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Financial Services

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

Underwriter throughput

● The challenge

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.

● What we built

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
● Executive KPI board

Outcomes measured, not promised

$41M

Annualized fraud avoidance

6h

Median SMB credit decision

0

Regulatory findings on model risk

+11 pts

Underwriter satisfaction

● ROI model

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.

● Reference architecture

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
Fiserv DNAnCinoSnowflakeOkta

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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