Deep dive — chart 3
Chart 3 — lane rate per client segment, baseline vs recent
The chart with the operational + legal bite. Where score-distribution shifts translate into actual intake routing. C3 SIU/fraud-review rate rose 18% to 71%. Other segments stable. That is the exact adverse-impact signature the four-fifths test flags.
Triage-lane rate per client segment — baseline (left) vs recent (right). C3 SIU/fraud-review rate rose sharply while other segments stayed stable.
What you are looking at
- Grouped bars per client segment (C1 / C2 / C3 / C4 / C5)
- Three colors per segment: Fast-Track Settle (green) / Standard Adjuster Review (blue) / SIU / Fraud Review (red)
- Left cluster: baseline (first 30 days) — the "normal" routing per segment
- Right cluster: recent (last 30 days) — the current routing per segment
What the chart shows
- C1, C2, C4, C5 — lane rates roughly stable between baseline and recent.
- C3 — SIU/fraud-review rate rises sharply (18% baseline → 71% recent). Standard Adjuster Review rate shrinks. Fast-Track Settle drops toward zero.
- The differential exists only for C3. Other segments are unaffected.
53 percentage-point differential in SIU/fraud-review lane routing. One client segment. Silently. Over 45+ days. Under the four-fifths test used for adverse-impact analysis, a C3 SIU-review rate 5-15x other segments' rates is a clear-cut prima facie showing.
Why aggregate metrics missed it
Because C3 is one segment among five, the aggregate SIU/fraud-review lane count barely moved: the C3 rise rounded to noise across the whole intake volume. That is why the case-management dashboard stayed green. Aggregate metrics are inherently blind to segment-differential patterns, and segment-differential patterns are inherently what adverse-impact analysis is built to surface.
The signal that fired the A/B/C determination
Multi-segment high-severity divergence — 7 high-severity events across C1, C4, and C5 by Day 51, plus C3's own shape shift landing Day 86. Once high-severity count ≥ 3, the Snapshot lands on Category C by rule — no matter what any other metric shows.
See the Category-A/B/C decision logic →
The exhibit-quality read
This chart is what a plaintiff-class expert or defense counsel asks the firm for first in an adverse-impact matter. It is also what the firm's GC should have on file before either lands — because the same chart, in the firm's hand, tells a different story:
Same chart, operator stance: "we identified this pattern through independent verification on Day X, initiated the following remediation on Day X+N, and here is the timeline of that remediation." The chart becomes evidence of good-faith investigation, not evidence of missed monitoring.
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Same lane-shift chart for your firm's actual AI surface + full determination + 3 fix-first items.
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