The solution is designed to tackle critical issues faced by finance teams—particularly in offline retail—including discrepancies, fraud risks, money loss incidents, and inefficiencies in manual reconciliations
Bengaluru, Karnataka, India
With an intent to reform financial data management for businesses, Razorpay POS, the offline payments arm of the fintech major, today announced the launch of ‘Razorpay Recon’, an industry-first AI-powered reconciliation solution to streamline and automate financial operations for businesses. The solution aims to empower businesses to manage large volumes of transactions with greater accuracy, boosting the efficiency of financial operations by 80%.
What is Reconciliation and why is it important?
Simply put, reconciliation of transactions refers to the process of verifying and matching the records of financial transactions such as bank statements, payment gateways, invoices, and internal transaction logs between two or more systems to ensure accuracy and consistency. Reconciliation is crucial because it helps businesses identify discrepancies, such as missing transactions, duplicate entries, or errors in amounts, which can lead to financial loss or fraud if left unaddressed.
In today’s fast-paced business environment, managing reconciliation has become increasingly complex due to the sheer volume and variety of transactions. Digital payments in India continue to surge, with a year-on-year (YoY) transactional volume growth of 42% in FY 2023–24, which is poised to grow by three times in FY 2028–29. Many businesses deal with multiple payment methods—online, offline, and custom – which makes manual reconciliation not only time-consuming but also error-prone. Additionally, the use of different systems for payment collections, disbursals, and financial reporting adds to the chaos, creating separate data sources that are difficult to align. This fragmented approach often results in undetected discrepancies, delayed action on financial issues, and increased risk of fraud, ultimately threatening the financial stability and compliance of businesses.
Razorpay Recon addresses these challenges by streamlining financial reconciliation processes and providing businesses with better control over their financial operations. Leveraging the power of AI, Razorpay Recon aims to enable businesses to reconcile transactions, as many as 200 million per month, more accurately, reducing manual effort and minimizing errors, thereby improving financial operations productivity by 80%. The solution is designed to tackle critical issues faced by finance teams—particularly in offline retail—including discrepancies, fraud risks, money loss incidents, and inefficiencies in manual reconciliations. Razorpay Recon supports multiple types of reconciliation, including online (such as payment gateway transactions, settlements, and bank statements), offline (POS transactions, bank files, and cash statements), and custom use cases tailored to industry-specific needs.
Commenting on the launch, Kunal Gothivarekar, Head of Razorpay POS said, “As businesses scale and transaction volumes increase, managing financial reconciliation has become more complex and time-consuming. At Razorpay, we recognize that this challenge can lead to costly errors and inefficiencies, especially for enterprises with diverse payment methods. Razorpay Recon isn’t just a tool—it’s a transformative solution designed to automate and simplify the reconciliation process, saving businesses valuable time and resources. By leveraging AI and advanced analytics, we’re helping businesses not only stay on top of their financial data but also proactively identify discrepancies and mitigate risks. With Razorpay Recon, businesses can now focus on growth and innovation, leaving behind the burdens of manual reconciliation.”
Razorpay Recon offers a user-friendly, self-serve dashboard that simplifies the reconciliation process. Unlike traditional systems, Razorpay Recon not only automates the reconciliation of financial records but also provides advanced analytics to help businesses identify mismatches, uncover discrepancies, and track trends in their financial data over time.
