Run rating structures against the same risks, let the lowest eligible price win, and see the premium, loss mix, margin, and adverse selection that follow.
Compare current and proposed rates across the full book before release.
Portfolio metricsProposed vs current
Average£591.26
Current £613.96−3.70%
Total£714.99m
Current £742.44m−3.70%
Loss ratio51.46%
Current 49.55%+1.90 pts
Gini0.2234
Current 0.1901+17.53%
Illustrative portfolio metrics comparing actual, current, and proposed results
Metric
Actual
Current
Proposed
Relative change
Absolute change
Portfolio result
Average
£304.24
£613.96
£591.26
−3.70%
−£22.70
Total
£367,905,192.68
£742,436,896.57
£714,986,582.75
−3.70%
−£27,450,313.82
Loss ratio
—
49.554%
51.456%
+3.84%
+1.90 pts
Gini
0.99449
0.19012
0.22344
+17.53%
+0.03
Competitive auction
Auction average
—
£575.69
£507.63
−11.82%
−£68.07
Auction total assigned
—
£281,398,294.66
£365,627,977.41
+29.93%
+£84,229,682.75
Auction loss ratio
—
64.4%
51.05%
−20.73%
−13.35 pts
Auction profit
—
£100,181,957.32
£178,963,433.55
+78.64%
+£78,781,476.24
Auction exposure share
—
40.42%
59.56%
+47.35%
+19.14 pts
Illustrative monetary values are shown to two decimal places. Directional colour reflects the stated portfolio objective, not whether a number is simply higher or lower.
Portfolio explorer
Explore how a pricing change moves through the portfolio.
Start with dislocation analysis to see the size and spread of price changes. Then use double lift to compare structures against observed outcomes, one way and two way views to inspect patterns, and geography to see where changes fall. Filter by segment, peril, model or rating structure.
Compare observed and predicted claim frequency with the fitted coefficient. Exposure is reflected directly beneath the curve.
VariableDriver ageMeasureClaim frequency
Actual, predicted, and fitted driver-age coefficients with exposure.
Illustrative driver-age one-way values
Driver age
Actual
Predicted
Coefficient
Exposure share
17
0.306
0.300
0.52
0.717%
25
0.261
0.256
0.30
1.376%
35
0.217
0.227
0.31
2.117%
45
0.258
0.261
0.48
1.908%
55
0.233
0.220
0.17
1.257%
65
0.144
0.145
−0.24
1.323%
75
0.055
0.069
−0.72
0.907%
85
0.120
0.115
−0.12
0.580%
95
0.345
0.278
0.94
0.298%
99
0.475
0.390
1.62
0.232%
Where do the current and proposed structures disagree?
Order risks by the proposed-to-current prediction ratio, then compare both estimates with the actual outcome in each equal-exposure bin. The same view can compare candidate models or complete rating structures.
Bins10 equal exposureOrderProposed ÷ current
Avg ActualAvg ProposedAvg Current
Illustrative double lift values, ordered by the proposed-to-current prediction ratio
Portfolio bin
Average actual
Average proposed
Average current
1
394
366
612
2
380
328
470
3
316
302
410
4
271
293
378
5
297
294
366
6
288
296
357
7
346
301
349
8
318
308
340
9
326
310
321
10
379
323
294
Proposed is closer to actual in 8 of 10 bins. The separation is strongest among the risks where the two structures disagree most.
Understand the spread of every price change.
Plot exposure by the proposed-to-current premium ratio to see how much of the book moves, where the tails sit, and which risks need review before release.
MeasureProposed ÷ currentReference1.00 · no change
Illustrative dislocation analysis values
Proposed-to-current premium ratio
Exposure
Approximate price change
(0.58, 0.62]
460
−40%
(0.70, 0.74]
2,144
−28%
(0.82, 0.86]
7,234
−16%
(0.90, 0.94]
10,026
−8%
(0.94, 0.98]
10,065
−4%
(0.98, 1.02]
9,124
No change
(1.10, 1.14]
4,160
+12%
(1.22, 1.26]
1,535
+24%
(1.38, 1.42]
475
+40%
(1.54, 1.58]
105
+56%
Most exposure sits between an 8% reduction and no change. The thinner tails identify customers with the largest decreases or increases for targeted review.
Proposed rate change by age and mileage
Rotate the two-way view to inspect every combination. Bar height shows the exposure supporting each cell; colour shows the proposed rate change.
InteractionDriver age × annual mileageHeightExposureColourProposed rate change
The illustrative view contains twenty combinations of driver-age and annual-mileage bands. Exposure is highest for drivers aged 35 to 49 travelling 5,000 to 10,000 miles. Proposed rate changes range from a 4.8% reduction to an 8.0% increase.
Three-dimensional driver age and annual mileage portfolio view.
Illustrative two-way simulation values
Driver age
Annual mileage
Exposure
Proposed rate change
17–24
0–5k
310
−4.8%
25–34
0–5k
640
−3.2%
35–49
0–5k
780
−0.8%
50–64
0–5k
520
+2.6%
65+
0–5k
260
+5.4%
17–24
5–10k
420
−3.1%
25–34
5–10k
920
−1.4%
35–49
5–10k
1,120
+0.7%
50–64
5–10k
810
+3.8%
65+
5–10k
390
+6.1%
17–24
10–15k
270
−0.9%
25–34
10–15k
710
+0.5%
35–49
10–15k
940
+2.7%
50–64
10–15k
690
+5.2%
65+
10–15k
310
+7.4%
17–24
15k+
140
+1.8%
25–34
15k+
350
+3.1%
35–49
15k+
460
+4.9%
50–64
15k+
320
+6.8%
65+
15k+
170
+8.0%
Drag to rotate · Scroll or pinch to zoom · Hover or tap a bar for values
Proposed rate change by area
Compare the proposed and current structures across the portfolio. Colour shows rate movement and height shows the exposure supporting each area.
ColourProposed ÷ currentHeightExposure
Proposed ÷ currentLoading portfolio geography
LowerHigher
Height = exposure
Loading geographic portfolio data…
What-if analysis
Test what happens when costs change.
Use a custom expression to test a change in cost for a specific group of risks. If electric vehicle repairs become 10% more expensive, apply that assumption only to electric vehicles, then compare expected claims, loss ratio and margin with the base case. Test any proposed rate adjustment against the same portfolio.
Illustrative scenarioEV repairs +10%
Custom expression
IfVehicle type is electric
ThenRepair cost assumption × 1.10
ElseKeep the base assumption
Compare with base case
See the portfolio effect.
Expected claims
Loss ratio
Margin
Cross-package importance
See what drives each rating package.
Rank variables once, then compare their contribution across frequency, severity, peril, and alternative rating packages. Missing values remain explicit, so shared signals and package-specific variables are easy to distinguish.
Illustrative variable importance scores for the motor third party liability frequency and severity rating packages
Rank
Variable
MTPL_Freq
MTPL_Sev
1
bm
0.2118
0.0216
2
coverage
0.0249
0.0705
3
ageph
0.0915
0.0373
4
postcode
—
0.0423
5
fuel
0.0649
—
6
power
0.0637
—
7
agec
0.0413
0.0198
8
fleet
0.0078
—
Rows follow the selected portfolio ranking. Bar lengths are normalised within each package; values show the underlying importance score.
Competitive rate auction
Test the anti-selection risk before release.
Price the same portfolio with two rate sets. For each eligible risk, assign the business to the lower price and carry the selected loss measure into the winning portfolio.
Where are we cheap for the wrong risk?
Risk mixFind segments where a lower price attracts a worse expected loss mix, then adjust the rating structure and run the auction again.
Scope of the testA lowest price auction isolates selection risk. It does not replace a demand model that accounts for customer behaviour, brand, channel, and product differences.
Saved and shareable views
Give reviewers the same versions, filters, and measures.
Share a link to the exact simulation view rather than rebuilding the analysis in slides. Authorised users open the same rating versions, portfolio filters, and selected measures. Access, expiry, and data permissions apply to the shared view.
CompareNew rates, current rates, alternative models, and competitor rates when comparable data is available.
FilterFocus on the segment, peril, geography, or version behind a result.
InspectCheck how a rate change varies across exposure, age, mileage and geography.
Every portfolio decision stays tied to the rates that produced it.
When the selected strategy moves forward, the simulation evidence moves with it.
Next: Optimisation
Turn the portfolio evidence into a pricing decision.