Akshay Rawat’s Innovative Payment Solutions for the Gig Economy

Akshay Rawat is a seasoned technologist and entrepreneur with over 20 years of experience in building scalable systems for the gig economy. As the engineering lead at Checkr Pay, he focuses on creating sophisticated payment platforms that cater to the unique requirements of gig workers and platform operators. Akshay's approach to technology and product development is shaped by his commitment to simplicity, people over processes, and actionable feedback loops. He has worked at leading technology firms like ThoughtWorks and co-founded companies such as Floh.in and Activesphere. Akshay emphasizes the need for adaptable payment platforms to meet diverse worker preferences, including various payment methods and real-time payments. He highlights the importance of offering a broad range of choices to satisfy worker expectations and retain talent. Akshay also stresses the need for regulatory compliance and the importance of a payment platform that adapts to regulatory changes without burdening companies with legal complexities. To address the complex challenges of the gig economy, Akshay recommends building product features that address common support issues upfront, such as payment fraud and worker identity fraud. He also emphasizes the importance of creating a robust technological infrastructure to manage various payment methods required by gig workers. Akshay describes a layered architecture and microservices approach that effectively manages and abstracts the differences across payment methods. Akshay stresses the need to cater to the diverse integration requirements of customers of varying sizes, offering prebuilt, low-code integration options to smaller customers and a slew of APIs to larger ones. He also emphasizes the importance of public APIs to maintain simplicity and accessibility across the platform. Akshay identifies several critical challenges faced by gig companies when managing payments at scale, including tracking cash flow and dispute resolution. To address these challenges, Akshay recommends implementing real-time low-balance notifications and using machine learning to predict necessary cash reserves. He also suggests validating payment method correctness using services like Plaid to verify bank details accurately. Akshay emphasizes the diversity of payment preferences among gig workers, highlighting the need for tailored solutions that cater to their unique needs. Looking ahead, Akshay sees several emerging technologies significantly impacting gig payment systems, including the growing adoption of RTP technologies, the evolution of fraud detection and security measures, and the integration of payment solutions with background checks. He also mentions the strategic use of next-generation API chatbots and the application of AI in implementing surge pricing based on supply and demand.