Recko raises $1 million seed funding from Prime Venture Partners

Recko, a fintech startup that enables artificial intelligence-powered reconciliation of digital transactions, has raised $1 million seed funding from Prime Venture Partners. The fintech startup has built a software-as-service-based reconciliation product that keeps track of the complete transaction lifecycle and commercial contracts for organizations. Emerging out of stealth, Recko said that it is at a run rate of reconciling a quarter billion transactions annually. In the first 12 months since launch, Recko has reconciled transactions worth $2 billion and is looking to scale this by 10x in the next year. Its early customers include online grocery firm Grofers, social commerce company Meesho and some of India’s top marketplaces across different industry sectors.

Reco was founded in 2017 by serial entrepreneurs and IIT Gandhinagar alumni Saurya Prakash Sinha and Prashant Borde.

“We see the current settlements and reconciliation process as a massive inefficiency in the payment lifecycle across industries such as banking, lending, insurance, telecom and e-commerce,” said Saurya Prakash Sinha, chief executive and co-founder at Recko. “We are on a mission to streamline every reconciliation flow in the transaction lifecycle and unlock massive efficiency and accuracy gains to make finance functions more strategic, efficient and confident to the last penny,” added Prakash, who has more than 6 years of experience working with sales and product teams at Flipkart, Grofers and PhonePe. Recko is the second startup founded by Sinha, after his first venture Townrush, a hyperlocal supply chain for the on-demand economy, was acquired by Grofers.

With the sudden surge in digital transactions, Reco said enterprises, banks and financial institutions are finding it difficult to keep a track of the money flowing across the organisation. Digital payments in India are expected to more than double to $135.2 billion in 2023 from $64.8 billion this year. According to the RBI, the volume of digital payments is expected to hit 30 billion in 2019. Reco said there is a dire need for an independent third party transaction reconciliation layer to ensure timely settlements among various interacting parties. The reconciliation layer will ensure that the businesses are receiving settlements in accordance with the agreed rate cards, payout cycles, and are able to track and report payment realization, refunds and chargebacks.

Currently, thousands of staff accountants and analysts across the world are putting in hours upon hours doing manual reconciliation for e-commerce, banks and insurance companies. The process is tedious, error prone and time consuming with reconciliation being done using spreadsheet or Excel and by comparing data that is spread across multiple sources, months and systems. Most companies often set aside a certain revenue error percentage to account for reconciliation write-offs because they can't trace an error back to the source.

"Reconciliation can only be solved by a neutral third-party and has traditionally been handled via a brute force approach of spreadsheets and a battery of analysts," said Sanjay Swamy, managing partner, Prime Venture Partners. “Recko’s tech and AI-driven approach has been very well accepted in the industry. We’re delighted to see the progress,” added Swamy.

Recko said it automates reconciliation and allows the data to be traced throughout the entire transaction cycle. It does so by getting connected to payment gateways, banks and merchant's order management system through APIs and helps the merchant in tracking receivables and identifying settlement discrepancies. It enables finance teams to ingest, enrich multiple data sources and reconcile millions of transactions in hours, instead of days, without writing a single line of code. Recko said it reduces manpower by 50 - 80 per cent and keeps a watch over the transactions to ensure money moves between the right parties, at the right time with correct deductions. The firm is now crunching massive volumes of transactional data to digitise organizational financial control and is building machine learning models to identify anomalies, risk and intelligence around money flow.