AFTERNOON CASE & RECRUITMENT DINNER
RiskQuest is a consultancy specialized in (risk) models for the financial sector. In an ever more complex world there is also an increasing need for risk management. Models are an important tool here. They help us understand underlying risk factors and relationships better. Models thus contribute to transparency, cost-effectiveness and eventually in making the right decisions. While models form the core of our service, we consider them as a means, no purpose. Models cannot replace people.
Our services focus on all aspects of the use of models: data, model development, model validation, policy and strategy. In recent years we have been able to build an impressive customer base. Apart from working for major Dutch financial institutions, we also build our own “in-house” models to service our clients. The type of model used depends on the assignment. For anti-money laundering we build state-of-the-art machine learning models, whilst for credit risk more traditional statistical models are employed.
RiskQuest consists of a team of 40 ambitious individuals with almost all quantitative backgrounds in econometrics, mathematics and physics. RiskQuest has been growing steadily over the past years and is always looking for more talent. We value social contacts between each other and an informal work environment highly, and combine this with a lot of individual freedom. This will make you feel at home quickly.
Sectors: Consulting, Banking & Data Science
Male-female ratio: 85-15 %
Number of employees (in Holland): 40
Number of starting positions per year: Always open
Number of internship positions per year: Always open
Number of working students per year: 5
Average age: 33
You will be given the chance to show your econometric skills by demonstrating how well you can predict future credit losses. More specifically, the case will revolve around estimation of Loss Given Liquidation (LGL). LGL refers to loss incurred by the lender in the situation where the borrower is unable to repay its outstanding debt and pledged collateral is sold to cover (part of) the debt. You are given a portfolio of mortgages that are about to be liquidated and you have to estimate the LGL percentage for clients. You are free to estimate the LGL percentage with a model that you think is suitable. In addition, we provide predefined functions for models (Bucketing, OLS, Tobit and AQR). Coding is done in R.