The winner of the best middle-office initiative category at the 2018 American Financial Technology Awards is New York-based Indus Valley Partners (IVP), thanks to its IVP Robo Recon platform. IVP previously won this category in 2016, while in 2017 the award went to data specialist RIMES Technologies.
According to Gurvinder Singh, CEO of Indus Valley Partners, most of the middle-office services platforms currently on the market tend to be Excel-driven. “We gave our clients full access to the platform so that once the raw data comes in they can actually see how the engine works and what people are doing,” says Singh. “It is all about full transparency—they are not just getting back reports. That has been the other problem in the middle-office space where the relationships are done through Chinese walls. Now, it is almost like both sides are sitting on the same system and they can see what each other is doing in a clean, auditable way.”
IVP Robo Recon is designed to meet the reconciliation needs of alternative asset managers with the help of machine learning and natural-language processing (NLP) technology. This new AI capability learns how to identify breaks, flags non-normal activity and automatically suggests resolutions. When IVP began looking at machine learning three years ago, reconciliation functions were one of the earliest areas it applied the technology to. “More and more people are asking us to make a golden copy of their security master transaction data,” says Singh. “And more people are asking us to make a golden copy for their IBOR (investment book of record) as well. Our teams are responsible for bringing in all of this data, processing it and applying all the rules to it. And now they are being assisted by our master data management solutions and robotic process automation (RPA).”
In terms of its reconciliation service, the vendor has approximately 70 clients with around 1,000 users. IVP Robo Recon currently supports $160 billion in AUM, across 300 portfolios and 80-plus counterparties. “We were able to come to a point where our suggestion engine was effectively reducing the workflow on the exceptions by 50 to 60 percent on our managed services team,” Singh explains. “Over time, we were actually able to start pricing our solution not by how many people were working on it, but by how many accounts we were reconciling based on the asset mix. So that was really an interesting first that we were able to bring to the market.”
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