FDIC · FDIC – 52 · record R1074

Trust Document Entity Extraction

Federal Deposit Insurance Corporation · Division of Resolutions and Receiverships

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: 2024-09 (month precision)Source: 09/2024
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Other
Operational functions: Document processing and extraction Derived by keyword rules; see methodology

Problem the AI is intended to solve

Currently, claims specialists spend countless hours manually reviewing trust agreements to extract grantor/settlor and beneficiary information. Implementing OCR and Llama to automate key data extraction from these agreements will significantly make the process faster, more efficient, and more accurate while eliminating the potential for manual errors.

Expected benefits

This will reduce the time and cost to transpose information from Trust Agreements.

System outputs

The extracted data, which will include key data elements, such as grantor/settler and beneficiaries from Trust Agreements, along with the original Trust Agreement will be presented to users through an application user interface.

Technology

AI classification: Natural language processingSource: Natural Language Processing: AI that processes, interprets, and shares information in human language.
System name(s): Resolution and Receivership Management Portal (RRMP)
Custom-developed code: Yes
Public source code: Not reported

Sourcing

How it was built: Contract and in-houseSource: c) Developed with both contracting and in-house resources
Vendor (as reported): Deloitte
Vendors (standardized): Deloitte

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

Data and privacy

Involves PII: Yes
Privacy Impact Assessment: www.fdic.gov ↗
Authorization to Operate: Yes
Demographic features: k) None of the above
Training and evaluation data: In-house Trust Documents
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