SmartStream, the financial transaction lifecycle management (TLM) solutions provider, has announced the launch of SmartStream Air version 7 – to further control exceptions management using advanced cloud-native AI technology.
AI exceptions management processes learns from user behaviours and is designed to help reconcilers focus on other tasks, which in turn helps businesses reduce costs when managing their reconciliations.
SmartStream Air’s AI for exceptions management will populate fields automatically, set labels, and run automations – all based on the processes it has learned. In addition, it will be able to provide suggestions to the user or fully automate the process – significantly improving STP rates.
Currently exception management is controlled by the users or user defined automation rules. In contrast, the new version learns from a user’s day-to-day actions when managing exceptions and proposes how the exception attributes should be populated. It will help with the cumbersome and work-heavy exceptions management process by learning directly from user actions or historic data – and when the model is trained, the AI can predict fields and workflows for managing exceptions.
Jethro MacDonald, product manager – artificial intelligence and machine learning, SmartStream, states: “Our new SmartStream Air version 7 will massively reduce the time spent working on managing exceptions that arise from unmatched data, or data integrity issues. AI technology will automatically classify the priority, assign to relevant teams, add labels including statuses, and populate exception detail fields, allowing reconcilers to manage their work more effectively. We feel this is what the market has been waiting for and we are proud to be the first to deliver such capabilities”.
SmartStream goal is to integrate AI exceptions management to its suite of middle- and back-office processing solutions, this means that its established client base will experience the operational benefits that advanced AI-enabled technology brings.
Francis Bignell
