Choco Up Makes Credit Decisions for Clients in 1 Hour with Revenue-Based Financing Platform on AWS
Starting a business comes with a series of challenges, one of the greatest being access to capital. Entrepreneurs often struggle to obtain financing from traditional banks, as they lack the collateral required for loans. This is certainly the case for service-based and digital businesses with a cash-flow business model.
Choco Up is a fintech platform based in Hong Kong and Singapore that recognized this funding gap and developed a revenue-based financing platform to serve digital merchants, providing brands and startups with a funding avenue that wasn’t available to them before. Because Choco Up’s founders come from both finance and tech startup backgrounds, they understand the need for quick financing to grow modern small businesses. Clients who obtain financing from Choco Up make payments on their funding based on how much revenue they earn in the months following the disbursement.
By leveraging cloud technology and big data, Choco Up has devised a straightforward process for startups to access quick financing. Clients simply go to Choco Up’s website, click “Get Funded,” and can receive funding in as little as one hour. Choco Up currently serves clients in 10+ markets throughout the Asia-Pacific region, with a focus on digital merchants and startups.
AWS is renowned for its data and analytics infrastructure, which provides a wide range of choices for building and deploying microservices.”
VP of Product, Choco Up
Receiving Roadmap Support as a Startup
Many of the digital merchants Choco Up works with are cloud-native and/or are operating on cloud-based commerce platforms and payment gateways such as Shopify and Stripe. After signing up to receive funding, they authorize Choco Up to access their store data—such as sales volume and average order value—through a direct application programming interface (API) plugin on Shopify or the ecommerce site they’re using.
Choco Up chose to also build its business on the cloud, selecting Amazon Web Services (AWS) based on positive experiences with the platform and its support of startups. “The AWS team spent time understanding our business and providing valuable resources. The fact that they engaged with us before we were a paying customer impressed us,” says Brian Tsang, co-founder and chief operating officer at Choco Up.
Leveraging Microservices to Autoscale and Control Costs
Another important consideration behind the decision to grow on AWS was the technology perspective. Choco Up leverages microservices for its big data architecture, using containers to auto scale and isolate applications alongside serverless code to control costs with on-demand infrastructure.
Choco Up also benefitted from the support of AWS solutions architects and resources such as online documentation, which sped up its adoption of analytics, artificial intelligence (AI), and machine-learning (ML) services. “AWS is renowned for its data and analytics infrastructure, which provides a wide range of choices for building and deploying microservices. The AWS team ensured our data pipeline can handle large volumes of data at speed, while maintaining stability,” says Lewis Pong, VP of product at Choco Up.
Since launching, Choco Up hasn’t experienced any downtime on its platform. The business uses Amazon Elastic Container Service (Amazon ECS) for container orchestration and AWS Lambda to deploy infrastructure as code. Choco Up also leverages Amazon Redshift as a data analytics warehouse for scenario analysis at scale, to help the credit risk team make data-driven funding decisions. Because clients make payments on their funding based on a share of their revenue, it’s critical that Choco Up generate reliable 6- to 12-month revenue projections based on all available data. “We have extensive proprietary data on our database from which we can benchmark each client to evaluate credit risk,” explains Tsang.
Making Quick, Unbiased Credit Decisions
With the power of near real-time data processing, Choco Up can make a preliminary offer of funding as soon as a client completes their application. A full offer is typically generated within the hour, and funds released in as little as 24 hours.
When applying for financing with a bank, on the other hand, companies typically have to wait at least three months for approval and funding. This is a key differentiator for Choco Up, as access to quick capital, even relatively small amounts such as $10,000, is crucial to online merchants’ ability to run marketing campaigns during peak periods such as Black Friday or Cyber Monday.
Additionally, Choco Up prides itself on its lack of bias in credit decisions. “In today’s day and age, a lot of things happen virtually, and we don’t necessarily meet clients face to face, so we rarely rely on relationships or “gut feeling” like traditional institutions do. As a result, we can underwrite objectively and look at each store from a pure performance perspective,” Tsang says.
Reducing Underwriting Risk with Predictive Modelling
After data passes through Choco Up’s data warehouse, before a final funding decision is made, it runs through AI and ML models built in Amazon SageMaker to help the credit risk team perform further validations. “Credit underwriting is a never-ending process because you can never be too careful,” Tsang says. “With increasing use of AI and ML models, we can process much more data, structured or unstructured, in a meaningful way.”
Choco Up is currently exploring how to integrate more AI and ML into its credit risk engine, using Amazon SageMaker for new use cases such as evaluating a client company based on how they engage with customers online. For example, when a customer leaves a comment on a social media or marketplace site, it evaluates client response time. “This type of unstructured data could be a strong indicator of the success of a client’s business. And the ability to bring in more of it using AI and ML will be very helpful for us in credit decision making going forward,” Tsang explains.
Scaling to New Markets while Maintaining Stability
Choco Up has been growing rapidly since its launch, serving new clients in Malaysia, Thailand, and Indonesia, and plans to expand the business further in the Asia-Pacific region. It processed $60 million in funding in 2021, is targeting $150 million in 2022, and projects to extend to $500 million in funding in 2023—leveraging programs such as AWS Activate and scalable, modern technology to grow the business.
“Because we operate 24/7 with clients all over the world, the stability and scalability of AWS is critical to maintaining our platform,” says Tsang. “AWS is able to keep supporting us as we grow into another stage as a startup, including our current phase of scaling up and marketing.”
To learn more, visit aws.amazon.com/big-data/datalakes-and-analytics/
About Choco Up
Choco Up is a revenue-based financing platform based in Hong Kong and Singapore. It offers digital merchants and startups flexible growth capital from $10,000 to $10 million, often making preliminary funding offers within 1 hour of a customer applying for capital.
Benefits of AWS
- Gains 400% growth in amount of funding disbursed in 2021
- Maintains 100% uptime in first year of operations
- Controls costs with serverless and microservices
- Receives ongoing roadmap, technology, and marketing support
- Projects revenue for next 6–12 months using ML
AWS Services Used
Build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows.
Amazon Elastic Container Service (Amazon ECS)
Amazon ECS is a fully managed container orchestration service that makes it easy for you to deploy, manage, and scale containerized applications.
AWS Lambda is a serverless, event-driven compute service that lets you run code for virtually any type of application or backend service without provisioning or managing servers. You can trigger Lambda from over 200 AWS services and software as a service (SaaS) applications, and only pay for what you use.
Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and machine learning to deliver the best price performance at any scale.
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