RC116: Data Analytics and Quantitative Methods in Risk Management UPDATE

Agenda:

• Fundamentals of stochastics and stochastic methods of risk analysis
  
  
• Introduction to data analytics (based on concrete case studies)
  
  
• Data literacy, methodological competence and information literacy
  
  
• Descriptive analytics (measures of central tendency, measures of dispersion, concentration measures, correlation and causality, regression analysis)
  
  
• Diagnostic analytics (cluster analysis, association analysis, linear regression, classification methods, time series analysis)
  
  
• Application example: linear regression in currency risk management
  
  
• Application example: GBM modelling for aluminium prices
  
  
• Predictive analytics (Bayesian networks, machine learning)
  
  
• Evaluation of predictive models
  
  
• Statistical distributions and their parameterisations (univariate and multivariate)
  
  
• Application of proven Value-at-Risk models used in the financial sector
  
  
• Application of stochastic scenario analyses to planned cash flows, budgets and expected profits (Cash Flow at Risk, EBIT at Risk)
  
  
• Analysis of dependencies between risks
  
  
• Risk reporting: readable and meaningful risk metrics
  
  
• Implementation of quantitative risk management in R and Python
  
  
• Development of various in-house tools for practical use in MS Excel and R (R code and Excel examples will be provided)

Objectives:

In the seminar RC116 Data Analytics and Quantitative Methods in Risk Management, you will learn a proven, practice-oriented toolkit of methods for risk quantification in your company. No prior statistical knowledge is required for this seminar.

Target audience:

Beginners and Advanced in the topic "Quantitative Methods", Risk Managers, Treasurers, Controllers, Project Leaders and Project Managers, Risk Owners (decentralized risk responsible persons), Auditors, Quality Managers and Executive Management or Board Members.

Prerequisites:

No statistical prerequisites are required for this seminar.

Description:

The seminar RC116 Data Analytics and Quantitative Methods in Risk Management teaches the systematic evaluation of data as a basis for well-founded decisions in risk management. Instead of considering individual pieces of information in isolation, data are captured, prepared, analysed and consolidated into a meaningful overall picture in a structured manner. Statistical analyses, machine learning, data mining and predictive models are used, among other things, to make patterns, trends and correlations in large data volumes visible.

The focus is on the application of data analytics to reduce uncertainties, decrease the probability of negative events (downside risk) and increase the probability of positive developments (upside risk). Data analytics opens up new perspectives, helps to uncover hidden interrelationships, identify trends at an early stage and make complex developments in a data-driven environment comprehensible.

The seminar RC116 Data Analytics and Quantitative Methods in Risk Management provides a practical, application-oriented introduction to mathematical and statistical methods of risk analysis as well as to the practical implementation of quantitative methods in corporate practice.

All participants receive comprehensive documentation in printed and electronic form (USB stick and download). In addition, all participants receive the book “Data Analytics im Risikomanagement – Descriptive Analytics – Diagnostic Analytics – Predictive Analytics” (Romeike / Wieczorek, Springer Verlag, Wiesbaden 2025) as well as a certificate from the Risk Academy.