A leading Fin-Tech lending company in Asia

Challenges

• KYC was a manual process and required the collection of a number of documents. Our team had to study the compliance laws and design an interface that would automate document collection, verification and reduce the total time taken for KYC.
• Underwriting was manual and required the collection of documents for income proof. Our team along with the underwriting officers created machine learning models to calculate income, liabilities and risk.
• Compliance and regulations required complex reports to be automatically generated on a daily, monthly and quarterly basis.
• The system had to be periodically audited by authorities to ensure it complied with laws and met regulatory requirements.

Solution

• Wednesday worked with the customer in a product development capacity. We setup a team of engineers, designers and a project manager to work closely with the customer to build a product that adds values to their business.
• Our design team took an agile approach. Wireframes were built and verified by showing them to users. They team also built a design system that included a suite of components, themes and types. This allowed the developers to build products for mobile and web with the same visual language.
• Our engineering team built the backend using Go. Integrations with payment gateways, third party lenders, account management, loan management, treasury, notifications, reporting and a configuration system to meet the regulatory requirements where some of the features that we built. The backend exposed a RESTful API.
• The frontend for the user facing portal, admin port and the treasury were built using React.
• The customer facing mobile app was built using the Android SDK. The application was built to be offline first and allowed user to be able to perform actions even when offline. A sync mechanism was built to allow data to sync back to the backend when network was restored.
• The team followed a strict software development process ensuring work was divided into sprints. After each sprint a build was released that was production ready. Each feature had atleast 70% test coverage and thorough documentation.

Results

• The use of machine learning models to analyze risk ensured there was constant learning from each loan which lead to a gradual reduction in NPAs.
• Time taken to onboard a new customer was reduce by 80% and completely automated without any manual intervention.
• Card processing and issuance was automated and required no manual intervention.
• Reporting was automated which lead to the reduction in time spent to meet compliance and regulatory requirements.
• Documentation and a configuration system