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Observational Learning Boosts Data Quality, Improves Reconciliations, Cuts Costs of Exceptions

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Large data volumes and manual data validation techniques are making it difficult for firms to achieve levels of data quality required to support seamless transaction processing and regulatory reporting. The problem is exacerbated by MiFID II and other emerging regulations that impose new processes on transaction reporting, including reconciliation of transactions from the trade repository against front-office records.

A solution to the problems of poor data quality and hence poor reconciliations lies in observational learning, a form of AI that learns by mimicking human behaviour and could, according to early indications, greatly reduce reconciliation exceptions and provide significant cost savings.

By applying observational learning disciplines to regulatory reporting, analysts at SmartStream Technology’s Innovation Lab in Vienna have completed proofs of concepts (POCs) with two major banks that succeeded in accelerating the exceptions management process while rapidly and vastly improving data quality. The result was a sustained reduction in error rates and an accompanying drop in operational costs associated with reconciliations in trade and transaction reporting.

SmartStream’s approach, which is detailed in an A-Team Group white paper Deploying Observational Learning for Improved Transaction Data Quality, took the concept of observational learning and applied it to exceptions management algorithms as part of its Affinity AI offering. This allowed Affinity to observe human data verification processes, capture and ‘understand’ them, and ultimately make recommendations for future exceptions.

The results of the POCs, which included Affinity observational learning within SmartStream’s Air cloud native reconciliations platform, show cost savings of at least 20%, with one participant in a PoC recording savings of $20 million. Download the white paper to find out how your organisation could benefit from observational learning.

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