Such an approach, that leverages the precision engine depicted above, is apt for a scenario when technology companies like Amazon, Google, and niche fintech players are forcing global incumbent banks to re-evaluate their current range of solution offerings and distribution models. What's more, the data-heavy world that we live in is encouraging financial institutions to attempt contextual relevance in their approach, which will also help close the existing gaps in their portfolios.
It’s clear that financial services will only become a more seamless part of customers’ lifestyles as advances take place in data analytics (with the availability of structured and unstructured data). Also, as banks and personal finance startups freely access and draw inferences from customer-behavior data to tailor product recommendations that suit every (segment of one) customer’s financial habits, the existing trust gap between the customer and his or her financial service provider is sure to close for good.