3D Economy Data, Digital & Disruption
Banking Frontiers|October 2020
3D Economy Data, Digital & Disruption
Banking Frontiers, in association with Snowflake, organized a web panel discussion by experts on the transformative role of data.
Ravi Lalwani

Bertram D’Souza, SVP & Head - Digital Payments, Open Banking & Fintech Partnerships, Kotak Mahindra Bank

When it comes to data, banks have an inherent competitive advantage in terms of volume, quality and quantity of data whether it is demographic or transactional. The key to building successful digital products in this space is to collect deep insights from users and then create a good data collection architecture. It is also important to understand how this data can translate into a framework to deliver key business outcomes. BFSI entities have goals like how to use digital to identify the right customers for an acquisition perspective and how to engage with existing customers. A lot of engagement would come from a deep understanding of what makes a customer pick a product. Also need to understand enough about what the customer aspires for in the next level of products and services.

I think, that’s where digital plays a key role in delivering that native user experience to capture a user’s attention and cross-sell and upsell effectively.

A few years ago, Kotak Mahindra Bank started its data centralization journey. We recognized the fact that centralizing a lot of disparate data sources was crucial for future use-cases. Implementing use cases does take its own time as building intelligence on top of data is not an easy task. Historical information and certain recommendations in the digital layer can prompt/nudge a customer. All these models are tried and tested across the industry and some large players in the ecommerce ecosystem too have adopted it. We have first-hand learnings from this area.

Most BFSI companies can provide real-time recommendations based on past information. This is possible by analyzing unstructured and structured data coming from multiple sources, such as social media, images, files, customer location and others to create a deeper understanding of customers. This will evolve more in the banking sector and each person will have a unique hyper-personalized experience. The way millennials would like to engage with the bank and the way senior citizens would like to engage with it are different and it is something that we are actively working towards.

Some of the platforms we have invested in, help us move towards a digital, paperless, presence-less and contactless kind of ecosystem. The covid pandemic has made it clear that digital is a primary channel and without it, it is difficult for any organization to survive. During the pandemic, we have seen a combination of offline digital payments that have been made easy to execute using QR codes and new online payment methods have also been rising sharply facilitating a lot of data collection from transactions. A lot of these initiatives - whether they are payments related or product-centric, have come together. It is an incredibly unique time to make sense of and build innovative usecases on top of it.

We must learn from large ecommerce companies and the way they use recommendations and bring some of that intelligence into our own digital experiences for our customers. The channel transformation that has happened over the years has made us create one of the best mobile banking apps in the market. Consumption of content and certain ecommerce journeys could also start originating from within bank ecosystems. I see this as an emerging trend in times to come.

Harshvardhan Chamria, Chief Digital Officer (CDO), Magma Fincorp

We started out by using a data analytics tool to create lending scorecards and on the basis of this, our underwriters could decide whether to give a loan to a customer or not. With the emergence of AI, ML and other technologies, we are now actually having the ability to analyze unstructured and surrogate non-financial data in realtime to create feedback loops. This has helped us to expand the market to financially include new - to credit and thin-file customers, who have no repayment history.

Additionally, it has helped us in taking lending decisions quickly, thereby creating a shift from a small number of large transactions to large number of small transactions, and also giving rise to card-less credit on the fly. Magma Fincorp works with customers who are part of the informal economy - 85% of them do not file ITR, most of them are thin-file or new to credit customers. Our field-teams capture alternate data such as acreage of farm, additional informal income of family members, electricity bill of manufacturing entities, routes plied by truck drivers, etc, and populate this data into the loan origination app on their smartphones. After this, we overlay portfolio-performance data for look-alike customers from the credit bureaus. All of this happens in real-time. The credit models are accordingly able to give an instant decision in two-thirds of our new customers. A human credit underwriter plays a limited verification role here.

Data analytics has helped us to move from purely efficient processing of data to expansion of market and customer segments. We are now planning on going direct-tocustomer with a smartphone app and also direct-to-dealer/channel to source business.

Digital transformation marks a radical rethinking of how an organization uses technology, people and processes to fundamentally change business performance. When we launched our new omni-channel CRM, we discovered that even though we were displaying crosssell offers on the screen, our field force could not cross-sell substantially and the reason was that we had not changed the behavior of our employees after launching the platform. Subsequently, we started gamification of the entire experience, incentivizing employees and introducing training programs, train the trainer kind of programs and after that we saw a boost in our cross-sell figures.

The loan underwriting application and collection application are 2 separate platforms. After building our data warehouse, we linked the risk, credit and portfolio performance data with cross-sell data. This has made pre-approved offer generation far quicker. It is of course much easier to give loans to repeat customers, compared to new customers because the analysis required is significantly lower for and existing customer, and the acquisition costs are also lower.


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October 2020