Data
Digitization means that more and more data is available. This data can be used as support for risk management and other purposes. Our team sets their broad knowledge and experience available for specific data analysis.
Our working method guarantees pragmatic, effective and high-quality solutions for several data questions:
- Descriptive statistics
- Clustering and classification
- Regression analysis
- Time series analysis
- Predictions
- Simulation studies
The solutions can be programmed as desired in Excel, Matlab, R or Python. In consultation, other programming languages could also be accomplished.
For an assignment, we put together a team of experts and business analysts so that a high-quality result is achieved, where a fully documents solution will be delivered that focuses on solving the initial question as the final product.
- Research into data quality, which is the starting point of the research.
- Classification of checking and savings accounts for LCR / NSFR purposes
- Clustering and classification of customers for marketing purposes
- Calculation stickiness of checking and savings accounts
- Credit score models
- Models for prepayment of mortgages / loans
- Commercial risk: insight into customer walk away / retention
- Other data related issues
risk
Risk management is a constant point of attention for financial institutions. In addition to a healthy ambition to have risk management up to standard, there are also constant requirements and wishes of the regulators. For example, there may be a need for extra support to meet the deadline or to make the necessary improvements. We can provide support in various areas:
- Liquidity management - ILAAP
- Funds Transfer Pricing
- Interest risk
- Capital management - ICAAP
- Stress testing (capital / liquidity) - scenario analysis
- Credit risk
- Market risk
- Regulations (eg Basel III / Solvency II)
The combination of knowledge and experience and the support of (master) students for the implementation activities ensure a high-quality result within the given timelines.