Previous research has indicated that increases in traffic offences are linked to increased crash involvement rates, making reductions in offending an appropriate measure for evaluating road safety interventions in the short-term. However, the extent to which traffic offending predicts fatal and serious injury (FSI) crash involvement risk is not well established, prompting this new Victorian (Australia) study. A preliminary cluster analysis was performed to describe the offence data and assess FSI crash involvement risk for each cluster. While controlling demographic and licensing variables the key traffic offences that predict future FSI crash involvement were identified. The large sample size allowed the use of machine learning methods such as random forests, gradient boosting and Least Absolute Shrinkage and Selection Operator (LASSO) regression. This was done for the all driver sample and five sometimes overlapping groups of drivers; the young, the elderly, and those with a motorcycle licence, a heavy vehicle licence endorsement and,or a history of licence bans. With the exception of the group of drivers who had a history of bans, offence history significantly improved the accuracy of models predicting future FSI crash involvement using demographic and licensing data, suggesting that traffic offences may be an important factor to consider when analysing FSI crash involvement risk and the effects of road safety countermeasures. The models are helpful for identifying driver groups to target with further road safety countermeasures and for showing that machine learning methods have an important role to play in research of this nature. This research indicates with whom road safety interventions should particularly be applied. Changes to driver demerit policies to better target offences related to FSI crash involvement and repeat traffic offenders who are at greater risk of FSI crash involvement are recommended.