AFTERNOON CASE

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 50 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.

STATISTICS

Sectors: Consulting, Banking, Actuarial Science & Data Science
Location: 1
Male-female ratio: 75-25 %
Number of employees (in Holland): 50
Number of starting positions per year: 10
Number of internship positions per year: 8
Number of working students per year: 8
Average age: 28

CASE

Quantifying credit risk – A bank is a financial institution that provides credit for corporations and private individuals. A common type of loan issued to private individuals is the mortgage. The distinctive feature of a mortgage is the requirement that real estate (a house) is posted as collateral. Dutch banks have large mortgage portfolios on their books. But these are not without financial risks! The main risk is credit risk and is defined as the risk that a client cannot (fully) repay the loan. Regulatory institutions require that banks reserve capital to compensate for this risk. Quantitative models are used to determine how much reserve is needed. During the case you will build a so called “Loss-given-Liquidation” (LGL) model; this model predicts the loss that the bank stands to incur given that the liquidation of collateral is needed to repay the loan. The case will primarily test your modelling skills, but solid programming skills will for sure come in handy. Which functional specification will you use for your model? Which variables do you select and in what form? How to test model performance? You will be glad to hear that a library containing a wide range of useful functions will be available and that experienced consultants will guide you through the process.