This blog is a contribution from our customer Razorpay, one of the largest financial technology companies in APAC. Learn how Razorpay leverages DataRobot to build AI models 10x faster and sharpen its competitive advantage.
In a fast-growing environment, how does our small data science team continuously solve our company’s and customers’ greatest challenges?
At Razorpay, our mission is to be a one-stop fintech solution for all business needs. We power online payments and provide other financial solutions for millions of businesses across India and Southeast Asia.
Since I joined in 2021, we have acquired six companies and expanded our product offerings.
Though we’re growing quickly, Razorpay competes against much larger organizations with significantly more resources to build data science teams from scratch. We needed an approach that harnessed the expertise of our 1,000+ engineers to create the models they need to make faster, better decisions. Our AI vision was fundamentally grounded in empowering our entire organization with AI.
Fostering Rapid Machine Learning and AI Experimentation in Financial Services
Given our goal of putting AI into the hands of engineers, ease-of-use was at the top of our wish list when evaluating AI solutions. They needed the ability to ramp up quickly and explore without a lot of tedious hand-holding.
No matter someone’s background, we want them to be able to quickly get answers out of the box.
AI experimentation like this used to take an entire week. Now we’ve cut that time by 90%, meaning we’re getting results in just a few hours. If somebody wants to jump in and get an AI idea moving, it’s possible. Imagine those time savings multiplied across our entire engineering team – that’s a huge boost to our productivity.
That speed allowed us to solve one of our toughest business challenges for customers: fraudulent orders. In data science, timelines are usually measured in weeks and months, but we achieved it in 12 hours. The next day we went live and blocked all malicious orders without affecting a single real order. It’s pretty magical when your ideas become reality that fast and have a positive impact on your customers.
‘Playing’ with the Data
When team members load data into DataRobot, we encourage them to explore the data to the fullest – rather than rushing to train models. Thanks to the time savings we see with DataRobot, they can take a step back to understand the data relative to what they’re building.
That layer helps people learn how to operate the DataRobot Platform and uncover meaningful insights.
At the same time, there’s less worry about whether something is coded correctly. When the experts can execute on their ideas, they have confidence in what they’ve created on the platform.
Connecting with a Trusted Cloud Computing Partner
For cloud computing, we’re a pure Amazon Web Services shop. By acquiring DataRobot via the AWS marketplace, we were able to start working with the platform within a day or two. If this had taken a week, as it often does with new services, we would have experienced a service outage.
The integration between the DataRobot AI Platform and that broader technology ecosystem ensures we have the infrastructure to tackle our predictive and generative AI initiatives effectively.
Minding Privacy, Transparency, and Accountability
In the highly regulated fintech industry, we have to abide by quite a few compliance, security, and auditing requirements.
DataRobot fits our demands with transparency, bias mitigation, and fairness behind all our modeling. That helps ensure we’re accountable in everything we do.
Standardized Workflows Set the Stage for Ongoing Innovation
For smoother adoption, creating standard operating procedures has been critical. As I experimented with DataRobot, I documented the steps to help my team and others with onboarding.
What’s next for us? Data science has changed dramatically in the past few years. We’re making decisions better and quicker as AI moves closer to how humans behave.
What excites me most about AI is it’s now fundamentally an extension of what we’re trying to achieve – like a co-pilot.
Our competitors are probably 10 times bigger than us in terms of team size. With the time we save with DataRobot, we now have the opportunity to get ahead. The platform is an extreme developer productivity multiplier that allows our existing experts to prepare for the next generation of engineering and quickly deliver value to our customers.
About the author
Pranjal Yadav is an accomplished professional with a decade of experience in the technology industry. He currently serves as the Head of AI/ML at Razorpay, where he leads innovative projects that leverage machine learning and artificial intelligence to drive business growth and enhance operational efficiency.
With a deep expertise in machine learning, system design, and solutions architecture, Pranjal has a proven track record of developing and deploying scalable and robust systems. His extensive knowledge in algorithms, combined with his leadership skills, allows him to effectively mentor and coach teams, fostering a culture of continuous improvement and excellence.
Throughout his career, Pranjal has demonstrated a strong ability to design and implement strategic solutions that meet complex business requirements. His passion for technology and commitment to growth have made him a respected leader in the industry, dedicated to pushing the boundaries of what’s possible in the AI/ML space.
