Slicing Through Fintech: How slice is Revolutionizing India’s Financial Landscape with MongoDB-Powered Innovation

CXOToday has engaged in an exclusive interview with Upendra Kumar Singh, Head of Engineering, slice

How slice is leading the disruption and innovation in the fintech ecosystem and setting global benchmarks

slice has emerged as a leading innovator in India’s fintech ecosystem, focusing deeply on India’s young population. We’ve built a one-of-a-kind product that makes our customers’ financial experience more intuitive, rewarding, and truly delightful. The major focus since launching slice and its slew of products has always been to provide solutions to basic everyday financial problems. We’ve achieved this by creating a seamless and hassle-free onboarding experience for our customers while simultaneously demystifying the complexities of finance and traditional banking.

What distinguishes us is our fully digital product that offers an intuitive app experience with an uncomplicated and transparent approach. Our lending product, known as slice borrow, exemplifies this commitment. It provides a seamless solution for short-term borrowing needs, with a streamlined digital onboarding process and transparent terms, eliminating any hidden complexities. Moreover, it offers flexible repayment options, making it one of the most convenient ways to borrow money.

In addition, our product slice mini, an everyday payment account enhances the user’s payment experience. It operates as a prepaid account, allowing users to add funds and make payments through slice card or UPI, while also earning rewards and cashback. This account caters to a wide range of use cases, including managing subscriptions and monitoring expenditures, while providing a comprehensive payment solution.

Insights into how and why did slice decide to partner with MongoDB 

From the early stages, MongoDB served as one of the primary databases for our company, leveraging its flexible schema and rapid development capabilities. Initially, our development team handled the management of the database. However, as our operations expanded, we recognized the necessity for dynamic scaling capabilities, leading us to adopt MongoDB Atlas, the cloud-based data platform.

MongoDB Atlas offered the necessary infrastructure to seamlessly scale our operations with its dynamic scaling feature. Additionally, we required the ability to pre-schedule clusters for scaling to accommodate high volumes of traffic, such as during marketing campaigns or monthly billing cycles. Moreover, we aimed to free up time spent on database management, allowing us to focus on developing customer-centric features. Partnering with MongoDB was the logical choice, as it enabled us to achieve our scaling and growth objectives while delivering enhanced value to our customers.

What are some of the challenges that slice faced in providing a seamless experience to customers?

As we started out, slice faced two significant challenges in delivering a seamless experience to its customers: speed and scalability. Since we offer a credit product in our app, assessing the user’s creditworthiness involved a complex process of matching various variables. Unfortunately, our manual reverse look-up process was time-consuming, causing users to wait for 24-48 hours for the credit underwriting process to be completed. This delay proved inconvenient, particularly during time-sensitive situations like flash sales or medical emergencies, where immediate credit was necessary.

The second challenge we faced was due to the rapid adoption and growth of slice, which placed significant pressure on our technological infrastructure. This resulted in difficulties in delivering the seamless experience we aimed to provide to our customers. As our user base continued to expand, ensuring a smooth customer experience became a little challenging.

How MongoDB enabled them to solve these challenges

We leverage MongoDB Atlas across multiple use cases, and one notable example is our real-time feature store. Our objective codenamed ‘Project Makhan’ (meaning a smooth and seamless experience), was to streamline and expedite the onboarding process for credit users. Our aim was to reduce the processing time to under a minute. To accomplish this, our team utilised Change Streams in MongoDB Atlas to help assess user information in real-time.

As users filled out their applications, providing details such as name and gender through our mobile app, we utilized the real-time feature store in conjunction with MongoDB and machine learning (ML) models. This allowed us to compute over 100 direct and indirect variables in real-time to determine credit eligibility.

Upon the commencement of the information-filling process, the data is stored in Atlas, and computation begins for the derived variables. Once all variables are computed, an AWS step function is triggered, which subsequently activates the credit underwriting service. This service takes all the computed variables and feeds them into the ML model, generating a user score ranging from 0 to 100. Additionally, a rules engine is employed to identify any red flags. MongoDB is utilized to analyze these red flags and determine the likelihood of a user being a defaulter. Thanks to MongoDB Atlas, this flag resolution now occurs automatically and in real-time, replacing the previous manual process. The final decision is then communicated to the user.

Currently, we have over 15 MongoDB clusters supported by MongoDB Atlas, holding a total of 17.5 TB of data, and serving over 12 million registered users. During peak hours, slice can perform approximately 15,000 MongoDB input/output operations per second (IOPS). With the flexibility of auto-scaling capabilities, our team can add or remove nodes as needed using Atlas cluster APIs. Given the potential criticality of these functionalities in emergency situations, ensuring uptime is of paramount importance.

How have the results improved post using MongoDB platform

The solution enables credit underwriting decisions to be taken in under 30 seconds by calculating more than 100 user variables in real-time. The solution has positively impacted growth as underwriting time decreased significantly which helped us onboard customers in a more efficient way. With MongoDB’s resilient distributed architecture, slice can achieve a 99.995% uptime SLA.

MongoDB Atlas provides the core capabilities that slice requires to dynamically scale the business, including the flexibility of the document model, change streams, always-on security, continuous backup, easy migrations, real-time analytics and native tooling has helped us with comprehensive functionality to support our dynamic operations.