Here's how analytics deliver in-house decisioning capabilities to Clix Capital

Clix Capital calls itself “a smart, contemporary lending firm that uses technology to make loans simpler, faster, and more accessible”.

The company is co-founded by Pramod Bhasin (founder of Genpact and former CEO of GE) and Anil Chawla (former CEO of GE Capital India and Asia’s commercial business) in 2016. The duo jointly acquired the commercial lending and leasing business of GE Capital India with the support of AION Capital Partners and rechristened it as Clix Capital.

One of the leading players in Retail and Corporate Lending space today, Clix leverages the power of data in arriving at the most relevant customer insights.

Data analytics is thus a strategic area of focus for the lender. It has made significant investments in developing internal resources and capabilities around analytics.

Moneycontrol spoke to Katerina Folkman, who is currently heading analytics at Clix Capital. A seasoned professional in analytics, she has over 15 years of experience in the field of advanced analytics, with track record in revenue growth and risk management.

Q: What are the key data analytics investments the company is making today? Which functions/areas will benefit out of this?

A: The biggest investment that Clix Capital is making in analytics is the set-up of our Data Lake and is preparing for its expansion with additional unstructured data flowing in. We are investing in an internal team developing in-house Decision Engine (Delphi). Having in-house decisioning is a critical capability. It will not only allow us to have full control and transparency of the decision, but we can work with A/B testing of the models and immediately enhance them as customers and markets are changing.

We are also moving into AI territory now and investing into AI specialists. Our current plan is to set up self-learning models, which will continuously sharpen their own decisions as more data, more customer applications and more customer events will be flowing through the algorithms.

Q: What are the challenges that companies face while scaling up data analytics initiative/projects within the company? How do you deal with this scenario?

A: The analytical challenges that financial services companies are facing in India is data – its availability, quality and integration across disparate sources. Traditionally for banks, internally structured data is easiest to capture. But for a lending company like Clix, it means one data point per month.

We overcome this challenge with capturing alternative, unstructured data from every customer interaction. For example, voice and video records, customer digital footprint, acquisition-point data from our partners.

Q: How do you leverage data in eliminating frauds and deciding creditworthiness?

A: Even tracking how the customer fills the online application form can help eliminate fraudsters and high-risk applicants. Interestingly, the systems can capture how customer types his own name, and if there are mistakes, or multiple attempts to type it – it is a red flag. Similarly, if a customer moves through an application form too quickly and mindlessly, moving all sliders to the right to get the money quickly, chances are that such applicant has no intention to pay. Another curious data source is mobile phone data – how many selfies an applicant is taking, what is his battery charging patterns. Such data can help fill the gaps and really describe potential customers much better than a simple pay slip from his employer.

However, the challenge is to extract immediate deep insights from such interesting data, for which we need superior data architecture and an engineering team in-house.

Q: How do you ensure that your analytics initiatives are aligned well with the organization’s business goals?

A: Aligning analytics scientists with business teams is an interesting challenge indeed. We spend a lot of time “in the field”, outside of the office and meeting customers along with our sales team. This helps us understand our customer needs and pain points better, so our models are grounded in reality. Of course, the most interesting creative insights come from such customer interactions. We use a “Design thinking” approach to get customer inputs, prototypes and test ideas faster, before developing long-term solutions.

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