Deep tech - The next big technology evolution

We are firmly in the midst of a vast technology revolution. We speak to faceless voice boxes that play our favorite music and order groceries from the nearest store. We can sit back in an autonomous car without worrying about the route. We just tided over a once-in-a-century pandemic on the back of breakthrough vaccine research and inoculated billions in a record time. How did we get here? The answer lies in disruptive technologies that are intrinsically geared towards improving the state of our lives.

How do you define deep-tech?

In the BCG report titled - Deep Tech and the Wave of Innovation, an argument is made that there is no such thing as a 'deep technology.' Instead, they use the analogy made by Clayton Christensen, who coined the term disruptive technologies, to describe true innovation that is in-built in businesses due to the adoption of technology. Similarly, deep tech is classified as a problem-solving approach that uses converging technologies powered by a design-build-test-learn (DBTL) cycle.

The applications are vast. We now can map the entire human genome that will eliminate diseases from the face of the earth. High-tech manufacturing disruption brought about by the convergence of IoT and 5G technology enables us to make autonomous cars. AI/ML allows better decision-making through predictive modeling that will allow nations to prepare for the next energy or food crisis.

Cloud is the enabler, data is the driver, AI is the differentiator.

The financial services ecosystem has significant implications on possibly every human being on the planet. From barter systems in the medieval ages to new-age cryptocurrencies built on blockchain technology, how we view personal finance, and economic trade has evolved many times over. In India, robust financial foundations have been made possible by the scale of UPI and inclusivity brought by the Aadhaar project.

Yet, there remains enormous scope for improvement regarding how banks and financial institutions adapt to the changing time of digitally native and mobile customers. In a world where connectivity features and products are not differentiated enough, customer experience is the only differentiator.

AI-Enabled Customer Centricity in Banking

Traditional banks, especially in India, have layers of legacy information, processes, and software piled on due to an incoherent technology adoption strategy. While banks have mountains of data available with them, unless business owners and growth leaders have analytics at their disposal, customers do not benefit from a personalized approach. The situation is similar to oil. You can have an ocean of crude oil discovery, but it is worth nothing without the knowledge to draw, refine, and combust it under control.

Data is similarly worthless without drawing analytics and making better business decisions at the right time. We live in an instant world, whether it is fast food, fast fashion, social media information overload with everything available at the click of a button. Yet, a customer applying for a loan may have to wait more than a few days to know whether they made the cut or not. This creates an experience gap for customers of banking institutions.

This brings us back to the core of deep-tech applications - where the fundamental purpose is problem-solving through the convergence of modern technologies. In a traditional banking ecosystem, data scientists (an increasingly saturated talent market) have struggled due to the lack of an end-to-end technology platform to process dynamic data and provide business users with the information they need to make customer-facing decisions quickly. Algorithms that can evaluate an applicant's risk profile, creditworthiness and track record in seconds can offer lending institutions an ability to lend fast, at scale, with a fraction of the risk.

Real-time decision making

Banks need to lower costs and cycle of customer acquisition. For this, timing is everything. The right product offered to the right prospect at the right time is the key to conversion. At the same time, the disparity between the Chief Growth Officer and Risk officer has to be bridged by balancing the risk portfolio and reducing NPAs while not becoming an impediment to customer acquisition at scale. Automated machine learning platform that allows the business user to categorize, validate and target prospects at the time of need will be the game-changer for banking institutions in India and worldwide. New-age banking and lending startups have already deployed a wide-scale of self-learning and no-code AI/ML technology platforms covering fraud detection, risk management and customer acquisition without the need for a complicated technology adoption curve.

Banking of the future is intelligent, data-driven and puts the customer at the heart of innovation.

Author: Suman Singh, Founder and CEO, CyborgIntell