Leading fintech organisations are betting heavily on data analytics to predict customer behaviour and develop sophisticated risk assessment mechanisms. The ability to process a large amount of data allows fintech companies to make smarter and effective decisions and create personalised experiences.
One such leading firm is the US-based fintech company PayPal, and data analytics forms a major cornerstone for many of their operations. Analytics India Magazine caught up with Mohit Pant, Senior Director, Advanced Analytics & Decision Strategy at PayPal, for an interview to understand more.
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Edited excerpts from the interview:
AIM: How did you start your professional career in analytics? How do you think the analytics industry has evolved?
Mohit Pant: My journey in analytics started by accident when I joined GE Capital as part of my campus placement. I was completely at sea, not knowing SAS programming or much about statistical model building. My learning happened on the job.
I have spent over 18 years in analytics and all of it in the financial services industry – credit card, brokerage & retirement, and now in the payments sector with PayPal.
I joined PayPal four years back to set up the Analytics Centre of Excellence in Chennai. We started the team from scratch, which today has grown to a 150+ member strong team. We focus on providing analytics for products, sales, marketing, and finance. More recently, my role was expanded to lead Global decision strategy, Business Intelligence and Program Management, along with being the site lead for the analytics team in India.
The industry itself has evolved over time. As the cost and processing of data have become cheaper and faster, the significance of the analytics function has also increased significantly. Analytics wasn’t a very common term when I started, and it was tough to explain to people what I do for a living. That has changed, with analytics now becoming a coveted job.
One great tip I had received early in my career was about how to be effective in analytics, and that can be summarised in this equation:
- Q= Quality of your solution (analytics depth)
- A= Adoption of solution
- E= Effectiveness of solution
We tend to sometimes focus on building the most sophisticated models(Q) without focusing on the adoption of that model(A), which leads to less than optimum results.
AIM: What is the role of data analytics at PayPal?
Mohit Pant: PayPal is a global leader in digital payments, and our two-sided network helps merchants and consumers transact. In order to provide our customers with a safe and seamless experience, we rely on data across all functions of the organisation – sales, marketing, product, and risk, while complying with applicable data regulations.
PayPal’s vision is to become a Customer Champion company. We leverage advanced analytics & experimentation to make decisions around how to personalise a customer’s journey, what product features to build, extend credit to them, etc.
AIM: How do you leverage AI, ML, automation, and other emerging technologies in your operations?
Mohit Pant: One of the teams I manage is the Decision strategy & Business Intelligence team (BI). As digital acceptance and penetration increase, our goal is to build scalable solutions to manage the volumes of payments. To give you a perspective, we surpassed 300 billion USD of total payment volume for the first time in Q2 2021, representing a 40 per cent growth. For such large volumes of transactions, automation plays a significant role in driving scalable solutions, which some of the new generation BI tools like ThoughtSpot, Looker, etc., are helping us explore through the NLP features.
AIM: What is the role of big data, especially in the fintech industry?
Mohit Pant: PayPal’s mission is to democratise financial services and ensure that everyone, regardless of background or economic standing, has access to a safe and seamless payments experience. Big data allows us to complete complex tasks like risk assessment, providing access to credit, etc., in a simple manner while complying with local regulations. Some of the key areas where we are leveraging big data include:
- Protecting the customer (fraud protection as well as regulatory requirements like GDPR)
- Personalising product experiences
- Offering a frictionless experience
AIM: How does PayPal manage big data?
Mohit Pant: Big data management is done in four steps at PayPal:
- The first one is by the team that manages site infrastructure (creating the raw layer of data, managing flow of raw data within the organisation, running automated jobs)
- The second team that gets involved is the data governance team which is enterprise-wide & works closely with the business to create a strong data governance framework & bring consistency in data definition/single source of truth
- The third team looks at what kind of data the business needs & enables the transformation of raw data into analytics-ready data marts that can be consumed by the data analytics team
- The analytics works on top of this transformed layer of data to drive business insights & actions
AIM: What are the current trends in data analytics?
Mohit Pant: While there are many trends evolving in the sector, I would like to focus on a couple of areas that are significant for us:
- Industry research shows that with data becoming more available and the capability to store & crunch the data getting democratised, the augmented consumer is rising. Businesses were earlier restricted to predefined dashboards and manual data exploration. This has now changed. There is a need and the appetite for automated, conversational, mobile and dynamically generated insights customised to a user’s needs and delivered to their point of consumption.
- The adoption of AI platforms allows users to analyse and process data, build machine learning models, and deploy and maintain these models. This is giving rise to citizen data scientists and democratising the use of data science models.
AIM: What kind of skills does PayPal look for in their analytics team?
Mohit Pant: Technical skills might vary depending on whether we are hiring for business analytics, Business Intelligence or Decision sciences team, but in general, we look for talent that has the ability to structure a problem, understands the domain, is able to cut through the ambiguity, and has relevant educational background along with the diversity of experience.