Financial Risk Graduate

Internships at Deloitte Financial Risk

Are you in the final phase of your master and interested in the problems financial organisations face to become both financially stable and liquid in this fast-changing global environment? Then a Graduate Internship at Deloitte Financial Risk perfectly suits you!

As a Graduate Intern at Deloitte Financial Risk you will get intensive support from your internship supervisor, who will be there for advice. While you work on your thesis, you are part of the Financial Risk talent pool. This means that besides writing your thesis you will also have the opportunity to work on projects for our clients or help with research assignments. Moreover, an internship at Deloitte gives you access to our extracurricular activities, such as

  • Training sessions
  • Drinks
  • Teambuilding activities

At Financial Risk, a service line within Deloitte Risk Advisory, our goal is to become the undisputed advisor within the financial services industry. Different teams focus on

  • Market, Credit & Actuarial Risk;
  • Capital Management, Liquidity & Treasury Risk;
  • Accounting & Financial Reporting Risk. 

If you combine writing your thesis with an internship at Deloitte, you will receive a compensation that is competitive in the market. The total amount of compensation you get depends on the number of days you will be with us. In addition to the internship compensation, we offer travel allowance in case you do not have a student transport card. You will also be provided with a laptop during your internship period. Read more about our employment benefits.

Topic: Forecasting mortality rates using deep learning models
Area of expertise: Insurance
Abstract: The Royal Dutch Actuarial Association (“AG”) publishes a projection table of mortality rates every two years. The table shows the general probability of a person to pass away before their next birthday, based on their current age. Mortality rates are an important input for insurers to calculate premiums and (regulatory) capital requirements. Currently, the AG applies a Li-Lee model to forecast mortality rates and to generate a projection table. Although, the Li-Lee model gives a good prediction, machine learning techniques have become more popular in the industry. As part of this thesis research you will investigate whether it is possible to apply machine learning techniques to forecast mortality rates and outperform traditional models. Can these techniques be used to determine mortality rates in stress scenarios? Also, can additional (third party) data be added to improve forecasts?

Topic: Modernizing Best Estimate Reserves modelling
Area of expertise: Actuarial Risk
Abstract: Claims and Premium reserves are the expected future value of the claims that the insurer will have to pay to its policyholders. These provisions aim to match the true future value as precisely as possible, the best estimate. In addition, insurers determine the required capital to pay out all the reported claims with a predefined certainty. The techniques that are used to create the best estimate for non-life insurers are generally a few decades old and do not make use of the new amounts of data or computational power that have become available in recent years. The application of new statistical techniques, such as Machine Learning algorithms, is a logical next step in the evolution of the insurer. To truly bring the reserving and capital modeling process of an insurer to the next level the combination of the Claims (i.e. backward looking) and Premium reserve (i.e. forward looking) into one model will be necessary. This next step is in reach when individual reserving, instead of the traditional aggregated approach, is implemented. The research into individual reserving should answer the question if the new techniques are successful in outperforming the old techniques in terms of both computational performance and accuracy in terms of estimating the best estimate. Additionally, an analysis of the transfer of risk between the Claims and the Premium reserves should provide additional insights into the dynamic nature of the risks that these reserves are meant to capture.

Topic: Impact of climate change on non-life insurance
Area of Expertise: Non-life insurance, catastrophe modeling
Abstract: Insurance provides protection against risks, for example damage caused by windstorm or hail. Insurers will want to estimate the expected value and variability around the estimate. Your research can focus on developing a model to simulate the effects of windstorms on the insurer’s portfolio, especially considering the effects of climate change on storms. The output of these models can be used in reserving, internal models and pricing.

Topic: Variability of risk premiums in non-life insurance
Area of Expertise: Non-life insurance
Abstract: Dynamic underwriting aims at finding an optimal balance between the customers’ and insurer’s risk profiles by balancing price, product and acceptance criteria. Expected customer lifetime value can be maximised to find the optimal premium. The estimated risk profile typically is a point estimate of the expected future claims. By investigating the variability of the policyholder’s risk profile, a distribution of the customer lifetime value can be estimated. This helps the non-life insurer in policy offering, balancing competitiveness and risk caused by variability in claims.

Topic: Stochastic correlations models for calculating capital requirements
Area of Expertise: Actuarial Risk
Abstract: Insurance companies are required to retain a part of their own funds as capital to withstand losses in (severe) stress scenarios. The amount of capital insurance companies need to hold reflects a loss event that could occur, on a statistical basis, once every 200 years. This is according to the Solvency II capital directive, which applies to all European (re)insurance companies. To calculate the required capital, also known as the Solvency Capital Requirement (SCR), insurance companies need to take their complete risk profile into account. E.g. insurance companies are exposed to market risk, life underwriting risk, etc. Insurance companies deal with many risk factors which have to be taken into account in their models. These risk factors may be correlated, which means that the risk factors may move in the same direction or in an opposite direction. Nowadays insurance companies who apply an internal model to calculate their SCR tend to use stochastic simulation models where they use static (deterministic) correlations between risk factors. The number of risk factors they take into account may be larger than 100. It depends mostly on the level of granularity on which the risk factors are taken into account. In the current situation insurance companies calculate the correlations between the different risk factors deterministically and keep them constant in their models for an entire year. The aim of this thesis is to determine if there is added value in making the correlations between risk factors dependent on time, i.e. stochastic.

Topic: BGM Libor Market Model in Risk Neutral and Real World Measure
Area of expertise: Market Risk
Abstract: Pricing interest rate derivatives is performed in the real-world, but what happens if we need “real-world” risk metrics for the risk departments? The solution is a combination of real-world and risk-neutral simulations. Possible directions for research are 'Implement of change of measure in the BGM model', 'Calibration of the utility function that links the risk neutral and the real world measures' and 'Application of Longstaff-Schwartz to avoid nested simulations and to increase the computational performance of the model'.

Topic: Bayesian variable selection in linear regression
Area of expertise: Credit Risk
Abstract: Financial institutions are performing regressions for many different models. Selecting the best set of regression parameters is often performed manually by the developer of the model. Model developers sometimes say; “It’s more of an art than a science”. Bayesian variable selection allows for the selection of a parameter set that is in fact mathematically optimal. Which, at the very least, is a great way to challenge the “art” of parameter selection. Credit risk is a possible direction for research. Low default portfolios (LDP) account for a large share of total bank lending. Due to the scarcity of default observations and subsequent need for numerous assumptions in order to model probability of default (PD) for LDP’s, model calibrations are needed, which on their turn, introduce significant model risk. This is usually absorbed by applying high level conservatism. Bayesian variable selection could offer a reduction of model risk.

Topic: Risk mitigating techniques under new accounting standards for insurance contracts
Area of expertise: Actuarial Risk
Abstract: On January 1th, 2021 a new accounting standard (IFRS17) will be introduced which will change the way insurers show their financial results to the world. Both profits as well as losses need to be reflected in a more transparent manner including the movement over time. Investors and analysts will, for the first time ever, be able to compare the dividend generating capacities of insurers within the market. However, these new accounting standards set the principles on which the financial reporting must be based. And principles give room for interpretation. Based on Deloitte’s current understanding you will build further on the development of risk mitigating techniques like reinsurance contracts and derivatives to provide insight on how the SII and IFRS17 are complementary or contradictory. 

Topic: Application of machine learning to reduce financial reporting risk
Area of expertise: Actuarial Risk
Abstract: On January 1th, 2021 a new accounting standard (IFRS17) will be introduced which will change the way insurers show their financial results to the world. Both profits as well as losses need to be reflected a more transparent manner including the movement over time. Investors and analysts will, for the first time ever, be able to compare the dividend generating capacities of insurers within the market. However, these new accounting standards set the principles on which the financial reporting must be based. And principles give room for interpretation. This thesis topic looks deeper into different options for the moment of transition, insurance policies must be reflected as if they were under IFRS17 from the start. However, there are many different option to determine the starting value (fully retrospective, modified retrospective and fair value). The choices made at the transition date determine the business strategy going forward. As a thesis student you will apply the machine learning techniques to find the optimal balance between the value at the moment of transition (Jan 1th 2021) and all possible scenario’s possible thereafter. Application of machine learning techniques are especially interesting for students with a quantitative back ground while the application towards insurance provides a clear link with the actuarial risk advisory practice of Deloitte.

Topic: Risk adjustment determination under IFRS 17
Area of expertise: Actuarial Risk
Abstract: The insurance industry has been subject to many regulatory developments in recent years and these developments are still ongoing. The traditional framework of actuarial calculations has been further expanded. The introduction of concepts as market value and market consistency ultimately led (as mutual agreements between insurers) to the Market Consistent Embedded Value (MCEV) approach to determine the economic value of insurance obligations. One of the components required to determine market value under IFRS 17 is the so-called 'risk adjustment', this component is comparable to the 'risk margin' under Solvency II but not exactly the same. The choice for the methodology to determine this risk adjustment is free, options include the Cost-of-Capital approach or a method based on conditional tail expectation. In addition to the own chosen method, each insurer must also report a risk adjustment based on a Confidence Level methodology. The graduation program has the following objectives: 1. Examining the possible methods that can be used to determine the risk adjustment under IFRS 4 phase II, and 2. Analyze the differences between the different methods and the consequences of different assumptions on the outcomes.

Topic: AI Insurance management board
Area of expertise: Actuarial Risk
Abstract: Do you believe that you have what it takes make an impact in the board room? Are you ready to challenge the board of an insurance company on their decisions? Are you quantitative but also able to see the bigger picture? In short, do you have what it takes to create a virtual board? The introduction of Solvency II provides more information to the management board. Additional risk and performance metrics have been added, reducing the complexity of running an insurance company to an optimization strategy of the new performance metrics. In this graduation program students take this concept one step further and develop a model that can replace a management board as a whole.  

Topic: Asset performance based liability valuation
Area of expertise: Actuarial Risk
Abstract: This thesis internship is the ideal combination between financial risk management, asset modelling and gaining insurance insights. The Matching Adjustment (MA) under Solvency II provides the possibility for insurers to make a cash flow match between assets and liabilities for a balance sheet approach to valuation. The starting point of this thesis internship will be to explore the characteristics of insurance liability cash flows. For the selection framework of assets the MA guidelines can be leveraged. The core of your thesis will be the asset selection mechanism which will be able to solve for the optimal balance sheet composition under MA and other real world restrictions. The thesis internship concludes with a case study where the outcomes of the thesis are tested as if the insurer would have to report under IFRS17. This topic is especially interesting for students with a financial or actuarial back ground while the application in the case study will make your first mark in the insurance sector.  

Topic: Dynamic pricing in life insurance
Area of expertise: Actuarial Risk
Abstract: Dynamic pricing is becoming more common in the Dutch insurance landscape. Recent developments have been particularly focusing on non-life insurance products. In this thesis subject the goal is to investigate the potential gains for life insurance products when using pricing techniques from the non-life sector. Apply general linearized models or machine learning techniques to predict profitability of Term life or annuity products with predicting variables such as age or smoker status. One of the areas that needs further research is the price elasticity, so what effect will a price increase (or decrease) on a portfolio segment have on the corresponding sales volumes. Build a simplified insurance product to illustrate the effects. 

Topic: Variability of risk premiums in non-life insurance
Area of expertise: Actuarial Risk
Abstract: Dynamic pricing aims at finding an optimal balance between the customers’ risk profile, their price elasticity and the competitors’ position in the market. Based on these models, the expected customer lifetime value can be maximised to find the optimal premium. The estimated risk profile typically is a point estimate of the expected future claims. By investigating the variability of the policyholder’s risk profile, a distribution of the customer lifetime value can be estimated. This helps the non-life insurer in balancing competitiveness and profitability of premiums, further optimising their product offering.

Topic: Analysis of methodology and assumptions prescribed by PRIIPs regulation
Area of expertise: Market Risk
Abstract: As of January 1, 2018 the so-called Packaged Retail and Insurance-based Investment Products (PRIIPs) regulation came in to place, impacting the banking, insurance and asset management industries. The PRIIPs regulation lays down regulatory technical standards with regard to key information documents (KIDs), which aim to improve the retail investor’s understanding of PRIIPs, the risks involved, and the comparability of those products. The legislation prescribes that parties offering PRIIPs should provide KIDs to investors. The KIDs are 3 pages long and contain general considerations, product information, performance indicators and risk indicators. The key aspects of the performance and risk indicators are market risk, credit risk, liquidity risk and costs. The regulation prescribes how to calculate these performance and risk indicators. For example, with regards to market risk a historical simulation based on the (historical) principal components of the interest rate curves is used to derive performance and risk indicators for interest rate derivatives. Deloitte advices its clients on the implementation of the PRIIPs regulation, in particular the calculation of these performance and risk indicators. Deloitte observes that model choices made by the regulator are debatable from a technical perspective and that some parts of the regulation are even left for interpretation. Deloitte would like to investigate the implication of the model choices and points of interpretations from an academic point of view. This could provide banks, insurers and asset managers with more insights in the potential shortcomings of the methodology and assumptions prescribed by the PRIIPs regulation and thus also the information communicated to its investors.

Topic: Interest rate risk modelling
Area of expertise: Market Risk
Abstract: Banks, insurers and asset managers are significantly exposed to interest rate risk. In order to obtain insight in interest rate risk, it is common for these financial institutions to determine the value distribution of assets and liabilities under a set of different interest rate scenarios. Interest rate scenarios are usually determined based on either historical simulation or some parametric assumptions about the distribution of historical interest rate changes. The methods used often suffer from several shortcomings, amongst others:

  • Financial institutions usually have a view and expectation on future interest rate developments. This may differ from what is historically as well as currently observed in the market. These views and expectations are rarely incorporated in the scenario generation.
  • When decomposing interest rate curve changes using principal component analysis, it can be shown that approximately three factors can explain more than 95% of the total variation on curve changes. These factors are the level, slope and curvature shapes. Given a state of the economy or interest rate levels, it could be argued that certain shape changes might be more likely than others.

The research topic will be to investigate the impact of defining (time-varying) priors as input to the interest rate scenario generation methodology in order to improve the performance of interest rate scenario generation. The priors can be determined based on the state of the interest rate curve (historically) as well as on expert views (future). The Normal Inverse Gaussian Distribution can be used as parametric distribution, given that it can account for skewness and as a consequence for asymmetric distributions over time given the priors.

Topic: Rule extraction algorithms
Area of expertise: Credit Risk – Advanced credit scoring
Abstract: Modern machine learning techniques (e.g. random forests, neural networks, …) deliver a great improvement in terms of predictive power in comparison with conventional techniques (e.g. generalized linear models, logistic regression, …) and are increasingly used to replace or enhance risk models. However, their complexity makes these kind of models hard to interpret and validate. Can we use rule extraction algorithms (deducing a set of prediction rules such that these prediction rules have (approximately) the same predictive power as the underlying model, but the rules are easier to interpret) for advanced machine learning models, to make the inner workings/decision making of the algorithm transparent and interpretable.

Topic: Value based accounting for banks under stress
Area of expertise: Capital Management
Abstract: Many banking product are valued on a market value basis. However, this market value isn’t always as stable or as representative for the “real” value of a product. Especially in times of stress, market values tend to be extremely volatile. Bid/Ask spreads tend to widen. Incorporating these new market values in the banking book can lead to large differences in value, even negative, while the real losses often tend to be less severe. How appropriate is this policy? Why should or shouldn’t banks incorporate these market values in their banking book? How realistic are the values?  Is there a reasonable bandwidth for the market value of a product?

Do you have other interesting ideas? Let us know! You are always welcome to present your topic to us.

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