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Natalia Lyarskaya, Chief Data Officer, ZestMoney

Natalia Lyarskaya, Chief Data Officer, ZestMoney

What has been your path to power?

I started my career as a statistics analyst in 2006 in CRIF, at that time an Italian credit bureau and credit risk consultancy company which was just about to open an office in Moscow. I spent the first months in Italy in the company’s HQ, getting trained on the new SAS softwareand learning the basics of scorecard development for the financial sector. I then decided to pursue my studies and do a PhD in Economics at the Sorbonne University in Paris. After completion in 2011, I moved to London and joined fintech start-up Wonga as its first data scientist building risk and fraud algorithms. There I had an opportunity to lead all of its data and ML initiatives to evaluate credit risk, prevent fraudulent behavior, optimise product and collection strategies, as well as to develop rigorous ID verification products for the UK, as well as a number of other highly-regulated international markets.

As the company was growing fast, I also grew my team and became lead data scientist, overlooking UK and International markets from a data and modelling perspective. Later, in 2014, I created a new team focusing on alternative data sources and emerging ML technologies for risk and fraud assessment. In August 2015, I joined ZestMoney, one of the largest and fastest-growing consumer lending fintech companies in India, with the mission to set-up its data science team in India, as well as to build its fully-automated decision engine using AI/ML technologies.

Since 2018, I have held the role of chief data officer role at ZestMoney, where I have built data, AI and risk strategy functions from scratch, while creating an efficient AI operating model, building an AI center of excellence and establishing a data-driven culture within the company. I also got deep experience working closely with the technology team to create the proper design of data and decisioning systems, satisfying the company’s requirements, including data protection and AI ethics angles.

What impact has the pandemic had on the role of data in your company/organisation?

ZestMoney has been a data-driven company with an advanced stage of predictive analytics and AI capabilities, even before the pandemic started. It’s a BNPL (buy now, pay later) platform operating in the Indian market. The Indian consumer is consuming digital products and services at a phenomenal rate, faster than any population in history. Increasingly people consume news, entertainment, healthcare and education in a digital format, having by-passed offline services completely.

This pace of digital adoption is enabling cheap, fast last-mile delivery of many of these services to consumers, even in remote locations, democratising access to everyone irrespective of income level or geography. Financial products are not an exception. Covid-19 has accelerated the demand for digital BNPL offerings in India and we have been well placed to benefit. Our fully-automated, AI-based decision engine allowed us to manage credit risk smoothly despite a turbulent economic backdrop and our key machine learning algorithms demonstrated a high level of stability and robustness, even at this challenging time.

Does data now have a seat at the table during strategic discussions? If not, what will it take to get it there?

Data at ZestMoney not only has a seat at the table during strategic discussions, but it also drives what strategic discussions need to take place. ZestMoney’s data and AI team is omnipresent across the company, with various use cases across all functions. Data and AI capabilities at ZestMoney are among our key competitive advantages on the market, they have been built with a purpose to deliver value for our customers and business at scale.

This is driven by the need to be agile and responsive in a very dynamic environment in which financial technology companies – and ZestMoney in particular – operate: customer expectations are increasing day-by-day, technology advances to respond to these expectations, fraudsters get smarter and more sophisticated, and the regulatory environment rapidly evolves.

What are your key areas of focus for data and analytics in 2022?

My key priority for 2022 is to scale the AI and predictive analytics solutions that are currently built and reach a certain level of maturity in terms of bringing the whole infrastructure in for MLOps, doing A/B testing at scale and combining these tools. In the last couple of years, we’ve been focusing on extending AI applications in products beyond classic risk scoring solutions.

Currently there are 30-plus use cases across the company where automated data and AI-based algorithms are used to improve our core products and/or processes, from affordability assessment, KYC solutions and fraud prevention mechanisms to personalised on-boarding experience, recommendation engine, and fully-automated collection strategies. This allowed us to significantly improve the company’s performance metrics, specifically around credit risk and fraud losses, as well as other KPIs, increased end-to-end conversion, optimisation of collection resources, increased customer engagement, etc. We are now focusing on scaling these solutions, building relevant infrastructure and teams to allow us continue a tremendous growth and purposeful direction towards our vision to make life affordable for 300 million households in India.

Tell us what leadership means to you in the context of your role as a senior data leader.

For me, leadership in data and analytics means having an impact, solving the problems for people using data and technology. It’s about focus on technology, but not as a tool, but as a mean to drive decision-making, grow teams, businesses and improve processes. Leadership is also about people, motivating, inspiring them, allowing them to grow and to develop their skills. One can teach data, however, a data leader is not a teacher, she/he is an ambassador who disseminates a culture of metrics and KPIs, not intuition; who creates and inspires great storytelling using data; who is a life-time learner her/him-self.

What key skills or attributes do you consider have contributed to your success in this role?

I think open mindedness and openness to learn and to try new things has been a big contributor to my success. The saying goes, “in times of change, learners inherit the Earth, while the learned find themselves beautifully equipped for a world that doesn’t exist”. This line always holds true for me. I’m insanely passionate about learning new skills, new technologies, as this allows me to be well-equipped for ever-emerging business models, technology challenges, socio-economic changes. And it’s not only about the hard skills, it’s also about the adaptability – the ability to adjust to changes not in a passive sense, but to enjoy, leverage and embrace this change.

How did you develop – and continue to develop – these skills or attributes?

I believe the best way to develop these skills is to step into your uncomfortable zone, to practice touch conversations, to ask challenging questions. Most of my development happens every day in my role, learning from my team, co-founders, from every challenge we face as a business, or even from the failures.

I am driven by new data problems and I’m very passionate about the power of data and technology to change our world for the better. This striving for new learnings brought me to various data and AI communities (Women in AI, Women in Data, Teens in AI, etc); networking proved to be invaluable, learning how others have managed or are simply going through similar challenges helps you to gain perspective.

Is the data tech you have keeping pace with your goals and requirements? Are your providers leading or lagging behind your demands?

For me, data tech is much more than building data lakes and analytics capabilities. It’s also about the integration architecture between data systems and the rest of the business processes and products. In the fintech environment, especially in consumer-facing business, if we want data and AI to drive business value, there is a need for very strong collaboration between data, analytics, engineering and product teams.

At ZestMoney, we’ve invested a lot of time and effort to create alignment between the teams on our data and AI-driven strategy to ensure the right processes are established for this collaborative work from requirements creation stage, through rigorous testing and production release cycle, to monitoring and alert system in place. I’m lucky to work with a very supportive CTO and the whole technology team who clearly understand the value of data and AI capabilities to the business.

Natalia Lyarskaya
has been included in:

100 Brands 2020 (EMEA)
100 Brands 2022 (EMEA)