A literature review on the application of evolutionary computing to credit scoring

The last years have seen the development of many credit scoring models for assessing the creditworthiness of loan applicants. Traditional credit scoring methodology has involved the use of statistical and mathematical programming techniques such as discriminant analysis, linear and logistic regression, linear and quadratic programming, or decision trees. However, the importance of credit grant decisions for financial institutions has caused growing interest in using a variety of computational intelligence techniques. This paper concentrates on evolutionary computing, which is viewed as one of the most promising paradigms of computational intelligence. Taking into account the synergistic relationship between the communities of Economics and Computer Science, the aim of this paper is to summarize the most recent developments in the application of evolutionary algorithms to credit scoring by means of a thorough review of scientific articles published during the period 2000–2012.This work has partially been supported by the Spanish Ministry of Education and Science under grant TIN2009-14205 and the Generalitat Valenciana under grant PROMETEO/2010/028

Artificial Intelligence and Visual Analytics: A Deep-Learning Approach to Analyze Hotel Reviews & Responses

With a growing number of online reviews, consumers often rely on these reviews to make purchase decisions. However, little is known about managerial responses to online hotel reviews. This paper reports on a framework to integrate visual analytics and machine learning techniques to investigate whether hotel managers respond to positive and negative reviews differently and how to use a deep-learning approach to prioritize responses. In this study, forty 4- and 5-star hotels in London with 91,051 reviews and 70,397 responses were collected and analyzed. Visual analyses and machine learning were conducted. The results indicate most hotels (72.5%) showing no preference to respond to positive and negative reviews. Our proposed deep-learning approach outperformed existing algorithms to prioritize responses

Computational Behavioural Economics

Both behavioral economics and computational intelligence (machine learning) rely on the extensive use of heuristics to address decision-making problems in an ill-defined and ill-structured environment. While the former has a focus on behaviors, and the other has a focus on the algorithms, this distinction is merely superficial. The real connection between the two is that through algorithmic procedure the latter provides the former with the computational underpinnings of the decision-making processes. In this chapter, we review this connection, dubbed computational behavioral economics. To do so, we review a number of frequently-used computational intelligence tools in the realm of computational economics, including K nearest neighbors, K means, self-organizing maps, reinforcement learning, decision trees, evolutionary computation, swarm intelligence, and “random” behavior. This review enables us to see how the heuristics employed in the latter, such as closeness, similarity, smoothness, default, automation, hierarchy, and modularity can lay a computational foundation of the heuristics studied by the former

Credit Risk Scoring: A Stacking Generalization Approach

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and ManagementCredit risk regulation has been receiving tremendous attention, as a result of the effects of the latest global financial crisis. According to the developments made in the Internal Rating Based approach, under the Basel guidelines, banks are allowed to use internal risk measures as key drivers to assess the possibility to grant a loan to an applicant. Credit scoring is a statistical approach used for evaluating potential loan applications in both financial and banking institutions. When applying for a loan, an applicant must fill out an application form detailing its characteristics (e.g., income, marital status, and loan purpose) that will serve as contributions to a credit scoring model which produces a score that is used to determine whether a loan should be granted or not. This enables faster and consistent credit approvals and the reduction of bad debt. Currently, many machine learning and statistical approaches such as logistic regression and tree-based algorithms have been used individually for credit scoring models. Newer contemporary machine learning techniques can outperform classic methods by simply combining models. This dissertation intends to be an empirical study on a publicly available bank loan dataset to study banking loan default, using ensemble-based techniques to increase model robustness and predictive power. The proposed ensemble method is based on stacking generalization an extension of various preceding studies that used different techniques to further enhance the model predictive capabilities. The results show that combining different models provides a great deal of flexibility to credit scoring models

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

What is a Good Pattern of Life Model? Guidance for Simulations

We have been modeling an ever-increasing scale of applications with agents that simulate the pattern of life (PoL) and real-world human behaviors in diverse regions of the world. The goal is to support sociocultural training and analysis. To measure progress, we propose the definition of a measure of goodness for such simulated agents, and review the issues and challenges associated with first-generation (1G) agents. Then we present a second generation (2G) agent hybrid approach that seeks to improve realism in terms of emergent daily activities, social awareness, and micro-decision making in simulations. We offer a PoL case study with a mix of 1G and 2G approaches that was able to replace the pucksters and avatar operators needed in large-scale immersion exercises. We conclude by observing that a 1G PoL simulation might still be best where large-scale, pre-scripted training scenarios will suffice, while the 2G approach will be important for analysis or if it is vital to learn about adaptive opponents or unexpected or emergent effects of actions. Lessons are shared about ways to blend 1G and 2G approaches to get the best of each

Design and Implementation of a Customer Personalised Recomender System

[ANGLÈS] Market basket analysis is examined through the application of probabilistic topic models and case-based reasoning in order to provide more insight into customer buying habits and generate meaningful recommendations