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Xceptor launches AI reconciliation rule builder
10 June 2026 UK
Image: WrightStudio/stock.adobe.com
Xceptor, a global provider of data automation for capital markets, has launched AI Reconciliation Rule Builder and Live Tester to improve ease of building and testing reconciliation logic for operation teams.
Initially available within Xceptor Reconciliations SaaS, the functionality uses AI and Xceptor’s proprietary evaluation logic to generate rule suggestions for humans to review and test before deployment.
AI Reconciliation Rule Builder allows operations teams to move faster by automatically generating matching and comparison rules from sample data.
With Live Tester, users can review, refine, and test these rules against that same data in a controlled environment, allowing issues to be identified and corrected before going live.
By enabling non-technical users to build reliable logic quickly, Xceptor is lowering the barrier to adoption and reducing the time from source file upload to a working rule configuration.
Xceptor says internal tests showed AI Reconciliation Rule Builder could cut rule validation time by more than half and reduce manual effort across rule creation and testing.
John Bevil, senior product manager for reconciliations, Xceptor, notes: “Reconciliation logic needs to be transparent, defensible, and consistently applied. Building this manually at scale places unsustainable pressure on operations teams and leaves too much room for error.
“Our AI Builder changes that by removing the hardest part — manually mapping and configuring rules from scratch — and enabling users to test and validate those rules inline before anything goes live.”
Vinod Jain, founder, Adkrest, an independent industry research firm, adds: “Reconciliation is one of the strongest control tools, yet gaps in data filtering, matching logic, and incomplete testing continue to limit its full value. Xceptor’s AI Reconciliation Rule Builder and Live Tester address this by delivering a robust engine and identifying control gaps during testing.
“AI helps teams build rule sets without logic gaps and flips the testing model entirely — generating the full range of test scenarios against the rule engine and reconciliation process, while eliminating the manual grind of code validation and test cycles.”
Initially available within Xceptor Reconciliations SaaS, the functionality uses AI and Xceptor’s proprietary evaluation logic to generate rule suggestions for humans to review and test before deployment.
AI Reconciliation Rule Builder allows operations teams to move faster by automatically generating matching and comparison rules from sample data.
With Live Tester, users can review, refine, and test these rules against that same data in a controlled environment, allowing issues to be identified and corrected before going live.
By enabling non-technical users to build reliable logic quickly, Xceptor is lowering the barrier to adoption and reducing the time from source file upload to a working rule configuration.
Xceptor says internal tests showed AI Reconciliation Rule Builder could cut rule validation time by more than half and reduce manual effort across rule creation and testing.
John Bevil, senior product manager for reconciliations, Xceptor, notes: “Reconciliation logic needs to be transparent, defensible, and consistently applied. Building this manually at scale places unsustainable pressure on operations teams and leaves too much room for error.
“Our AI Builder changes that by removing the hardest part — manually mapping and configuring rules from scratch — and enabling users to test and validate those rules inline before anything goes live.”
Vinod Jain, founder, Adkrest, an independent industry research firm, adds: “Reconciliation is one of the strongest control tools, yet gaps in data filtering, matching logic, and incomplete testing continue to limit its full value. Xceptor’s AI Reconciliation Rule Builder and Live Tester address this by delivering a robust engine and identifying control gaps during testing.
“AI helps teams build rule sets without logic gaps and flips the testing model entirely — generating the full range of test scenarios against the rule engine and reconciliation process, while eliminating the manual grind of code validation and test cycles.”
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