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SCOR and two other FDIC models flag 67 of 244 banks later downgraded

The FDIC's Inspector General found that SCOR, SCOR-Lag and GMS had flagged 67 of the 244 banks rated 1 or 2 that were downgraded from late 2014 to late 2017.

  • 2 for an organization

    The models flagged 67 of the 244 well-rated banks that were later downgraded, which gave supervisors early notice of about a quarter of them.

Depth runs from 1, a convenience or time saved, to 5, a life or a livelihood preserved.

Status
Partly reported benefit.
Evidence

found, the grade of this recordstudiedreportedclaimed

An independent audit report supports the benefit.

The system’s part
Not isolated. The OIG counted how many later-downgraded banks the models had flagged and did not compare supervision with and without them. The report calls SCOR a statistical model that predicts ratings from Call Report data and does not describe any of the three as machine learning. In its response, the FDIC said that it would explore machine learning in early warning models.
Not yet checked
LimitsCosts and harmsLearns from dataLater outcome
Benefit type
Reduced Risk
System
Statistical CAMELS Offsite Rating (SCOR), SCOR-Lag and Growth Monitoring System (GMS) offsite monitoring models
Made by
Federal Deposit Insurance Corporation
Used by
Federal Deposit Insurance Corporation, Division of Risk Management Supervision (Case Managers in six Regional Offices)
Where
United States (national)
Stage
In routine use
Date
, when the source was published
Kind
Aggregate result
Broad benefit
Monitoring Processes, Materials, or Surroundings, Analyzing Data or Information
Collections
Scrutiny and findings
Last revised
1 November 2026

Sources

  1. Audit report, by authors with no tie to the system.

    Federal Deposit Insurance Corporation, Office of Inspector General. Offsite Reviews of 1- and 2-Rated Institutions. Evaluation Report EVAL-20-002, 18 December 2019. Background, pp. 2-3, and section 'Downgraded Institutions', pp. 8-9.

    From December 31, 2014 through December 31, 2017, 244 1-or 2-rated IDIs received composite downgrades to a 3- or 4-rating. Of these, SCOR, SCOR-Lag, and GMS identified 67 of the downgraded institutions at some point during this 3-year period.

Details

AI system

Workflow

SCOR and SCOR-Lag scored quarterly Call Report data and flagged 1- and 2-rated banks with a 50 percent or higher probability of a downgrade within a year. GMS flagged 1- and 2-rated banks in the 98th percentile or higher of downgrade risk over three years, based on rapid growth or noncore funding. Case Managers reviewed each flagged bank within 3½ months after the Call Report date and could recommend a visitation or an earlier examination.

Follow-up

Conditions for replication

The weakness must show in a bank's Call Report data, and the models missed downgrades driven by weak management, credit administration and board oversight. Banks must file accurate Call Reports, and about 25 percent of the missed banks in the OIG's table filed amendments to correct errant data.

Entry details

Sector

Bank supervision.

Purpose

The FDIC built the SCOR, SCOR-Lag and GMS models to flag banks rated 1 or 2 whose quarterly financial data point to a likely downgrade before their next onsite examination.

31 more questions not yet checked

model, release, provider, role, configuration, inputs and outputs, autonomy, human oversight, AI contribution, beneficiaries, reach, distribution and access, duration, every measurement question, every money question, ownership and control, dependencies, cross-border connections, geopolitical implications, replication attempts, availability, institutional decision, grounds, action and result, success criterion, every evidence question, observed or intended, alternative assessments, related entries, other identifiers, category mappings.

Cite this record

Peter H. Vartanian. (2026). SCOR and two other FDIC models flag 67 of 244 banks later downgraded (AIBD-1037). Artificial Intelligence Benefits Database, edition of 1 November 2026. https://petervartanian.xyz/aibd/records/AIBD-1037.html (archived at https://doi.org/10.7910/DVN/AADSII, version 7.0)

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