Banks and other financial institutions are dependent on data quality to support automated processes that can streamline their operations. By automating key elements of the transaction workflow, institutions can reduce the number of exceptions and their associated costs, optimising operational processes and overall operational efficiency.
But data quality can be a barrier to realising the promise of automation. The complexity of today’s marketplace means that many banks are forced to deal with huge volumes of transaction data, across an array of inputs and formats. Legacy manual data validation techniques are withering under the data deluge, and the resulting data quality issues have implications for straight-through processing rates.
New AI techniques – most notably the concept of observational learning – can help address these data quality issues by building on reconciliation systems’ ‘knowledge’ of the firm’s data preferences, allowing financial institutions to reduce the number of data issues that need attention and speed up the mitigation process for those that remain.
This paper looks at the data challenges that are hindering firms’ attempts to improve STP rates, and explains how observational learning can help. It discusses specific use-cases for AI and observational learning in critical operational and regulatory processes, and describes how SmartStream has added the Affinity observational learning capability it developed in its Innovation Lab to its SmartStream Air cloud-native reconciliations offering.