Data Analytics In Digital Lending

By Parikshit Chitalkar, Co-Founder & CTO, StashFin

Parikshit Chitalkar, Co-Founder & CTO, StashFinIndia is a growing economy with spending on the rise. With increasing wages & new additions to the workforce these are ripe conditions for a growing demand in consumer credit.

Demographically being a young country more Indians joining the workforce directly results in more borrowers appearing on the landscape, one of the key challenges for lending institutions to meet this demand is to find ways whereby they can broaden the borrower base via while keeping overall portfolio risk within various enterprise constraints.

Traditional lending institutions may not be structured to capture this emergence of a data rich economy. Digital lending platforms are at the forefront of this opportunity.

One realistic way to address this problem is through using data analytics to extrapolate a measure of risk from a mix of traditional and new data sources so as to assess new types of borrowers that were not serviced by traditional underwriting models.

Within the digital lending industry there are various business models that exist however there is commonality between how a data analytics platform could be structured.

There a few critical elements of a robust platform, core technology platform, data pipes, data warehousing, modelling & monitoring. For a platform to deliver value there has to be a balance of all such elements each of which needs its own iterative improvements from ongoing business learnings.

“The climate demands the CIO role evolves into someone who understands aspects of data analytics and core technology while developing a view on global risk & compliance initiatives that may start to apply to the domestic market in times to come”

At StashFin we consider the role of data analytics as a mission critical function. Our philosophy is to use data to select the best customers & provide them with a suitable product offering. We achieve this objective by running models that find indicative variables which will allow us to build a point of view on a borrower.

This approach helps us process a large number of applications through the system, thereby training the risk model further & at the same time minimizing underwriting costs by weeding out profiles, preventing adverse selection & detecting fraud.

This has proven successful for us because it allows us to rely on industry wide benchmarking metrics to measure our own performance at the same time operate with flexibility in underwriting leading to lower overall risk and diverse portfolio.

We made an early decision to invest heavily in our data storage & integration systems, that investment has seen a huge pay off for us. We focused on making sure that we developed extensible storage models to retain data coming from various traditional & new sources and our systems were built to ensure that all the data talks to each other and can be used across functions ranging from data driven decisioning functions like risk assessment & underwriting to operational technology. Addition and consumption of new data sources also has to be a very quick and scalable process.

For this approach to be successful and for an organization to see the true benefits of data analytics a holistic approach is critical.

This is an effort that cannot be left to the technology or data science teams alone. Organizations building machine learning models or using alternative data sources is a tool for the underwriting or risk teams only are not leveraging the full potential of data.

It is critical that business stakeholders have a keen understanding & involvement in this process. Data analytics should be viewed as an investment in an asset with clearly measurable returns.

In any lending business, cost is the most important operating lever & the impact that data analytics and ML can drive in operational efficiency is often times very significant.

Interconnectivity of data across the system is key. At StashFin we have seen rich dividends in operational efficiency resulting from our data analytics efforts.

The Digital lending industry in India is in its infancy, as it matures, we will see the role of data analytics realize its full potential. The availability of data sources is growing rapidly, initiatives like open government data (data.gov.in) are a clear signal of intent from the government that digital is the way forward and that data truly is the new currency going forward.

As the industry evolves, we are also witnessing a rapid evolution of the regulatory framework, which is a very healthy sign. These are very interesting times for the modern CIO in the lending industry. The role has moved from an IT infrastructure expert to a multifaceted technologist with in-house development and data science teams.

The climate demands the CIO role evolves into someone who understands aspects of data analytics and core technology while developing a view on global risk & compliance initiatives that may start to apply to the domestic market in times to come. The opportunity also extends to building an organizational culture where data is treated as an essential asset in decision making and creating value.

It is an opportunity to be at the tip of the spear in a digital India.

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