A Review of Algorithms for Credit Risk Analysis

The interest collected by the main borrowers is collected to pay back the principal borrowed from the depositary bank. In financial risk management, credit risk assessment is becoming a significant sector. For the credit risk assessment of client data sets, many credit risk analysis methods are used. The assessment of the credit risk datasets leads to the choice to cancel the customer\u27s loan or to dismiss the customer\u27s request is a challenging task involving a profound assessment of the information set or client information. In this paper, we survey diverse automatic credit risk analysis methods used for credit risk assessment. Data mining approach, as the most often used approach for credit risk analysis was described with the focus to various algorithms, such as neural networks. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.</p

An academic review: applications of data mining techniques in finance industry

With the development of Internet techniques, data volumes are doubling every two years, faster than predicted by Moore’s Law. Big Data Analytics becomes particularly important for enterprise business. Modern computational technologies will provide effective tools to help understand hugely accumulated data and leverage this information to get insights into the finance industry. In order to get actionable insights into the business, data has become most valuable asset of financial organisations, as there are no physical products in finance industry to manufacture. This is where data mining techniques come to their rescue by allowing access to the right information at the right time. These techniques are used by the finance industry in various areas such as fraud detection, intelligent forecasting, credit rating, loan management, customer profiling, money laundering, marketing and prediction of price movements to name a few. This work aims to survey the research on data mining techniques applied to the finance industry from 2010 to 2015.The review finds that Stock prediction and Credit rating have received most attention of researchers, compared to Loan prediction, Money Laundering and Time Series prediction. Due to the dynamics, uncertainty and variety of data, nonlinear mapping techniques have been deeply studied than linear techniques. Also it has been proved that hybrid methods are more accurate in prediction, closely followed by Neural Network technique. This survey could provide a clue of applications of data mining techniques for finance industry, and a summary of methodologies for researchers in this area. Especially, it could provide a good vision of Data Mining Techniques in computational finance for beginners who want to work in the field of computational finance

Decision Support Systems for Risk Assessment in Credit Operations Against Collateral

With the global economic crisis, which reached its peak in the second half of 2008, and before a market shaken by economic instability, financial institutions have taken steps to protect the banks’ default risks, which had an impact directly in the form of analysis in credit institutions to individuals and to corporate entities. To mitigate the risk of banks in credit operations, most banks use a graded scale of customer risk, which determines the provision that banks must do according to the default risk levels in each credit transaction. The credit analysis involves the ability to make a credit decision inside a scenario of uncertainty and constant changes and incomplete transformations. This ability depends on the capacity to logically analyze situations, often complex and reach a clear conclusion, practical and practicable to implement. Credit Scoring models are used to predict the probability of a customer proposing to credit to become in default at any given time, based on his personal and financial information that may influence the ability of the client to pay the debt. This estimated probability, called the score, is an estimate of the risk of default of a customer in a given period. This increased concern has been in no small part caused by the weaknesses of existing risk management techniques that have been revealed by the recent financial crisis and the growing demand for consumer credit.The constant change affects several banking sections because it prevents the ability to investigate the data that is produced and stored in computers that are too often dependent on manual techniques. Among the many alternatives used in the world to balance this risk, the provision of guarantees stands out of guarantees in the formalization of credit agreements. In theory, the collateral does not ensure the credit return, as it is not computed as payment of the obligation within the project. There is also the fact that it will only be successful if triggered, which involves the legal area of the banking institution. The truth is, collateral is a mitigating element of credit risk. Collaterals are divided into two types, an individual guarantee (sponsor) and the asset guarantee (fiduciary). Both aim to increase security in credit operations, as an payment alternative to the holder of credit provided to the lender, if possible, unable to meet its obligations on time. For the creditor, it generates liquidity security from the receiving operation. The measurement of credit recoverability is a system that evaluates the efficiency of the collateral invested return mechanism. In an attempt to identify the sufficiency of collateral in credit operations, this thesis presents an assessment of smart classifiers that uses contextual information to assess whether collaterals provide for the recovery of credit granted in the decision-making process before the credit transaction become insolvent. The results observed when compared with other approaches in the literature and the comparative analysis of the most relevant artificial intelligence solutions, considering the classifiers that use guarantees as a parameter to calculate the risk contribute to the advance of the state of the art advance, increasing the commitment to the financial institutions.Com a crise econômica global, que atingiu seu auge no segundo semestre de 2008, e diante de um mercado abalado pela instabilidade econômica, as instituições financeiras tomaram medidas para proteger os riscos de inadimplência dos bancos, medidas que impactavam diretamente na forma de análise nas instituições de crédito para pessoas físicas e jurídicas. Para mitigar o risco dos bancos nas operações de crédito, a maioria destas instituições utiliza uma escala graduada de risco do cliente, que determina a provisão que os bancos devem fazer de acordo com os níveis de risco padrão em cada transação de crédito. A análise de crédito envolve a capacidade de tomar uma decisão de crédito dentro de um cenário de incerteza e mudanças constantes e transformações incompletas. Essa aptidão depende da capacidade de analisar situações lógicas, geralmente complexas e de chegar a uma conclusão clara, prática e praticável de implementar. Os modelos de Credit Score são usados para prever a probabilidade de um cliente propor crédito e tornar-se inadimplente a qualquer momento, com base em suas informações pessoais e financeiras que podem influenciar a capacidade do cliente de pagar a dívida. Essa probabilidade estimada, denominada pontuação, é uma estimativa do risco de inadimplência de um cliente em um determinado período. A mudança constante afeta várias seções bancárias, pois impede a capacidade de investigar os dados que são produzidos e armazenados em computadores que frequentemente dependem de técnicas manuais. Entre as inúmeras alternativas utilizadas no mundo para equilibrar esse risco, destacase o aporte de garantias na formalização dos contratos de crédito. Em tese, a garantia não “garante” o retorno do crédito, já que não é computada como pagamento da obrigação dentro do projeto. Tem-se ainda, o fato de que esta só terá algum êxito se acionada, o que envolve a área jurídica da instituição bancária. A verdade é que, a garantia é um elemento mitigador do risco de crédito. As garantias são divididas em dois tipos, uma garantia individual (patrocinadora) e a garantia do ativo (fiduciário). Ambos visam aumentar a segurança nas operações de crédito, como uma alternativa de pagamento ao titular do crédito fornecido ao credor, se possível, não puder cumprir suas obrigações no prazo. Para o credor, gera segurança de liquidez a partir da operação de recebimento. A mensuração da recuperabilidade do crédito é uma sistemática que avalia a eficiência do mecanismo de retorno do capital investido em garantias. Para tentar identificar a suficiência das garantias nas operações de crédito, esta tese apresenta uma avaliação dos classificadores inteligentes que utiliza informações contextuais para avaliar se as garantias permitem prever a recuperação de crédito concedido no processo de tomada de decisão antes que a operação de crédito entre em default. Os resultados observados quando comparados com outras abordagens existentes na literatura e a análise comparativa das soluções de inteligência artificial mais relevantes, mostram que os classificadores que usam garantias como parâmetro para calcular o risco contribuem para o avanço do estado da arte, aumentando o comprometimento com as instituições financeiras

A Systematic Review of Consumer Behaviour Prediction Studies

Due to the importance of Customer behaviour prediction, it is necessary to have a systematic review of previous studies on this subject. To this effect, this paper therefore provides a systematic review of Customer behaviours prediction studies with a focus on components of customer relationship management, methods and datasets. In order to provide a comprehensive literature review and a classification scheme for articles on this subject 74 customer behaviour prediction papers in over 25 journals and several conference proceedings were considered between the periods of 1999- 2014. Two hundred and thirty articles were identified and reviewed for their direct relevance to predicting customer behaviour out of which 74 were subsequently selected, reviewed and classified appropriately. The findings show that the literature on predicting customer behaviour is ongoing and is of most importance to organisation. It was observed that most studies investigated customer retention prediction and organizational dataset were mostly used for the prediction as compared to other form of dataset. Also, comparing the statistical method to data mining in predicting customer behaviour, it was discovered through this review that data mining is mostly used for prediction. On the other hand, Artificial Neural Network is the most commonly used data mining method for predicting customer behaviour. The review was able to identify the limitations of the current research on the subject matter and identify future research opportunities in customer behaviour prediction

A Systematic Review of Consumer Behaviour Prediction Studies

Due to the importance of Customer behaviour prediction, it is necessary to have a systematic review of previous studies on this subject. To this effect, this paper therefore provides a systematic review of Customer behaviours prediction studies with a focus on components of customer relationship management, methods and datasets. In order to provide a comprehensive literature review and a classification scheme for articles on this subject 74 customer behaviour prediction papers in over 25 journals and several conference proceedings were considered between the periods of 1999-2014. Two hundred and thirty articles were identified and reviewed for their direct relevance to predicting customer behaviour out of which 74 were subsequently selected, reviewed and classified appropriately. The findings show that the literature on predicting customer behaviour is ongoing and is of most importance to organisation. It was observed that most studies investigated customer retention prediction and organizational dataset were mostly used for the prediction as compared to other form of dataset. Also, comparing the statistical method to data mining in predicting customer behaviour, it was discovered through this review that data mining is mostly used for prediction. On the other hand, Artificial Neural Network is the most commonly used data mining method for predicting customer behaviour. The review was able to identify the limitations of the current research on the subject matter and identify future research opportunities in customer behaviour prediction.Keywords: Consumer Behaviour, Prediction, Statistics, Data Mining, Dataset, Customer Relationship Management, Literature Revie

Methodologies for innovation and best practices in Industry 4.0 for SMEs

Today, cyber physical systems are transforming the way in which industries operate, we call this Industry 4.0 or the fourth industrial revolution. Industry 4.0 involves the use of technologies such as Cloud Computing, Edge Computing, Internet of Things, Robotics and most of all Big Data. Big Data are the very basis of the Industry 4.0 paradigm, because they can provide crucial information on all the processes that take place within manufacturing (which helps optimize processes and prevent downtime), as well as provide information about the employees (performance, individual needs, safety in the workplace) as well as clients/customers (their needs and wants, trends, opinions) which helps businesses become competitive and expand on the international market. Current processing capabilities thanks to technologies such as Internet of Things, Cloud Computing and Edge Computing, mean that data can be processed much faster and with greater security. The implementation of Artificial Intelligence techniques, such as Machine Learning, can enable technologies, can help machines take certain decisions autonomously, or help humans make decisions much faster. Furthermore, data can be used to feed predictive models which can help businesses and manufacturers anticipate future changes and needs, address problems before they cause tangible harm