AI ለባንክ ቁጥጥር ሪፖርት

Banks file a steady stream of regulatory reports. In the US, most insured banks file the quarterly Consolidated Reports of Condition and Income, known as the Call Report, using FFIEC forms 031, 041 or 051 depending on size and activities, submitted through the FFIEC's Central Data Repository. Bank holding companies file reports such as the FR Y-9C, and the largest firms file detailed stress-testing data. Other jurisdictions have their own regimes. Filings pass through validation edits that check mathematical relationships and flag unusual values that banks must explain. The underlying challenge is data. Reported numbers are assembled from many source systems through layers of transformation. The Basel Committee's principles for effective risk data aggregation and risk reporting, known as BCBS 239 and published in 2013, set expectations for accuracy, completeness, timeliness and governance of risk data at large banks, and supervisors have repeatedly found that banks struggle to meet them. AI helps in three main ways. Lineage discovery uses code parsing and pattern matching to map how data moves, filling gaps in documentation. Validation and anomaly detection go beyond fixed edits by learning normal patterns and flagging surprising values or relationships before submission. Rule interpretation uses language models to summarize new instructions, compare versions and draft mapping proposals from regulatory text to internal data. The central constraint is auditability. Regulators, internal audit and external auditors must be able to trace a figure to its source and understand the logic that produced it. A common misconception is that AI can generate report figures directly. In practice, reported numbers should come from deterministic, controlled calculations; AI assists by finding problems, documenting lineage and speeding interpretation, with humans approving changes. Models used this way still fall under model risk management expectations, such as the US supervisory guidance known as SR 11-7.