Course in Integrating Telematics Data into Pricing
Online course of 300 h in integrating telematics data into pricing. 6 modules covering Regulation (EU) 2016/679 (GDPR), Directive 2002/58/EC (privacy in electronic communications) and Python (pandas, 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
Transform raw vehicle data into pricing variables.
Build driving indicators such as braking, speed and time of day.
Measure the marginal contribution of telematics variables in premium models.
Design pay-as-you-drive and pay-how-you-drive products.
Industrialise the capture and processing of data.
Apply consent, privacy and non-discrimination requirements.
Present a complete telematics tariff proposal.
Who it's for
- Telematics pricing actuary who wants to specialise in Integrating Telematics Data into Pricing.
- Mobility data scientist who wants to specialise in Integrating Telematics Data into Pricing.
- Pay-as-you-drive product manager who wants to specialise in Integrating Telematics Data into Pricing.
- Telematics data engineer who wants to specialise in Integrating Telematics Data into Pricing.
- Pricing analyst who wants to specialise in Integrating Telematics Data into Pricing.
- Privacy officer who wants to specialise in Integrating Telematics Data into Pricing.
- Engineering or science graduates looking to enter motor insurance.
What you'll work with
- Regulation (EU) 2016/679 (GDPR)
- Directive 2002/58/EC (privacy in electronic communications)
- Regulation (EU) 2023/2854 (Data Act)
- Directive 2004/113/EC (equal treatment in goods and services)
- Regulation (EU) 2015/758 (eCall)
- Delegated Regulation (EU) 2017/2358 (product governance)
- ISO 15638 (telematics for commercial vehicles)
6 modules · 300 h
1.1Architecture of a telematics programme: device, app and manufacturer data1.2What actually arrives: format, frequency and volume1.3Identification of trip, vehicle and driver1.4Ingestion, validation and storage of raw data1.5Signal loss, duplicates and unsynchronised clocks1.6Catalogue of available signals and their reliability1.7Cost of data and sampling decisions1.8Separation between raw data, processed data and final variable1.9Traceability from the variable back to the source record1.10Workshop: inventory and assessment of a supplier's signals
2.1Normalisation by exposure: minutes, kilometres and trips2.2Speed relative to the road limit2.3Accelerations and braking: thresholds and sensitivity2.4Turns, bends and roundabout driving2.5Time band, night driving and weekends2.6Inferred road type and urban or interurban environment2.7Weather at the time of the trip2.8Distraction and phone use: what can and cannot be measured2.9Aggregation at policy and period level2.10Practice: calculating a set of driving variables
3.1Base model with classic variables as a reference3.2Incorporating telematics variables into the GLM3.3Collinearity with traditional variables3.4Measuring the true marginal contribution3.5Machine learning models and their control3.6Driving score: construction and scaling3.7Stability of the score over time3.8Handling policies with little recorded mileage3.9Validation on an independent sample3.10Practice: comparing a model with and without telematics
4.1Pay-per-use, pay-how-you-drive and hybrid models4.2Observation period before adjusting the premium4.3Discounts, surcharges and limits on variation4.4Initial premium and selection risk4.5Communicating the score to the customer4.6Incentives and their real effect on driving4.7Retention and elasticity in telematics portfolios4.8Adverse selection: who accepts and who rejects the device4.9Acquisition cost and programme profitability4.10Practice: designing the price structure of a telematics product
5.1Data pipelines in production and quality control5.2Batch and near-real-time calculation of variables5.3Integration with the pricing engine and the policy5.4Monitoring data coverage by policy5.5Device management: installation, faults and removals5.6Supplier dependency and exit plan5.7Infrastructure costs and optimisation5.8Use of data in claims and assistance5.9Operational indicators for the programme5.10Practice: designing the data pipeline
6.1Legal basis, customer information and data minimisation6.2Separation of purposes: pricing, claims and commercial6.3Fairness and proxy variables for protected attributes6.4Explainability of the score to the customer and the supervisor6.5Model governance and owners of each stage6.6Auditing the score calculation6.7Technical and functional documentation of the product6.8Product evolution and tariff versions6.9Common mistakes when launching a telematics product6.10Final project: a complete telematics product, from data to price
How you'll learn
100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: students progress at their own pace within the intake, which makes it possible to combine the course with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (pandas, scikit-learn), R (GLM), WTW Emblem, Apache Spark, in free, academic or evaluation versions. Case studies are based on real engineering procedures for driving variables, GLM models with telematics variables, and pay-as-you-drive product pilots. Assessment is continuous across each 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.
Transform raw vehicle data into pricing variables.
Build driving indicators such as braking, speed and time of day.
Measure the marginal contribution of telematics variables in premium models.
Design pay-as-you-drive and pay-how-you-drive products.
Industrialise the capture and processing of data.
Where this takes you
Requirements
- No prior qualification is required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient command 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 totals 300 hours, spread over 3 to 5 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.
The course works with Python (pandas, scikit-learn), R (GLM), WTW Emblem and Apache Spark. Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.
A SEIUM diploma upon passing the continuous assessment and the final integrating project. This is in-house training, 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 transfer policy when you enrol.
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