How Can AI Improve the Quality of Middle Office Controls?
By shifting from sample-based verification to comprehensive verification, where humans now handle only the exceptions. AI doesn’t validate anything—it prioritizes, and that’s precisely what the teams are missing.
Three concrete benefits. First, detection: anomalous variations from one quarter to the next, inconsistencies between sources, and incomplete fields—across all rows, not just a sample. Next, explanation: each alert must be justified, including the rule that triggered it and the values in question; otherwise, the team will stop reading the alerts. Finally, traceability: who resolved the exception, on what basis, and with what supporting documentation, so that both the regulator and the valuator can follow the trail.
The Bodic Apps Middle Office module applies this logic to data shared with the rest of the platform, eliminating the need for cross-tool consistency checks. The definition of the control plan itself—including thresholds, priorities, and responsibilities—is part of the framework established in collaboration with Bodic Conseil.
Three concrete benefits. First, detection: anomalous variations from one quarter to the next, inconsistencies between sources, and incomplete fields—across all rows, not just a sample. Next, explanation: each alert must be justified, including the rule that triggered it and the values in question; otherwise, the team will stop reading the alerts. Finally, traceability: who resolved the exception, on what basis, and with what supporting documentation, so that both the regulator and the valuator can follow the trail.
The Bodic Apps Middle Office module applies this logic to data shared with the rest of the platform, eliminating the need for cross-tool consistency checks. The definition of the control plan itself—including thresholds, priorities, and responsibilities—is part of the framework established in collaboration with Bodic Conseil.