DECISION SUPPORT IN CAR LEASING: A FORECASTING MODEL FOR RESIDUAL VALUE ESTIMATION

The paper proposes a methodology to support pricing decisions in the car leasing industry. In particular, the price is given by the monthly fee to be paid by the lessee as compensation for using a car over some contract horizon. After contract expiration, lessors are obliged to take back the vehicle, which will then be sold in the used car market. Therefore, lessors require an accurate estimate of cars’ residual values to manage the risk inherent to their business and determine profitable prices. We explore the organizational and technical requirements associated with this forecasting task and develop a prediction model that complies with identified application constraints. The model is rigorously tested within an empirical study and compared to established benchmarks. The results obtained in several experiments provide strong evidence for the proposed model being effective in generating accurate predictions of cars’ residual values and efficient in requiring little user intervention

Leveraging Image-based Generative Adversarial Networks for Time Series Generation

Generative models for images have gained significant attention in computer vision and natural language processing due to their ability to generate realistic samples from complex data distributions. To leverage the advances of image-based generative models for the time series domain, we propose a two-dimensional image representation for time series, the Extended Intertemporal Return Plot (XIRP). Our approach captures the intertemporal time series dynamics in a scale-invariant and invertible way, reducing training time and improving sample quality. We benchmark synthetic XIRPs obtained by an off-the-shelf Wasserstein GAN with gradient penalty (WGAN-GP) to other image representations and models regarding similarity and predictive ability metrics. Our novel, validated image representation for time series consistently and significantly outperforms a state-of-the-art RNN-based generative model regarding predictive ability. Further, we introduce an improved stochastic inversion to substantially improve simulation quality regardless of the representation and provide the prospect of transfer potentials in other domains

SUPPORT OF MANAGERIAL DECISION MAKING BY TRANSDUCTIVE LEARNING

Transductive inference has been introduced as a novelparadigm towards building predictive classi¯cation modelsfrom empirical data. Such models are routinely employedto support decision making in, e.g., marketing, risk manage-ment and manufacturing. To that end, the characteristics ofthe new philosophy are reviewed and their implications fortypical decision problems are examined. The paper\u27s objec-tive is to explore the potential of transductive learning forcorporate planning. The analysis reveals two main factorsthat govern the applicability of transduction in business set-tings, decision scope and urgency. In a similar fashion, twomajor drivers for its e®ectiveness are identi¯ed and empir-ical experiments are undertaken to con¯rm their in°uence.The results evidence that transductive classi¯ers are wellsuperior to their inductive counterparts if their speci¯c ap-plication requirements are ful¯lled

Targeting Customers under Response-Dependent Costs

This study provides a formal analysis of the customer targeting decision problem in settings where the cost for marketing action is stochastic and proposes a framework to efficiently estimate the decision variables for campaign profit optimization. Targeting a customer is profitable if the positive impact of the marketing treatment on the customer and the associated profit to the company is higher than the cost of the treatment. While there is a growing literature on developing causal or uplift models to identify the customers who are impacted most strongly by the marketing action, no research has investigated optimal targeting when the costs of the action are uncertain at the time of the targeting decision. Because marketing incentives are routinely conditioned on a positive response by the customer, e.g. a purchase or contract renewal, stochastic costs are ubiquitous in direct marketing and customer retention campaigns. This study makes two contributions to the literature, which are evaluated on a coupon targeting campaign in an e-commerce setting. First, the authors formally analyze the targeting decision problem under response-dependent costs. Profit-optimal targeting requires an estimate of the treatment effect on the customer and an estimate of the customer response probability under treatment. The empirical results demonstrate that the consideration of treatment cost substantially increases campaign profit when used for customer targeting in combination with the estimation of the average or customer-level treatment effect. Second, the authors propose a framework to jointly estimate the treatment effect and the response probability combining methods for causal inference with a hurdle mixture model. The proposed causal hurdle model achieves competitive campaign profit while streamlining model building. The code for the empirical analysis is available on Github.Comment: 20 pages, 2 figure

A CASE STUDY OF RANDOM FOREST IN PREDICTIVE DATA MINING

The paper examines the potential of a novel data mining method, the random forest classifier, to support managerial decision making in complex forecasting applications. A modelling paradigm is proposed that embraces a learning curve analysis and grid-search to analyse the model’s sensitivity towards the number of training examples and parameter settings, respectively, and, eventually, produce a final classifier with high predictive accuracy. The effectiveness of the approach is evidenced by experimental evaluation using the data of the 2008 data mining cup competition

Multimodal Document Analytics for Banking Process Automation

In response to growing FinTech competition and the need for improved operational efficiency, this research focuses on understanding the potential of advanced document analytics, particularly using multimodal models, in banking processes. We perform a comprehensive analysis of the diverse banking document landscape, highlighting the opportunities for efficiency gains through automation and advanced analytics techniques in the customer business. Building on the rapidly evolving field of natural language processing (NLP), we illustrate the potential of models such as LayoutXLM, a cross-lingual, multimodal, pre-trained model, for analyzing diverse documents in the banking sector. This model performs a text token classification on German company register extracts with an overall F1 score performance of around 80\%. Our empirical evidence confirms the critical role of layout information in improving model performance and further underscores the benefits of integrating image information. Interestingly, our study shows that over 75% F1 score can be achieved with only 30% of the training data, demonstrating the efficiency of LayoutXLM. Through addressing state-of-the-art document analysis frameworks, our study aims to enhance process efficiency and demonstrate the real-world applicability and benefits of multimodal models within banking.Comment: A Preprin