New Offsite Models

Overview

Development stage

PilotSource: b) Pilot – The use case has been deployed in a limited test or pilot capacity.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

Not reported

Start date

2018-01 (month precision)Source: 01/2018

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Information Technology

Operational functions

No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

This machine learning (ML) model is intended to identify banks most likely to be downgraded at or before the next examination.

Expected benefits

An improved methodology of identifying potential downgrades for use in our quarterly offsite review program. This provides information on potentially deteriorating financial conditions in the better rated banks across the country.

System outputs

Identify banks more likely to be downgraded.

Technology

AI classification

Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…

System name(s)

ViSION

Custom-developed code

Yes

Public source code

Not reported

Sourcing

How it was built

Developed in-houseSource: b) Developed in-house

Vendor (as reported)

Not reported

Vendors (standardized)

None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

No

Privacy Impact Assessment

Not reported

Authorization to Operate

Yes

Demographic features

k) None of the above

Training and evaluation data

Call Report data, bank ratings, and macroeconomic data

Federal Data Catalog

Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

Not reported