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Deloitte is seeking a Thesis Intern for Financial Risk Management in Banking to contribute to FRM projects in Amsterdam. You will engage with clients, develop quantitative analyses, and translate results for stakeholders while learning from experienced professionals.
The role requires pursuing a master’s degree in econometrics, mathematics, economics or quantitative finance, with availability for at least 3 days per week for 3 months and a strong interest in the financial sector.
Company Deloitte Type Scriptant Location Amsterdam Sector Banking, Consultancy, Financial Risk Management, Other Required language Dutch, English Website https://careersatdeloitte.com/students?utm_source=vesting_studyassociation&utm_medium=paid_companyprofile&utm_campaign=landingpage_consideration&utm_term=full_evp&utm_content=riskadvisory_landingpage
Join the FRM Banking team to deliver extraordinary performance and impress your clients with knowledge and expertise. At Deloitte.
When it comes to the technical aspects of the job, anything can happen. At the same time, you can inspire and motivate the team with new insights to deliver excellent results. You dare to try new things and you keep learning and developing, even if you experience setbacks along the way. We are looking for:
FRM (Financial Risk Management) professionals help banks and other financial institutions manage risks arising from market risk factors such as interest rates and customer behaviour, as well as credit risk, where losses occur due to borrower or counterparty default. We use our extensive knowledge to help clients determine the capital they need to remain solvent in a crisis and to provide insight on how to mitigate risk, for example by adjusting assets and liabilities or using financial derivatives. We have identified the following thesis topic that we would like to explore:
In ALM modelling, such as for Non-Maturing Deposits (NMDs), we observe that a significant amount of expert knowledge is embedded in the models. This is often achieved by using experts to set the level or range for certain parameters in the model, while a frequentist approach is used for the other modelparameters. Can estimating the model with expert-based prior knowledge and using Bayesian statistics improve ALM models and incorporate expert input into model calibration?
We are also open to your own ideas, so if you have a proposal for a thesis, don't forget to include it with your application.