AI Fraud Detection & Underwriting Assist for a Regional Bank
Illustrative reference architecture. This is a composite engagement template built to show how Mopshy structures enterprise work. It is not a real Mopshy client, and every figure, KPI, and quotation on this page is illustrative rather than measured client data.
Real-time fraud models across cards and ACH, plus an underwriter copilot that cuts small-business credit decisioning from days to hours.
−58%
Fraud losses
−72%
False-positive declines
5×
Underwriter throughput
The bottleneck
Rules-based fraud engine misfired at 6.4% false positives, choking legitimate volume. Small-business underwriters spent 60% of time in document wrangling, not credit judgment.
The system
- Ensemble fraud model on cards + ACH with challenger framework and monthly retraining
- Underwriter copilot summarizing tax returns, bank statements, and covenants
- Explainability layer producing adverse-action reasoning aligned with ECOA / Reg B
- Model risk documentation aligned with SR 11-7 for internal validation
Outcomes measured, not promised
$41M
Annualized fraud avoidance
6h
Median SMB credit decision
0
Regulatory findings on model risk
+11 pts
Underwriter satisfaction
What it cost, what it returned
Investment
$6.2M program (year one)
Payback
9 months
Annualized value
$41M fraud + $18M capacity value
Validated by second-line model risk management and internal audit.
How it is built
- Streaming feature pipeline on Kafka + Flink
- Champion/challenger fraud ensemble with human-in-the-loop review
- Underwriter copilot with document intelligence over IRS + bank statement schemas
- Model risk platform: lineage, monitoring, and SR 11-7 documentation
Representative engagement. This case study describes a composite of Mopshy AI's enterprise engagement patterns, delivery methodology, and observed outcome ranges. Company names, individual quotes, and specific figures are illustrative and used to communicate the enterprise pattern rather than to describe a single identified client.
30-minute working session
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