Tas/Agency Location Code Mapping Anomaly Detection
Description
Detect unusual patterns in TAS/agency location code mapping using transaction features, user behavior, timing, amount, fund/account, and historical peer benchmarks. The MVP would connect Treasury data, CARS, DDS/ADS, disbursing records, Advana FBWT dashboards and produce read-only recommendations for DFAS, OUSD(C), Reporting Entities.
AI / analytics pattern
ML anomaly detection
Automation level / stage
human-in-the-loop alert triage
Expected benefit
Higher detection coverage, fewer missed exceptions, better prioritization of high-risk items.
Audit / financial statement impact
Balance Sheet FBWT; SBR outlays; Statement of Changes in Net Position
Controls / human review
Human approval required before posting, payment, denial, personnel action, or official audit response; model validation; drift monitoring; exception sampling; full prompt/data/output logging. Do not use alerts as sole basis for adverse action; require sampled validation and feedback loop.
Data needed
Treasury data, CARS, DDS/ADS, disbursing records, Advana FBWT dashboards; master/reference data; audit logs; policy/control requirements; prior exceptions; relevant document evidence.
Possible metrics
precision/recall of alerts; dollars reviewed; false-positive rate; high-risk exception closure time
MVP scope
Start with one Component/reporting entity and one subprocess (TAS/agency location code mapping) for two close/audit cycles; read-only outputs first.
Related material weakness / control objective
Fund Balance with Treasury material weakness