FDIC · FDIC-62 · record R1082

Restitution Order AI Assistance

Federal Deposit Insurance Corporation · Division of Resolutions and Receiverships

Overview

Development stage: In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: Not reported
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Other
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

The current process for managing restitution order data lacks efficiency, making it difficult to effectively identify, track, classify, and resolve these orders. This hinders the FDIC's ability to maximize recoveries from failed financial institutions. Implementing a strategic solution to enhance the identification, tracking, classification, and resolution of restitution order data will improve efficiency and ultimately support the FDIC's mission to maximize recoveries from failed financial institutions.

Expected benefits

This is a strategic solution to enhance the identification, tracking, classification, and resolution of restitution order workload, ultimately supporting the FDIC’s mission to maximize recoveries from failed financial institutions The outputs will include restitution order dataset, case resolution report, consolidated recovery statement, loss estimation and asset tracking metrics.

System outputs

Restitution order data in structured format.

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): Not reported
Custom-developed code: Not reported
Public source code: Not reported

Sourcing

How it was built: Not reported
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: Not reported
Privacy Impact Assessment: Not reported
Authorization to Operate: Not reported
Demographic features: Not reported
Training and evaluation data: Not reported
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.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported