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Allianz: Predicting direct debit with machine learning
No full textThe case is suitable for participants who want to enhance their machine learning and data science skills by solving a real-world application in the insurance industry. By working through the case and assignment questions, students will have the opportunity to do the following:
Learn about direct debit and see how it can improve cash flow.
Gain preliminary insights using exploratory data analysis.
Train and interpret supervised learning algorithms in a binary classification problem.
Apply unsupervised techniques such as clustering and gain insights into the data.
Obtain relevant business insights from the data and determine how they can be used to drive intelligent decision-making.In January 2021, the chief data and analytics officer (CDAO) at Allianz Benelux SA (Allianz) spotted a possible opportunity to optimize cash flow with direct debit. Direct debit was a pre-authorized financial transaction between two parties where the amount due was directly and automatically collected from the payer’s bank account. Direct debit would allow Allianz to shorten payment processes, reduce risks by anticipating payments, and improve customer loyalty. Despite the clear advantages of direct debit for both clients and insurers, only a few of Allianz’s clients were currently making use of direct debit. It was not clear what drove Allianz’s customers or brokers to implement direct debit. This was where the CDAO and his data office team came in. The data office possessed a large amount of data on Allianz’s property and casualty insurance contracts and customers. Now the team needed to investigate how this data could be leveraged to determine the value drivers and develop a strategy to convert more clients to direct debit payments
Value in Health
No full textStudies on variability drivers of treatment costs in hospitals can provide the necessary information for policymakers to redesign reimbursement schemes, as well as for healthcare providers seeking to improve the outcomes-over-cost ratio, i.e., value, of their interventions. This systematic literature review aims to provide an overview of studies focusing on variability in treatment cost from the hospital perspective, providing an outline of their study characteristics, summarizing their cost drivers and offering suggestions on methodology for future research
PE buyouts: predatory practice...or a catalyst for growth. The real effects of private equity buyouts
No full textPredatory practice… or catalyst for growth? The topic of leveraged buyouts can be divisive. Do they deserve the sometimes bad press that surrounds them? Or is it time to look at PE buyouts in a completely new light? Up until now, research into the effects of PE buyouts on companies has been patchy – and contradictory. But new research from Vlerick has revealed the true impact of private equity buyouts – and its findings may surprise you
Dual Sourcing and Smoothing Under Nonstationary Demand Time Series: Reshoring with SpeedFactories
No full textWe investigate near-shoring a small part of the global production to local SpeedFactories that serve only the variable demand. The short lead time of the responsive SpeedFactory reduces the risk of making large volumes in advance, yet it does not involve a complete reshoring of demand. Using a break-even analysis, we investigate the lead time, demand, and cost characteristics that make dual sourcing with a SpeedFactory desirable compared with complete off-shoring. Our analysis uses a linear generalization of the celebrated order-up-to inventory policy to settings where capacity costs exist. The policy allows for order smoothing to reduce capacity costs and performs well relative to the (unknown) optimal policy. We highlight the significant impact of auto-correlated and nonstationary demand series, which are prevalent in practice yet challenging to analyze, on the economic benefit of reshoring. Methodologically, we adopt a linear policy and normally distributed demand and use Z–transforms to present exact analyses.
This paper was accepted by Jayashankar Swaminathan, operations management
A reduction tree approach for the Discrete Time/Cost Trade-Off Problem
No full textThe Discrete Time/Cost Trade-Off Problem is a well studied problem in the project scheduling literature. Each activity has multiple execution modes, a solution is obtained by selecting a mode for each activity. In this manuscript we propose an exact algorithm to obtain the complete curve of non-dominated time/cost alternatives for the project. Our algorithm is based on the network reduction approach in which the project is reduced to a singular activity. We develop the reduction tree, a new datastructure that tracks the modular decomposition structure of an instance at each iteration of the reduction sequence. We show how it is related to the complexity graph of the instance. Several exact and heuristic algorithms to construct a good reduction tree are proposed. Our computational experiments show that the use of the reduction tree provides significant speedups when compared to the existing reduction plan approach. Although the new approach does not outperform the best performing branch-and-bound procedure from the literature, the experiments show that incorporating modular decomposition can provide significant performance improvements for solution algorithms, showing potential for developing improved hybridized procedures to solve this challenging problem type.We acknowledge the support provided by the Special Research Fund, Belgium (BOF grant no. DOC014-18 Van Eynde) and the National Bank of Belgium for providing the first author with a pre-doctoral fellowship. The computational resources (Stevin Supercomputer Infrastructure) and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by Ghent University, Belgium, FWO, Belgium and the Flemish Government – department EWI
Essays in financial innovation and sustainability
No full textThe scope of this dissertation is to observe three of the most prominent trends of this era and to present innovations that could inspire both academics and businesses. There is an urgent need to improve the traditional ways in which the markets operate to achieve financial growth while respecting the environment and the society. The third and fourth manuscript tackle directly the topic of sustainable development through the lenses of SMEs. The first manuscript offers evidence related to the development of FinTech entrepreneurship in a market. Through FinTech and sustainability might appear unrelated at first, their simultaneous development could support achievement of a sustainable economy. FinTech could foster the availability of green finance, which is necessary to the capital-intensive sustainability transformation (Vergara and Agudo, 2021). FinTech supports the sustainable development not only through green finance but also by providing financial resources to underrepresented groups, hence promoting financial inclusion (Arnert et al., 2020). Both FinTech and sustainability are relatively recent trends, hence this dissertation, which focuses on financial innovation and sustainability, oughts to provide evidence for both considering their increasing dependency and relevance.The dissertation was funded by the Academic Research Fund of Vlerick Business School and ABN AMRO Belgium - Partner of the Centre for Sustainable Finance of Vlerick Business Schoo
Do courts apply a private company discount or a marketability discount?
No full textThe value of an unlisted entity remains a contentious issue. This is notably because there is no consensus on the nature, size, and drivers of the so-called discount for lack of marketability (“DLOM”). The DLOM is the estimated percentage difference in value between an unlisted and an all-else-equal listed company. The traditional methods for estimating the DLOM are essentially based on financial and transactional data. It is notoriously difficult to obtain information about the internal organization of private companies and this explains why the knowledge of the DLOM determinants remains limited. These limitations have led us to consider an alternative data source. Specifically, we have based our research on a unique dataset of U.S. court decisions that apply a DLOM to a private company and justify the percentage discount by reference to the specifics of the valuation subject. This article shows that courts apply different discount percentages depending on whether they value operating or non operating companies. The difference between the DLOM applied on operating companies and the DLOM applied on non-operating companies is 7 percent. This paper explains the difference by distinguishing between a private company discount and a marketability discount
How Private Equity-Backed Buyout Contracts Shape Corporate Governance
No full textThis paper explores how contracts in private equity-backed buyouts shape corporate governance in portfolio companies. Drawing upon agency theory and incomplete contracting theory, 34 actual contracts are analysed in detail. Contracts focus on reducing information asymmetries, mainly during the due diligence process, and aligning the goals of managers and PE investors during the investment period and at exit. Residual powers and contingencies are mainly used to deal with incomplete contract designs due to uncertainties. While some contractual mechanisms are comparable to those used in VC contracts, others are idiosyncratic to PE
