Adoption of artificial intelligence (AI) and machine‑learning technologies is on the rise as firms push boundaries to increase automation. In response to the growth of these technological phenomena, the Inside Market Data and Inside Reference Data Awards this year introduced a new category, Best AI/Machine Learning Data Initiative, to recognize these projects, and Indus Valley Partners (IVP) has landed the inaugural award.
IVP specializes in portfolio management technology for alternative asset managers, and has brought machine learning to the middle office by building an artificial engine into its IVP Robo Reconciliation offering. According to the judges, the offering “stands out for its anticipation of future market trends and needs,” and is a “proper AI application doing real work.”
The capability improves efficiency by learning how to identify breaks, and it automatically flags abnormal activity and suggests resolutions in the data in real time via a “suggestion engine.” IVP CEO Gurvinder Singh says the result for clients using the new offering has been that the workload in reconciliations has decreased, which helps free up capacity in the team. Going forward, clients are set to continue seeing improved efficiencies when leveraging the offering, as Singh says the technology is rapidly improving. “Our managed services teams initially had 40 percent match rates, and doubled it within a year,” he says.
IVP Robo Reconciliation is the first case of IVP showcasing its use of machine learning, but since its release the vendor has also delivered several other implementations with similar capabilities in other areas of its product suite. “We’ve been playing with these technologies for the past five years, and we started this journey with reconciliations as we felt there was a natural use case here,” he says, adding that there are a number of repetitive tasks within data management where AI can be applied to improve automation, data exception classification and resolution.
Earlier this year, IVP released a data analyzer, which enables clients to process and rate non-numeric datasets. “As firms are starting to leverage alternative datasets—which could be textual in nature like websites and blogs—as inputs into investment processes, they need new tools and capabilities to process them,” says Singh. With the new capability from IVP, portfolio managers can, for example, create models to identify early indicators of alternative data affecting price movement.
In addition, IVP has a new IVP Bot, which allows users to query data in their natural language by, for example, asking questions such as ‘what is my exposure to a specific country or region?’ “This really enhances the user experience,” says Singh.
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