How slice Enables Credit Approval in Less Than a Minute for Millions of Indians

MongoDB
April 12, 2023 | Updated: April 19, 2023

Building a fast, simple, and flexible way to make payments and offer credit to 12 million registered users

slice, which has emerged as a leading innovator in India’s fintech ecosystem, calls itself a zillennial product built by the zillennials. Zillennials being a term for the demographic group born towards the end of the time period for millennials and beginning of the generation-z time period.

The company believes that personalization combined with an extreme focus on superior customer service is the key to building long-lasting relations with young people. Not surprising then that the company’s singular focus is on providing the best consumer payments experience in the world, according to Upendra Kumar Singh, Head of Engineering, slice.

Speaking at the December 2022 MongoDB Day at Bangalore, Upendra described how slice is leading the disruption and innovation in the fintech ecosystem and setting global benchmarks.

The company has more than 12 million registered users on its platform. The slice app provides a fast, simple, and flexible way for users to make payments and avail of credit. For the vast majority of Indians, getting credit is typically challenging due to stringent regulations and lack of credit data. Through the use of modern underwriting systems, slice is helping broaden the accessibility to credit in India. The company is among the top pre-paid card providers in the South Asian market.

The challenge

Providing a seamless credit experience for customers by transforming the ‘Know Your Customer’ (KYC) process

slice found itself compared to other fintech apps which were typically ready to use immediately after downloading. They didn’t require users to fill out forms or wait for approvals. Since slice offers a credit product, the process of determining worthiness requires that they match several variables to ascertain the creditworthiness of the user. They would run a reverse look-up process matching the profile of the user to a network of other users to estimate likelihood of default.

This process was manual and quite time-consuming, which meant that users needed to wait 24 to 48 hours for the completion of the credit underwriting process. This was inconvenient, especially in scenarios such as flash sales or medical issues where users require credit immediately.

The other problem was dynamic spikes in traffic which could bring down the entire infrastructure and, therefore, the whole service.

“The team needed the infrastructure to scale seamlessly through a dynamic scaling capability. We also needed the ability to preschedule clusters for scaling when high volumes were anticipated such as in the event of marketing campaigns or monthly billing cycles,” said Upendra Kumar Singh, Hhead of Engineering, slice.

The team was also keen to free up time from managing databases to be able to focus on building features that would add value for customers.

The solution

Real-time computation of more than 100 variables to determine creditworthiness in minimal time

From early on MongoDB was one of the core databases the company used in order to take advantage of its flexible schema and speed of development. However, at first the development team self-managed the database. Then as the scale grew, they found there were often dynamic spikes which would threaten to bring down the application and cause an outage. The team realized the need for dynamic scaling capabilities and moved to MongoDB Atlas.

slice uses MongoDB Atlas for a number of use cases. One example highlighted in slice’s MongoDB Day presentation was a use case called real time feature store. The slice team took on the challenge of making the onboarding process for credit users more smooth and quick. Code named ‘Project Makhan’ (Makhan means butter in Hindi), the objective was to reduce processing time to less than a minute. To achieve this, the team used Change Streams in MongoDB Atlas to evaluate the user information in real-time.

“As and when the user filled out the application with details such as name and gender, we used the Real-time feature store with MongoDB and ML models to compute 100+ direct and indirect variables in real-time to determine if the user was eligible for credit,” said Upendra Kumar Singh, Head of Engineering, slice.

When users start filling out details using the mobile app, this information is stored in Atlas and computation starts with derived variables. Once all the variables are computed, an AWS step function is triggered. In turn, this triggers the credit underwriting service, which takes all the computed variables and feeds them into the ML model. The model determines the user’s score between 0 and 100. A rules engine looks out for red flags. The red flags are analyzed using MongoDB to determine the likelihood of the user being a defaulter.

This was a manual process, but with MongoDB Atlas, the resolution of red flags happens automatically in real-time. The decision is then communicated to the user.

Currently, MongoDB supports 15+ clusters with 17.5 TB data, which is used by more than 12 million registered users. slice can carry out about 15,000 MongoDB input/output operations per second (IOPS) during peak hours.

With the auto-scaling capabilities, the team can add or remove nodes on demand using Atlas cluster APIs. Given that the functionalities can often be used for critical workloads in emergency situations, uptime was important.

The result

Unprecedented processing speed and 99.995% uptime for more than 12 million registered users

“The solution enables credit underwriting decisions to be taken in under 30 seconds by calculating more than 100 user variables in real-time. This solution triggered exponential growth of slice as no other player in the industry was providing credit in 30 seconds. With MongoDB’s resilient distributed architecture, slice can achieve a 99.995% uptime SLA. There have been no outages except for those due to manual errors,” said Upendra Kumar Singh, Head of Engineering, slice.

MongoDB Atlas provides the core capabilities that slice requires to dynamically scale their business, including the flexibility of the document model, Change Streams, always-on security, continuous backup, easy migrations, real time analytics and native tooling.

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Overcome the Biggest Obstacle to True Customer 360: with MongoDB Atlas and Dataworkz

The more you know about your customers, the better you can attract them and increase their lifetime value to your business. The ability of modern systems to collect, store, and leverage customer data across disparate systems has raised the bar for what it means to understand your customer. To the winner goes the spoils, and the spoils go to the companies that navigate the last mile in the journey to attain true Customer 360: they leave no customer data behind. They hunt down every relevant source of customer data in their reach and turn them into valuable insight – without breaking a sweat. How customer acquisition costs and customer lifetime value impacts business success Two calculations are critical to understanding modeling how to grow your business. Customer Acquisition Costs (CAC): the amount of money a company spends to get a new customer Customer Lifetime Value (CLV): the net profit attributed to the customer relationship for the duration of the time they use a product Note that “customer” is front and center in both terms. Capturing the data behind these two calculations correctly and continuously on a timely basis across the whole customer journey – from first touch to renewals – and making sense of it, is essential to understanding how to grow and retain your customers. These calculations must be performed on multiple customers and their interactions over a span of years. Analytical and AI models that calculate CLV and LTV need to be fed up-to-date, contextual, reliable data to identify relevant patterns. Historical customer data guides future CLV and influences decisions on how to optimize investments in sales, marketing, and customer service to positively impact the customer’s future purchase and product adoption behaviors. Similarly, customer data analysis guides decisions on how to optimize sales and marketing investments to dial-in CAC for the fastest and most profitable conversion of prospects to customers. How successful a company is at leveraging CLV, and LTV calculations depends on their ability to link different data sources and formats, and integrate all of the internal and external systems required for modeling. Until now, companies have struggled to accomplish this because data joining technologies have fallen short for one or more of these reasons: cost, complexity, and scalability. Forrester1 declared Activation Of Unstructured “Dark” Data a 2022 mega trend. According to Forrester” "The question is no longer about collecting enough data — it’s about ensuring that the data is usable and liberated from company silos to create new streams of value. In 2022, being “data driven” must become more than just a slogan — the next wave of business transformation will hinge on activating “dark” data (data that organizations collect but do not effectively use) to drive differentiated experiences for both customers and employees." Creating that holistic 360° view of your customer is a business imperative for every organization–but creating a unified view or golden record of your customers is hard and expensive, especially if you want to capture and make sense of the structured and coherent unstructured data and include unstructured “dark” data. Why creating a customer 360 is a technical challenge Customer 360 data resides in your ecommerce platforms, Customer Relationship Management (CRM), Sales Force Automation (SFA), ERP, Customer Service systems, Partner Relationship Management programs, loyalty programs, payment portals, web apps, mobile apps and more–across business units and functional departments. Cross-object reporting in a single application (like Salesforce) can be a challenge but cross application reporting (SFDC + Zoom + Zendesk + Parquet files in a data lake) present an even bigger set of challenges with no common identifiers in the data collected from various applications. The data not only comes from a variety of sources but also in different formats significant time and data engineering effort is required to combine data to create the elusive golden record. To compound the challenge, different teams require different views of the same data which demands different flavors of customer 360: The power to harness customer data where it belongs: Business users The traditional approach to creating a customer 360 takes the view that data from disparate sources like Prospecting (outreach.io), CRM(Salesforce), Marketing Automation (Marketo), Web Meetings (Zoom) and Support (Zendesk) needs to be combined, converted into a star or snowflake schema and deposited in an enterprise data warehouse. Or buy a Customer Data Platform (CDP) that makes yet another copy of your data that is a black box with no visibility into how data is processed. Keep in mind, this approach requires months of back and forth with the IT organization, resulting in cost and time overruns that lead to costly, stale customer 360 data that is no longer relevant. Dataworkz gives anyone from marketing ops to sales leaders the ability to assemble a complete, reliable customer 360 in minutes or hours. Unlike a legacy CDP, Dataworkz eliminates data security concerns as a barrier because it does the job without storing customer data. Users configure their sources and destinations, then Dataworkz acts as the “personal data engineer” bringing together data, processing, and ML with a visual no-code interface for business users – with the option to use SQL. A visual, easy-to-use with a no-code approach, to seamlessly gain a 360 view of your entire customer journey is the most innovative and efficient way to gain the contextual and quick customer insights necessary to grow your business. No more kinks in your Customer 360 data: Dataworkz and MongoDB Atlas Getting the best of all breeds is the mantra of smart, future-proof solutions. Capitalizing on Dataworkz and MongoDB Atlas together is a “no brainer.” Connecting Dataworkz and Atlas Add the Dataworkz cluster IP to the list of white listed IP addresses in the “network access” section of the Atlas console. Navigate to the “Databases'' section in the Dataworkz configuration section and specify the Atlas cluster IP address, username and password. Here are three ways this partnership can help: Get out of your RDBMS: Combining data from different sources, in different formats, with changing schema, is exceptionally hard, if not impossible with a rigid relational model. But MongoDB Atlas uses a JSON data model with a flexible schema which makes it ideal for building a customer 360. Work with a flexible, adaptive construct: Agility is a key tenet for organizations constantly adapting to their customer’s ever changing needs. With dynamic schemas, you are no longer worried about changes in source data breaking downstream applications. In minutes, not months: Dataworkz and MongoDB Atlas are SaaS services with usage based pricing. You get started without an upfront investment or hiring a team of data engineers and scientists and get to value in a matter of minutes and not weeks or months. Customer case study - Gunderson Dettmer Gunderson Dettmer is the preeminent international law firm with an exclusive focus on the innovation economy. In order to serve their clients, they need to leverage the latest and greatest information about corporate innovators anywhere in the world, from a wide variety of disparate data sources. To accomplish this, they had to transform and integrate multiple data sources into a coherent, evolving data warehouse and attempt to match financial, legal, and personnel information across “un-mappable” data sets without any common identifiers or keys. The process had to be scalable, reliable, flexible, and continuous since new information and new data sources are added daily. Gunderson needed to compare attributes for venture-backed companies from multiple internal and external datasets, but there were no globally unique identifiers available to join the datasets in most cases, and there were no off-the-shelf, end-to-end solutions to solve the problem. Working with Dataworkz, we developed an automated, scalable fuzzy matching pipeline that feeds likely matches to our internal infrastructure for manual review and approval, ultimately flowing into our MongoDB data warehouse. Maria Concordia, Data Operations and Analytics Architect The objective Gunderson's data warehouse and analytics team were tasked with creating a brand new process for unlocking data insights by weaving together previously un-mappable data sets and serving them up to the organization to create an evolving, holistic view of the Venture Capital universe. The resulting data warehouse would be leveraged by downstream users, including data analysts, upper management, marketing, and other firm stakeholders. The challenge with no single source of truth Gunderson Dettmer needed to compare attributes for companies and venture capital investors across multiple internal and external datasets, but there were no globally unique identifiers available to join the datasets in most cases, and there were no off-the-shelf, end-to-end solutions to solve the problem. Data sources included Gunderson’s on-premise proprietary data held in a SQL-based relational database, CSV data dumps from Pitchbook, Crunchbase, and various PDF files, and other potential sources, as available. Although the data sets overlapped, there were no common keys or identifiers across them. How they used power of MongoDB Atlas + Dataworkz Before the data warehouse team at Gunderson got involved, the de facto approach was to create interim data sets for the entity matching process by extracting data from the relational database, performing multiple Excel spreadsheet lookups to enrich it, and then manually matching the exported data to the external data sources. This process typically took 3-4 weeks in order to create a unified data set that could be used for analytics. The process was static and needed to be repeated (complete with manual record matching) when any of the data sources were updated. Not thrilled with this process, the data warehouse team created python scripts using Request and Fuzzy-Wuzzy modules to call a REST API endpoint, programmatically joining the datasets and then performing fuzzy logic for matching. Unfortunately, the REST API and Fuzzy matched datasets created were also one-time-only events and could not be leveraged for automating future analysis or ongoing data integration. This is where MongoDB Atlas was introduced to change the one-time only dataset into a JSON collection that could be reused. It required transforming the resulting datasets into rich, nested JSON objects that could be searched and queried using MongoDB Atlas. While this reduced the data set preparation time to about one week, there was still a large human review process that required Subject Matter Experts (SMEs) for difficult matches that fell out of the simplistic, fuzzy match parameters. For the final step in this project, they replaced the python scripts for transforming raw data into the JSON data model with Dataworkz to ingest data into MongoDB Atlas. The Dataworkz platform provided crucial features that vastly simplified the process of weaving the disparate “unmappable” data sets together into a coherent whole. Using Dataworkz allowed Gunderson’s team to eliminate their python scripts and enabled: Discovery, introspection, and manipulation of Parquet, BSON, JSON, CSV, structured, and unstructured data sources by bringing them all onto the same playing field for transformation, A clearer understanding of the possible challenges to creating easy-to-match collections, Automatically perform JSON format transformations without code, The ability to describe and execute no-code multi-stage pattern matching Automate their ingestion, transformation, and data storage pipeline in a day (rather than the months that it took to write the original python scripts), Provide continuous automatic data integration within minutes, with improved accuracy, lower human intervention, and built-in data monitoring and governance to ensure against data stream contamination in the future. Don't struggle any more for the metrics you need Whether you are a Chief Revenue Officer, CMO, head of a regional team, leading customer service, or a data analyst/scientist; you no longer need to spend extended time and dollars to gain the insights that should be available at your fingertips. Timely, reliable CAC and CLV metrics that are correct no matter what changes occur in your processes, definitions, calculations, or systems can be yours – when you need them – across departments, product lines, and business units. Dataworkz marks the entry of a new class of data platform that delivers the insights you need – accessible across any data, from any source, combined for any use, when you need it.

April 11, 2023

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MongoDB and Partners: Building the AI Future, Together

If you’re like me, over the past year you’ve closely watched AI’s developments—and the world’s reactions to them. From infectious excitement about AI’s capabilities, to impatience with its cost and return on investment, every day has been filled with AI twists and turns. It’s been quite the roller coaster. During the ride, from time to time I’ve wondered where AI falls on the Gartner hype cycle, which gives "a view of how a technology or application will evolve over time." Have we hit the "peak of inflated expectations" only to fall into the "trough of disillusionment?" Or is the hype cycle an imperfect guide, as The Economist argues? The reality is that it takes time for any new technology—even transformative ones like AI—to take hold. And every advance, no matter how big, has had its detractors. A famous example is that of Picasso (!), who in 1968 said, “Computers are useless. They can only give you answers.” (!!) For our part, MongoDB is convinced that AI is a once-in-a-generation technology that will enhance every future application—a belief that has been reinforced by the incredible work our partners have shared at MongoDB’s 2024 events. Speeding AI development MongoDB is committed to helping organizations of all sizes succeed with AI, and one way we’re doing that is by collaborating with the MongoDB partner ecosystem to create powerful, user-friendly AI development tools and solutions. For example, Fireworks.ai —which is a member of the MongoDB AI Applications Program ecosystem —created an inference solution that hosts gen AI models and supports containerized deployments. This tool makes it easier for developers to build and deploy powerful applications with a range of easy-to-use tools and customization options. They can choose to use state-of-the-art, open-source language, image, and multimodal foundation models off the shelf, or they can customize and fine-tune models to their needs. Jointly, Fireworks.ai and MongoDB provide a solution for developers who want to leverage highly curated and optimized open-source models and combine these with their organization’s own proprietary data—and to do so with unparalleled speed and security. “MongoDB is one of the most sophisticated database providers, and it’s very easy to use,” said Benny Chen , cofounder of Fireworks.ai. "We want developers to be able to use these tools, and we want to work with providers who enable and empower developers." Nomic , another MAAP ecosystem member, also enables developers with best-in-class solutions across the entire unstructured data workflow. Their Embed offering, available through the Nomic API , allows users to vectorize large-scale datasets for use in text, image, and multimodal retrieval applications, including retrieval-augmented generation (RAG), using only their web browser. The Nomic-MongoDB solution is a highly efficient, open-weight model that developers can use to visualize the unstructured datasets they store in MongoDB Atlas . These insights help users quickly discover trends and articulate data-driven value propositions. Nomic also supported the recently announced vector quantization in MongoDB Atlas Vector Search , which reduces vector sizes while preserving performance. Last—but hardly least!—there’s our new reference architecture with MAAP partners AWS and Anthropic. Announced at MongoDB.local London , the reference architecture supports building memory-enhanced AI agents, and is designed to streamline complex processes and develop smarter, more responsive applications. For more—including a link to the code on Github— check out the MongoDB Developer Center . Making AI work for anyone and everyone The companies MongoDB partners with aren’t just making gen AI easier for developers—they’re building tools for everyone. For example, Capgemini has invested $2 billion in gen AI and is training 100,000 of its employees in the technology. GenYoda, a solution that helps insurance professionals with their daily work, is a product of this investment. GenYoda leverages MongoDB Atlas Vector Search to analyze large amounts of customer data, like policy statements, premiums, claims history, and health information. Using GenYoda, insurance professionals can quickly analyze underwriters’ reports to make informed decisions, create longitudinal health summaries, and streamline customer interactions to improve contact center efficiency. GenYoda can ingest 100,000 documents in just a few hours and respond to users’ queries in two to three seconds—a metric on par with the most widely used gen AI models. And it produces results: in one example, by using Capgemini’s solution an insurer was able to increase productivity by 15%, add new reports 25% faster (thus speeding decision-making), and reduce the manual effort of searching PDFs, increasing efficiency by 10%. Building the future of AI together So, what’s next? Honestly, I’m as curious as you are. But I’m also incredibly excited. At MongoDB, we’re active participants in the AI revolution, working to embrace the possibilities that lie ahead. The future of gen AI is bright, and I can’t wait to see what we’ll build together. To learn more about how MongoDB can accelerate your AI journey, explore the MongoDB AI Applications Program .

November 4, 2024