Evolution of Accelerators to Fast-Track Emerging Technologies & Scale

Evolution of Accelerators to Fast-Track Emerging Technologies & Scale

Amit Shah, Chief Fintech Officer, YES BANK

Every business strives to innovate, but only few succeed consistently over time. `Innovation' and `disruption' are no more lab words, confined to R&D departments of organizations. In fact, disruptions are not as much a product of consumers adapting fast to technology as are of incumbents not responding quickly and lacking the `sense of urgency' to match the pace of change. Companies today are aware that investing in innovation is no more an option and are defining it primarily in three distinct ways sourcing innovation from within, sourcing them externally and an increasingly prevalent path of choosing `collaboration over ownership'.

A million dollar question that keeps CXOs awake at night is `where do I find my next moonshot?' Per-haps, the answer lies in obsessing about the customer, future & existing, and understanding what works best for them. We live through fortunate times where ideas from Ivy League dorms and journeys of start-up heroes like Jobs, Gates, Jack Ma, Bezos et al. have been democratized; we now have an aspiring entrepreneur in every nook and corner of progressive economies like India. The moonshots, I believe, will come by immersing ourselves in this democratized economy where ideas are free-flowing and there's fire in the belly to implement them.

Companies today attempt to apply the mantra of collaboration through the accelerator route. While the definition of accelerators is steadily evolving, I believe in keeping it simple. Accelerator programs should focus on two key things; first, to be a great place to find promising new startups, and second, discover new technologies that maybe moonshots of today, but necessary tech for tomorrow. This sort of experimentation will not only lead businesses to fast-tracking of some of the most promising emerging technologies to further their objectives, but also help assist startups find the right stakeholders - global mentors, investors, industry partners and a resource & knowledge-rich cohort. In due course, the entire organization also goes through a steep learning curve, further raising the general level of technological intelligence.

BFSI has been a primary contributor to not only developing fintech ecosystems around the world, but also pioneering use cases of innovative solutions like API banking, Blockchain, RPA, Chat-bots and so on. Going forward, with growth of connected systems, Big Data and AI ­ ML, among others should galvanize significant progress in not only the fintech space,
but also add a sizeable multiplier to other industries.

In order to explore the length and breadth of emerging technologies, several banks and financial services institutions around the world are setting-up platforms for Fintech startups to co-create innovative solutions for their customers. For instance, YES BANK - YES FINTECH's recent autumn cohort focused on startups in the space of Machine Learning, Block-chain and Artificial Intelligence. Through this initiative, we have identified startups that we will partner with to help us customize solutions for customer acquisition, assist underwriters in under-standing a customer's financial behavior and use behavioral based authentication technology for YES BANK systems through AI.

Also, it is widely understood that `DATA' is the bedrock of organizational continuum & intelligence, and the right use of data can result in creating a huge competitive advantage for any business. Be it in the form of use cases built on top of strong models through AI/ML, fintech applications, robotics or other robust applications built on various data models, I believe data will fuel future product, service and process innovations. Banks deal with troves of data ranging from transaction details to history of credit scores and risk assessment reports. In order to keep-up and sail through the data wave, there needs to be a two-pronged strategy ­ building data centric in-house capabilities & creating an external collaborative data ecosystem. And in order to do so, it is imperative for organizations to hunt for the sharpest business and technology minds in the country to create a specialized teams comprising of Data scientists, full stack developers and engineers. The goal of this team should be to evaluate and develop new strategy, build green field solutions and explore potential of new technologies.

To keep the wheel of learning rolling, we must also create an ecosystem that fosters creative thinking & constantly motivates us to innovate. `Datathons' of sorts enable participation from data professionals, enthusiasts and students to create robust and scalable data models. This in my experience helps build and test new data models focusing on, but not limited to predictive analytics for new product recommendations, customer satisfaction and loan defaulters.

It also becomes very important to build in-house capabilities to address needs of your business' multi-sector clients. Perhaps the best way to do this is to identify collaboration opportunities with businesses that are equally passionate to together experiment with you on emerging technologies like Blockchain, IoT, AI & Analytics to cater to latent opportunities, say in the areas of Smart Cities, Clean Tech, Agritech, Lifesciences Tech, Edu Tech and many others.

The point I'm trying to make here is that the sooner you realize that not all innovation comes from within and that one must not always be a recipient of innovative products, but actually a hybrid of these approaches, the faster you'll be able to find your moonshot through co-creation and collaboration.