Boosting insights in insurance tariff plans with tree-based machine learning methods

Pricing actuaries typically operate within the framework of generalized linear models (GLMs). With the upswing of data analytics, our study puts focus on machine learning methods to develop full tariff plans built from both the frequency and severity of claims. We adapt the loss functions used in the algorithms such that the specific characteristics of insurance data are carefully incorporated: highly unbalanced count data with excess zeros and varying exposure on the frequency side combined with scarce, but potentially long-tailed data on the severity side. A key requirement is the need for transparent and interpretable pricing models which are easily explainable to all stakeholders. We therefore focus on machine learning with decision trees: starting from simple regression trees, we work towards more advanced ensembles such as random forests and boosted trees. We show how to choose the optimal tuning parameters for these models in an elaborate cross-validation scheme, we present visualization tools to obtain insights from the resulting models and the economic value of these new modeling approaches is evaluated. Boosted trees outperform the classical GLMs, allowing the insurer to form profitable portfolios and to guard against potential adverse risk selection

The application of Machine Learning methods in Economics and Econometrics

The contribution of the thesis is to compute a survey of literature showing the main Machine Learning algorithm, how they worked and how they can be classified. The main classification describes are Supervised and Unsupervised Machine Learning, parametrized and non-parametrized tests. Then different regularizer depending on the function class. Subsequently, specification of different regression, in particular Lasso Regression, Ridge Regression, Elastic Net, Regression trees and finally Neural Network. The work continues by pointing to the best known techniques for improving the performance of the algorithm, SGD, Boosting, Bootstrap, Bagging, Bumping, Orthogonalization, Cross- validation. Following the table of contents, there are the specification of advantages and disadvantages coming from both Machine Learning and traditional methods, as the OLS method. Lastly, an examination of different real cases in which the algorithms of Machine Learning are applied. The main area that are selected: Poverty, Banking and Finance and Politics and Policy. The thesis provides an overview of Machine Learning and how it can be applied in economics and econometrics, drawing tangible cases

Dating and Forecasting the G7 Business Cycle

openThe two recent global recessions triggered by the Global Financial Crisis in 2007 and the pandemic in 2020 have put business cycle analysis at the forefront of economic research. An important aspect relates to the identification of turning points. Following the methodology proposed by Stock and Watson (2010) to date turning points in the United States, this thesis uses a disaggregated dataset of economic indicators for the G7 to identify turning points in the global business cycle. A machine learning algorithm XGBoost is used to evaluate the new chronology and compares it to OECD reference chronology. Moreover, the algorithm selects the best indicators of global recessions

A Review of Machine Learning Approaches for Real Estate Valuation

Real estate managers must identify the value for properties in their current market. Traditionally, this involved simple data analysis with adjustments made based on manager’s experience. Given the amount of money currently involved in these decisions, and the complexity and speed at which valuation decisions must be made, machine learning technologies provide a newer alternative for property valuation that could improve upon traditional methods. This study utilizes a systematic literature review methodology to identify published studies from the past two decades where specific machine learning technologies have been applied to the property valuation task. We develop a data, reasoning, usefulness (DRU) framework that provides a set of theoretical and practice-based criteria for a multi-faceted performance assessment for each system. This assessment provides the basis for identifying the current state of research in this domain as well as theoretical and practical implications and directions for future research

«They'll just go to Moody's» : Investigating Corporate Credit Rating Updates Using Machine Learning Techniques

Credit Rating Agencies («CRAs») play an important role in the global debt market. They influence the credit spread and thus the borrowing costs for major corporations. An inherent problem is the conflict of interest that arise when the CRAs are paid by issuers. This is not a recent concern, and numerous studies have looked into this and other issues with CRAs. In this master's thesis, we extend this area of research by applying machine learning («ML») models for predicting credit rating updates. For this task, we construct a prediction model using financial ratios, for which we have 20 years of data for two major agencies; Moody's and Fitch. We also include ratings for an investor-paid agency: Egan-Jones. In the model, we change the soft factor in the CRAs' assessment with a new factor that both theoretically and, as will be shown, empirically explain rating updates; trailing stock returns. We apply the XCBoost algorithm to provide more accurate predictions of credit rating updates. Moreover, we analyse SHAP values to interpret different features' contributions to the predictions of rating updates. We evaluate our approach on a dataset of credit ratings in the US and EU and obtain an accuracy of 84.25%. We find that the total return 12 months before the update is the most important when predicting, which suggests stale credit rating updates. Most excitingly, we find that for CRAs with an investor-paid model, the total return three months before the update is the most important when predicting. For the issuer-paid revenue model, twelve months' total stock return turned out to be important: This suggests that investor-paid revenue models are more proactive in updating credit ratings than issuer-paid agencies. The model is applied to the rating downgrade of Wirecard in 2020, which allows for an interesting interpretation of local SHAP values. We also discuss the potential limitations of using ML in credit rating predictions, such as loss of interpretability, unreliable accounting data and the sensitivity of SHAP values.nhhma

A Machine Learning Approach to Credit Allocation

This dissertation seeks to understand the shortcomings of contemporaneous credit allocation, with a specific focus on exploring how an improved statistical technology impacts the credit access of societally important groups. First, this dissertation investigates a variety of limitations of conventional credit scoring models, specifically their tendency to misclassify borrowers by default risk, especially for relatively risky, young, and low income borrowers. Second, this dissertation shows that an improved statistical technology need not to lead to worse outcomes for disadvantaged groups. In fact, the credit access for borrowers belonging to such groups can be improved, while providing more accurate credit risk assessment. Last, this dissertation documents modern-day disparities in debt collection judgments across white and black neighborhoods. Taken together, this dissertation provides valuable insights for the design of policies targeted at reducing consumer default and alleviating its burden on borrowers and lenders and across societally important groups, as well as macroprudential regulation

Financial risk management in shipping investment, a machine learning approach

There has been a plethora of research into company credit risk and financial default prediction from both academics and financial professionals alike. However, only a limited volume of the literature has focused on international shipping company financial distress prediction, with previous research concentrating largely on classic linear based modelling techniques. The gaps, identified in this research, demonstrate the need for increased effort to address the inherent nonlinear nature of shipping operations, as well as the noisy and incomplete composition of shipping company financial statement data. Furthermore, the gaps illustrate the need for a workable definition of financial distress, which to date has too often been classed only by the ultimate state of bankruptcy/insolvency. This definition prohibits the practical application of methodologies which should be aimed at the timely identification of financial distress, thereby allowing for remedial measures to be implemented to avoid ultimate financial collapse. This research contributes to the field by addressing these gaps through i) the creation of a machine learning based financial distress forecasting methodology and ii) utilising this as the foundation for the development of a software toolkit for financial distress prediction. This toolkit enables the practical application of the financial risk principles, embedded within the methodology, to be readily integrated into an enterprise/corporate risk management system. The methodology and software were tested through the application of a bulk shipping company case study utilising 5000 bulk shipping company-year accounting observations for the period 2000-2018, in combination with market and macroeconomic data. The results demonstrate that the methodology improves the capture of distress correlations, that traditional financial distress models have struggled to achieve. The methodology's capacity to adequately treat the problem of missing data in company financial statements was also validated. Finally, the results also highlight the successful application of the software toolkit for the development of a multi-model, real time system which can enhance the financial monitoring of shipping companies by acting as a practical "early warning system" for financial distress.There has been a plethora of research into company credit risk and financial default prediction from both academics and financial professionals alike. However, only a limited volume of the literature has focused on international shipping company financial distress prediction, with previous research concentrating largely on classic linear based modelling techniques. The gaps, identified in this research, demonstrate the need for increased effort to address the inherent nonlinear nature of shipping operations, as well as the noisy and incomplete composition of shipping company financial statement data. Furthermore, the gaps illustrate the need for a workable definition of financial distress, which to date has too often been classed only by the ultimate state of bankruptcy/insolvency. This definition prohibits the practical application of methodologies which should be aimed at the timely identification of financial distress, thereby allowing for remedial measures to be implemented to avoid ultimate financial collapse. This research contributes to the field by addressing these gaps through i) the creation of a machine learning based financial distress forecasting methodology and ii) utilising this as the foundation for the development of a software toolkit for financial distress prediction. This toolkit enables the practical application of the financial risk principles, embedded within the methodology, to be readily integrated into an enterprise/corporate risk management system. The methodology and software were tested through the application of a bulk shipping company case study utilising 5000 bulk shipping company-year accounting observations for the period 2000-2018, in combination with market and macroeconomic data. The results demonstrate that the methodology improves the capture of distress correlations, that traditional financial distress models have struggled to achieve. The methodology's capacity to adequately treat the problem of missing data in company financial statements was also validated. Finally, the results also highlight the successful application of the software toolkit for the development of a multi-model, real time system which can enhance the financial monitoring of shipping companies by acting as a practical "early warning system" for financial distress