Deep learning for Insurance and Finance

ldats2310  2024-2025  Louvain-la-Neuve

Deep learning for Insurance and Finance
3.00 credits
15.0 h
Q2
> Schedule
Teacher(s)
Hainaut Donatien;
Language
French
> English-friendly
Prerequisites
Knowledge of basic concepts in statistics and probability calculation as well as programming, at the course level of the FSA1BA, INGE1BA, MATH1BA programs or the access minor in statistics, actuarial sciences and data science.
Main themes
Artificial neural networks, deep learning, auto-encoder, LSTM, convolution networks, pricing and forecasting
Learning outcomes

At the end of this learning unit, the student is able to :

1
  • Program a regression/classification artificial neural network for pricing or forecasting in insurance/finance
  • Choose the calibration algorithm, the optimization criterion, as well as the constraints most appropriate to the data
  • Implement a neural network reducing the size of a dataset
  • Using a network to model time series
 
Content
  • Neural networks (NN), general
  • NN for insurance and credit risk: deviance and personalized loss function, bias regularization.
  • NN and high dimension: ridge and lasso penalization as well as “embedding layers”
  • Bias-variance: bootstrapping, randomization drop-out
  • Interpretation of models: PDP, ICE, feature importance, LIME, SHAP
  • Neural autoencoders and variational autoencoders
  • Time series forecasting with recurrent networks and LSTM
  • Regression with convolution network
Teaching methods
  • Reading with slides
  • Programs in Python (KERAS & TENSORFLOW)
  • Case studies
Evaluation methods
Students will prepare an individual report in which the methods seen during the readings are applied to a real data set. Note that the professor reserves the right to orally question students on the content of their work.
Online resources
Moodle website
Bibliography
Denuit M., Trufin J. , Hainaut D. 2019. Effective statistical learning III : neural networks and extensions. Springer actuarial lectures notes.
Faculty or entity
> LSBA


Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Sigle
Credits
Prerequisites
Learning outcomes
Master [120] in Data Science : Statistic
DATS2M
3
Master [120] in Actuarial Science
ACTU2M
3
Master [120] in Statistics: General
STAT2M
3
Certificat d'université : Statistique et science des données (15/30 crédits)
STAT2FC
3