It’s no wonder that credit card giants have failed to enter the Indian market because conventional credit networks focus on confidence and faith rather than numbers. Just 59.4 million credit cards are in circulation, compared to 829.4 million debit cards, according to a Reserve Bank of India (RBI) survey.
Consumers have a strong aversion to using credit cards. The most significant cause of this aversion is the so-called “secret costs,” or the numerous processing fees that credit card companies charge their clients. Annual servicing fees, cash advance fees, gasoline surcharges, and GST fees are just a few of the additional costs that a credit card customer agrees to by using the card.
With the rise of e-commerce and e-commerce-specific credit items, Indians can now borrow in new ways. That is where Simpl, an online credit wallet, comes into the picture. Simpl is creating the credit-based payments of the future for India’s mobile-first consumer: completely digital, frictionless, secure, inclusive, and customized.
Simpl is scaling the idea of a khata or tab (book of accounts) used by merchants to accommodate their daily customers, taking inspiration from the age-old concept of a khata or tab (book of accounts) merchants to serve their regular customers. Consumers can aggregate their purchases with Simpl’s Buy Now Pay Later line of credit, get good customer security and quick chargebacks, while merchants can have a secure and smooth 1-click checkout experience. Simpl helps retailers improve cart transfer, basket capacity, and order volume while also driving consumer satisfaction and engagement by powering this best-in-class mobile-first shopping experience.
India’s financial sector can be oversimplified in terms of debit and credit transactions from the consumer’s viewpoint. Although paying with cash is easy, paying with credit is a little more complicated. Based on various criteria, banks and financial services companies determine whether or not an individual is suitable for the money in the future. On the other hand, credit is deeply rooted in Indian customs, with structures such as house accounts or informal tabs (known locally as khata) held by merchants for their most faithful and long-term clients.
When Chaitra Chidanand returned to India from the United States in 2014, she was shocked to find that she was ineligible for a credit card because she had never paid taxes in India and had no permanent address. To make matters worse, obtaining a bank account and a debit card took her eight weeks.
After co-founder Nityananda Sharma’s credit card application was denied after returning to India from the United States, he began collecting khata credit lines from local merchants. Sharma agreed to resolve the problem with technology and collaborations with retailers after observing the offline relationship between merchant and customer and the challenges and inconveniences in obtaining a credit card.
In 2016, the pair introduced Simpl after realizing how complex India’s financial processes and goods are. Simpl Pay, headquartered in Bengaluru, is an online payment method that acts as an extra credit wallet, allowing customers to order now and pay later.
Via their mobile-first app with a one-click checkout feature for online credit, Simpl has found an adequate middle ground to provide options to customers who are either new to lending services or don’t require big loans. Simpl, which launched in 2016, is a mobile-first short-term lending network that provides fast credit at points of sale and during e-commerce checkout. The organization claims to give consumers a one-click experience by maintaining ease of use at the forefront of BNPL operation.
For timely payments, Simpl relies on data, architecture, communication, and a network. The startup’s success, according to the co-founder, can be attributed to its use of technology, data, and credit risk management, as well as a lot of bravery. The core model is made up of polyglot microservices, primarily written in Ruby, GoLang, or Python. To ensure high availability, the startup used AWS and its multi-AZ capabilities. It also used a combination of open-source and proprietary DevOps software.
To scale its commodity, Simpl has developed a framework that includes an integrated anti-fraud and underwriting model. Since the portal is invite-only, it draws a positive-selected audience of comfort seekers (rather than credit seekers) committed and trusted clients, resulting in an intrinsic risk reduction system.
According to the co-founder, credit enables the platform to have a superior payment experience (industry-high performance rates, checkout speed, and reimbursement performance). Simpl has developed a lot of proprietary technologies in-house using AWS as the backbone. The startup has grown from servicing a single retailer — Rebel Foods’ cloud kitchen brand Faasos — to over 1,000.
Using data to its full potentialAccording to the co-founder, the startup is strategically positioned to allow retailers to deliver personalized incentives to the right customers directly and in real-time using this proprietary data. Machine learning (ML) algorithms support Simpl’s affinity and credit models. The startup needed to widen its customer funnel to spot “bad behaviour,” which would enable the team to develop anti-fraud mechanisms and deter “bad” customers from accessing the system.
Simpl also eliminated all unfavourable interactions for first-time customers, who would otherwise be refused a line of credit, in addition to preventing adverse filtering by its pre-approval process. Onboarding Simpl for merchants necessitates the incorporation of Simpl’s SDK (software development kit). The business then runs on a set of APIs (application programming interfaces) that allow it to stay light.
The journey aheadSimpl’s operations rely heavily on data processing. According to Nityananda, the platform receives millions of signals every day that must be analyzed, stored, and interpreted to make large-scale credit decisions. The platform’s data collection engine compiles organized and unstructured data from its network to aid consumer behaviour prediction. Various user touchpoints dump data points into the event store, which are then asynchronously parsed by various subsystems to extract the required ones.
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