Postgraduate Diploma in Telematics-Based Driving Risk Algorithms
Online 800-hour Postgraduate Diploma in Telematics-Based Driving Risk Algorithms. 8 modules covering Regulation (EU) 2024/1689 (Artificial Intelligence Act), Regulation (EU) 2016/679 (GDPR) and Python (PyTorch, scikit-learn). Assigned tutor and open enrolment.
Take the controls.
Run a real engineering trade-off from this programme. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Understand the structure and quality of telematics data.
Design driving behaviour variables.
Apply classical statistical models to driving risk.
Train deep learning models on trajectories.
Detect anomalies and data manipulation.
Integrate driving scores into actuarial pricing.
Deploy algorithms with fairness and transparency criteria.
Who it's for
- Telematics data scientist who wants to specialise in Telematics-Based Driving Risk Algorithms.
- Usage-based pricing actuary who wants to specialise in Telematics-Based Driving Risk Algorithms.
- Machine learning engineer who wants to specialise in Telematics-Based Driving Risk Algorithms.
- Driving risk analyst who wants to specialise in Telematics-Based Driving Risk Algorithms.
- Telematics fraud analyst who wants to specialise in Telematics-Based Driving Risk Algorithms.
- Telematics product manager who wants to specialise in Telematics-Based Driving Risk Algorithms.
- Engineering or science graduates seeking to enter insurance companies.
What you'll work with
- Regulation (EU) 2024/1689 (Artificial Intelligence Act)
- Regulation (EU) 2016/679 (GDPR)
- EDPB Guidelines 01/2020 on connected vehicles
- Directive 2004/113/EC (equal treatment, Test-Achats ruling)
- ISO/IEC 23894 (AI risk management)
- ISO 39001 (road traffic safety management)
- ASOP No. 12 (risk classification)
8 modules · 800 h
1.1Formulating risk as an algorithmic problem1.2Units of analysis: trip, driver and vehicle1.3Exposure and behaviour variables1.4Data structures for trajectories1.5Tabular data versus time series1.6Labelling with claims1.7Temporal granularity1.8Benchmarks and public datasets1.9Computational requirements1.10Case study: specifying a risk algorithm
2.1Kinematic features2.2Acceleration statistics by segment2.3Speed distributions by road type2.4Contextual features2.5Temporal features2.6Histogram representation2.7Route features2.8Variable selection2.9Temporal leakage2.10Practical: feature extraction with pandas and tsfresh
3.1Frequency GLM with offset3.2GAM for non-linear relationships3.3Trees and random forest3.4Gradient boosting3.5Monotonicity and constraints3.6Regularisation3.7Hyperparameter tuning3.8Driver-level validation3.9Comparison with models without telematics3.10Practical: GLM versus gradient boosting
4.1LSTM recurrent networks4.21D convolutional networks4.3Transformers for time series4.4Driver embeddings4.5Autoencoders for driving styles4.6Contrastive learning4.7Semi-supervised learning4.8Computational cost4.9Interpretability4.10Laboratory: LSTM on driving sequences
5.1Trajectory anomalies5.2Isolation Forest5.3Device manipulation5.4Driver impersonation5.5Detection of simulated collisions5.6Atypical driving5.7Hybrid rules and models5.8Evaluation with few labels5.9Managing false positives5.10Practical: device manipulation detector
6.1Bühlmann credibility6.2Combination with traditional factors6.3Experience models6.4Premium calculation6.5Dynamic bonus-malus6.6Actuarial validation6.7Solvency requirements6.8Documentation for the supervisor6.9Portfolio impact6.10Case study: integration into a motor insurance tariff
7.1Batch and streaming architectures7.2Model serving7.3Performance monitoring7.4Data drift7.5Gender and age proxy variables7.6Insurance directive and non-discrimination7.7Explanations to the customer7.8Algorithmic audits7.9Model governance7.10Workshop: fairness audit of a model
8.1Problem formulation8.2Data8.3Features8.4Baseline model8.5Advanced model8.6Validation8.7Actuarial integration8.8Deployment plan8.9Report and presentation8.10Final review with the tutor
How you'll learn
100 % online training on the SEIUM Institute campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetables: students progress at their own pace within the intake, which allows them to combine it with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (PyTorch, scikit-learn), R, Apache Spark, MLflow, QGIS, in free, academic or evaluation versions. The trials are studied using real variable-engineering procedures based on GPS and accelerometer data, sequence models on trajectories, and fairness auditing of a driving score. Assessment is continuous per module plus the final integrating project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Understand the structure and quality of telematics data.
Design driving behaviour variables.
Apply classical statistical models to driving risk.
Train deep learning models on trajectories.
Detect anomalies and data manipulation.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. No university degree is required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient level of Spanish to follow technical regulatory texts.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme comprises 800 hours in total, spread over 6 to 8 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.
The programme uses Python (PyTorch, scikit-learn), R, Apache Spark, MLflow and QGIS. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
A SEIUM Institute diploma on passing the continuous assessment and the integrating project. It is an in-house programme, not an official qualification from the Ministry.
Yes. Modules passed in a course or postgraduate diploma in the same area can be credited towards the advanced programme. Check the credit recognition policy when you enrol.
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