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Postgraduate DiplomaInsurance, Risk & Actuarial Data Applications open

Postgraduate Diploma in Residual Value Models for Vehicles

800-hour online Postgraduate Diploma in Residual Value Models for Vehicles. 8 modules covering IFRS 16 (leases), IFRS 9 (financial instruments, impairment) and Python (statsmodels, LightGBM). Personal tutor assigned and enrolment open.

Duration800 h
ModeOnline
AwardSEIUM Institute diploma
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Postgraduate Diploma in Residual Value Models for Vehicles — studying from Singapore

Singapore is a major insurance and reinsurance hub in Asia, regulated by the Monetary Authority of Singapore. Engineering skills support risk modelling, catastrophe analysis, data analytics and actuarial work for insurers and reinsurers. The sector draws on data science and quantitative methods, with demand from insurers, brokers and risk consultancies. The city's financial ecosystem makes it a common base for global risk and actuarial teams.

Fees for Singapore: 1,600 S$ (Singapore dollar), with instalments and employer sponsorship available.

Relevant bodies in Singapore

  • Monetary Authority of Singapore (MAS)
  • Life Insurance Association Singapore
  • General Insurance Association of Singapore
  • Society of Actuaries in Singapore

Typical roles

  • Actuarial Analyst
  • Risk Data Scientist
  • Catastrophe Modelling Analyst
  • Insurance Analytics Consultant

Questions from Singapore

Are SEIUM qualifications official degrees in Singapore?

No. SEIUM qualifications are private qualifications awarded by SEIUM Institute and are not official state degrees. Learners should check with the relevant professional bodies in Singapore about how any qualification is treated for their specific purpose.

What language are the programmes taught in?

All programmes are taught in English.

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Learning outcomes

What you will be able to do

01

Explain the role of residual value in car rental, leasing and financing products.

02

Identify the technical, commercial and regulatory factors that determine a vehicle's depreciation.

03

Build statistical models to forecast residual values by segment.

04

Apply machine learning algorithms to used vehicle transaction data.

05

Assess the particularities of electric vehicles, including battery degradation.

06

Measure and manage the residual risk of a contract portfolio.

07

Integrate residual value forecasts into lease pricing and financial reporting.

Profile

Who it's for

  • Residual value analyst who wants to specialise in Residual Value Models for Vehicles.
  • Asset risk manager in car rental who wants to specialise in Residual Value Models for Vehicles.
  • Automotive leasing pricing manager who wants to specialise in Residual Value Models for Vehicles.
  • Used vehicle remarketing analyst who wants to specialise in Residual Value Models for Vehicles.
  • Mobility data scientist who wants to specialise in Residual Value Models for Vehicles.
  • Automotive finance consultant who wants to specialise in Residual Value Models for Vehicles.
  • Engineering or science graduates looking to enter vehicle rental and leasing.
Standards & tools

What you'll work with

  • IFRS 16 (leases)
  • IFRS 9 (financial instruments, impairment)
  • Regulation (EU) 2019/631 (CO2 emission standards for passenger cars)
  • Regulation (EU) 2023/1542 (batteries and waste batteries)
  • Regulation (EC) No 715/2007 (Euro 5 and Euro 6 type-approval)
  • Regulation (EU) No 575/2013 (CRR) as regards residual risk
  • Royal Decree 265/2021 (end-of-life vehicles)
Syllabus

8 modules · 800 h

  1. 1.1Concept of residual value and depreciation
  2. 1.2Importance in rental, leasing and buy-back
  3. 1.3Residual value risk for the lessor
  4. 1.4Manufacturer buy-back guarantees
  5. 1.5The used car market in Spain
  6. 1.6Price cycles and supply of used vehicles
  7. 1.7Price sources: Ganvam, Eurotax and portals
  8. 1.8Mileage, condition and equipment
  9. 1.9Effect of electrification on residual values
  10. 1.10Case study: depreciation analysis of a model
  1. 2.1Make, model and segment
  2. 2.2Age and mileage
  3. 2.3Engine type and environmental label
  4. 2.4Mechanical and cosmetic condition
  5. 2.5Accident history
  6. 2.6Equipment and options
  7. 2.7Colour and market preferences
  8. 2.8New launches and generation changes
  9. 2.9Manufacturer discount policies
  10. 2.10Practice: quantifying factors using advertisement data
  1. 3.1Exponential depreciation curves
  2. 3.2Hedonic price regression
  3. 3.3Generalised linear models of price
  4. 3.4Cohort panel models
  5. 3.5Quantile regression for risk
  6. 3.6Time series of price indices
  7. 3.7Validation and prediction error
  8. 3.8Seasonal adjustment
  9. 3.9Interpretation of coefficients
  10. 3.10Laboratory: hedonic price model for used vehicles
  1. 4.1Legal web scraping and advertisement data
  2. 4.2Cleaning and normalising model versions
  3. 4.3Gradient boosting for price prediction
  4. 4.4Neural networks and model embeddings
  5. 4.5Auction and remarketing data
  6. 4.6Telematics data on vehicle condition
  7. 4.7Explainability of predictions
  8. 4.8Continuous model updating
  9. 4.9Error monitoring
  10. 4.10Practice: residual value prediction with LightGBM
  1. 5.1Battery state of health (SoH)
  2. 5.2Battery degradation and warranties
  3. 5.3Technological obsolescence and range
  4. 5.4Public incentives and their effect on prices
  5. 5.5Charging infrastructure and demand
  6. 5.6Second life and battery value
  7. 5.7Battery passport under Regulation (EU) 2023/1542
  8. 5.8Residual risk in electrified portfolios
  9. 5.9Regulatory scenarios for combustion engine bans
  10. 5.10Case study: residual value of an electric SUV at 48 months
  1. 6.1Risk measurement and expected loss
  2. 6.2Price stress scenarios
  3. 6.3Accounting provisions for impairment
  4. 6.4Coverage with guarantees and insurance
  5. 6.5Residual value insurance and GAP
  6. 6.6Portfolio diversification
  7. 6.7Remarketing strategies
  8. 6.8Setting rental instalments
  9. 6.9Residual value committees
  10. 6.10Workshop: stress test of a rental portfolio
  1. 7.1Offers with guaranteed future value
  2. 7.2Consumer transparency
  3. 7.3IFRS 16 and lessor accounting
  4. 7.4Supervisory requirements on residual risk
  5. 7.5Governance of valuation models
  6. 7.6Interaction with credit risk
  7. 7.7Forecasting remarketing results
  8. 7.8Communication with dealers
  9. 7.9Performance indicators
  10. 7.10Practice: setting residual values for a commercial campaign
  1. 8.1Defining the vehicle segment
  2. 8.2Collecting market data
  3. 8.3Exploratory analysis
  4. 8.4Building the model
  5. 8.5Validation and backtesting
  6. 8.6Stress scenarios
  7. 8.7Proposal for a residual value table
  8. 8.8Monitoring plan
  9. 8.9Report and presentation
  10. 8.10Final review with the tutor
Methodology, software & labs

How you'll learn

100 % online training on the SEIUM campus, with downloadable material, recorded classes and a assigned technical tutor. No fixed timetables: students progress at their own pace within the intake, which makes it possible to combine study with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (statsmodels, LightGBM), R (survival), JATO Dynamics, SAS Visual Analytics, advanced Excel, in free, academic or evaluation versions. Tests are studied using real procedures for depreciation curves by segment and engine type, backtesting of residual value forecasts, and battery health analysis for used electric vehicles. Assessment is continuous across each module plus the final integrating project.

Accredited laboratories(EN ISO/IEC 17025) with a rotor test rig, EMC/Lightning pre-compliance, HIL/SIL, vibration/acoustics.
Evidence-focused Master's Thesis:safety case, test plan, compliance dossier and operational limits.
Industry mentoring:teaching staff with experience in training, , eVTOL/UAM and flight test.
Flexible format(hybrid/online), international cohorts and support from SEIUM Career Services.
Ethics and safety:a safety-by-design approach, with cyber-OT, DIH and compliance as its pillars.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Defining the vehicle segment

Explain the role of residual value in car rental, leasing and financing products.

Collecting market data

Identify the technical, commercial and regulatory factors that determine a vehicle's depreciation.

Exploratory analysis

Build statistical models to forecast residual values by segment.

Building the model

Apply machine learning algorithms to used vehicle transaction data.

Validation and backtesting

Assess the particularities of electric vehicles, including battery degradation.

Career opportunities

Where this takes you

Residual value analystAsset risk manager in car rentalAutomotive leasing pricing managerUsed vehicle remarketing analystMobility data scientistAutomotive finance consultant
Admissions & scholarships

Requirements

  • Prior technical training or professional experience in the sector. A university degree is not required.
  • Internet connection and a computer capable of running the programme's software.
  • Sufficient Spanish to follow technical regulatory reading.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme consists of 800 hours in total, spread over 6 to 8 months. That is between 10 and 15 hours a week on average, compatible with a full-time job.

You will work with Python (statsmodels, LightGBM), R (survival), JATO Dynamics, SAS Visual Analytics and advanced Excel. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.

An SEIUM own diploma upon passing the continuous assessment and the integrating project. It is in-house training, not an official qualification from the Ministry.

Yes. Modules passed in a course or diploma in the same area are credited towards the advanced programme. Check the credit transfer policy when you enrol.

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