Page 86 - Procurement guidelines for smart sustainable cities - A U4SSC deliverable

The clustering model grouped MOT-authorized garages based on the behaviour they show when
conducting MOTs such as the test duration, time of test and result of inspection (compared to
the expected result). The DVSA created a risk (of testing incorrectly) score for each garage, which
allowed them to rank garages and their testers and helped it to identify regional trends.

The model was validated against those who had been identified as doing things incorrectly,
ensuring that the model could learn what behaviours are good indicators of underperformance
or potential MOT fraud.

An important consideration was the ability to explain the outcome of the risk rating without losing
the integrity of the test. Having a human in the loop who interrogates and decides to take action
on the risk score was crucial to making the use of AI successful. All the data used for the AI system
were data that had already been collected by the DVSA and it did not include a lot of sensitive data.

The results

Clustering techniques offered new insights that help the DVSA make predictions, and now support
a more targeted approach to inspections at garages and testers with the highest risk scores. By
identifying areas of concern in advance, examiners’ preparation time for enforcement visits has
fallen by 50 per cent.

72 Procurement guidelines for smart sustainable cities | May 2023