Arkadeep Banarjee

Data entered into QuickBooks has real financial implications - a single data entry mistake can be expensive for the business requiring significant effort in undoing the impact. Customer research highlighted the following top concerns: Error in entering invoice data, Error in paying employees, Fat finger a bill payment to a new vendor.. We needed a system which will flag errors on key fields while entering transaction data, and nudge the user towards the correct range. AI solutions are now norm for delivering smart product features to enhance customer experience but most solutions are focused on tailored use-cases that need resources from ideation to deployment and subsequent integration, every time we adopt a new use case. Given the scope of multiple use-cases across data input fields in Intuit products, the error-detection system had to be extensible so that it allows developers to build tailored AI models for specific error detection by providing information about the fields and data sources only. FEDS employs novel methodologies for error detection based on Empirical Bayesian Test, Likelihood Ratio Test and Deep Learning approaches. It decouples the efforts of Data Scientists from the underlying implementation layer, which facilitates the product developers to easily adopt across multiple use cases.