Credit scoring data for information asset analysis

Risk assessment is an important topic for financial institution nowadays, especially in the context of loan applications. Some of these institutions have already implemented their own credit scoring mechanisms to evaluate their clients’ risk and decide based in this indicator. In fact, the information gathered by financial institutions constitutes a valuable source of data for the creation of information assets from which credit scoring mechanisms can be developed. The purpose of this paper is to, from information assets, create a decision mechanism that is able to evaluate a client’s risk. Furthermore, upon this decision mechanism, a suggestive algorithm is presented to better explain and give insights on how the decision mechanism values attributes.The work described in this paper is part of TIARAC -Telematics and Artificial Intelligence in Alternative Conflict Resolution Project (PTDC/JUR/71354/2006), research project supported by FCT (Science & Technology Foundation), Portuga

Multi-agent system for credit scoring

The use of multi-agent systems to solve complex problems in today’s world is not a new approach. Nevertheless, there has been a growing interest in using its properties in conjunction with machine learning and data mining techniques in order to build smarter systems. A multi-agent system able to classify and recommend attribute values in an instance of a dataset is presented and intended to provide to the end user a better understanding of both the classification in the dataset and client possibilities to obtain a good classification. The multi-agent system presented will have the ability to classify a user credit application and suggest different values for its attributes under assessment

Credit scoring as an asset for decision making in intelligent decision support systems

Dissertação de mestrado em Engenharia de InformáticaRisk assessment is an important topic for financial institution nowadays, especially in the context of loan applications or loan requests and credit scoring. Some of these institutions have already implemented their own custom credit scoring systems to evaluate their clients’ risk supporting the loan application decision with this indicator. In fact, the information gathered by financial institutions constitutes a valuable source of data for the creation of information assets from which credit scoring mechanisms may be developed. Historically, most financial institutions support their decision mechanisms on regression algorithms, however, these algorithms are no longer considered the state of the art on decision algorithms. This fact has led to the interest on the research of new types of learning algorithms from machine learning able to deal with the credit scoring problem. The work presented in this dissertation has as an objective the evaluation of state of the art algorithms for credit decision proposing new optimization to improve their performance. In parallel, a suggestion system on credit scoring is also proposed in order to allow the perception of how algorithm produce decisions on clients’ loan applications, provide clients with a source of research on how to improve their chances of being granted with a loan and also develop client profiles that suit specific credit conditions and credit purposes. At last, all the components studied and developed are combined on a platform able to deal with the problem of credit scoring through an experts system implemented upon a multi-agent system. The use of multi-agent systems to solve complex problems in today’s world is not a new approach. Nevertheless, there has been a growing interest in using its properties in conjunction with machine learning and data mining techniques in order to build efficient systems. The work presented aims to demonstrate the viability and utility of this type of systems for the credit scoring problem.Hoje em dia, a análise de risco é um tópico importante para as instituições financeiras, especialmente no contexto de pedidos de empréstimo e de classificação de crédito. Algumas destas instituições têm já implementados sistemas de classificação de crédito personalizados para avaliar o risco dos seus clientes baseando a decisão do pedido de empréstimo neste indicador. De facto, a informação recolhida pelas instituições financeiras constitui uma valiosa fonte de dados para a criação de ativos de informação, de onde mecanismos de classificação de crédito podem ser desenvolvidos. Historicamente, a maioria das instituições financeiras baseia os seus mecanismos de decisão sobre algoritmos de regressão. No entanto, estes algoritmos já não são considerados o estado da arte em algoritmos de decisão. Este facto levou ao interesse na pesquisa de diferentes algoritmos de aprendizagem baseados em algoritmos de aprendizagem máquina, capaz de lidar com o problema de classificação de crédito. O trabalho apresentado nesta dissertação tem como objetivo avaliar o estado da arte em algoritmos de decisão de crédito, propondo novos conceitos de optimização que melhorem o seu desempenho. Paralelamente, um sistema de sugestão é proposto no âmbito do tema de decisão de crédito, de forma a possibilitar a perceção de como os algoritmos tomam decisões relativas a pedidos de crédito por parte de clientes, dotando-os de uma fonte de pesquisa sobre como melhorar as possibilidades de concessão de crédito e, ainda, elaborar perfis de clientes que se adequam a determinadas condições e propósitos de crédito. Por último, todos os componentes estudados e desenvolvidos são combinados numa plataforma capaz de lidar com o problema da classificação de crédito através de um sistema de especialistas, implementado como um sistema multi-agente. O uso de sistemas multi-agente para resolver problemas complexos no mundo de hoje não é uma nova abordagem. No entanto, tem havido um interesse crescente no uso das suas propriedades, em conjunto com técnicas de aprendizagem máquina e data mining para construir sistemas mais eficazes. O trabalho desenvolvido e aqui apresentado pretende demonstrar a viabilidade e utilidade do uso deste tipo de sistemas no problema de decisão de crédito