326 research outputs found
Credit Scoring with AHP and Fuzzy Comprehensive Evaluation Based on Behavioural Data from Weibo Platform
It is increasingly necessary to evaluate the customers\u27 credit. In the era of big data, Information on the Internet is commonly used to judge the credit worthiness of customers. Some users\u27 credit information is incomplete or unavailable, so credit managers cannot judge the true credit situation of these users. However, with the support of social data especially behavioural data and credit evaluation system, this problem can be effectively solved. This study used Weibo to obtain the behavioural data of Chinese users for credit evaluation. Two methods are used to calculate the credit scores of Weibo users, which are the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation methods. By analysing social processes and inviting experts to make decisions, we constructed a credit evaluation system to expose users\u27 behavioural characteristics. We found that the three key indexes determining the user’s social credit are personal identification, behavioural characteristics and interaction among friends. Then, AHP was used to determine the weight of each index. Finally, a static algorithm was proposed to compute the credit evaluation system of Weibo users using fuzzy comprehensive evaluation methods
La evaluación del riesgo de crédito en las instituciones de microfinanzas
This paper reviews the empirical research focused on credit risk assessment in microfinance institutions (MFIs), particularly identifying those related to Latin America. Since the pioneering work of Vigano (1993), literature has spread over the last two decades, covering a significant number of countries and with the main objective of assessing credit risk. First, this work focuses on identifying the use of credit scoring techniques in the literature to assess the risk of microcredit incurring some type of costly delay. In this way, the MFI could establish measures to mitigate and be more efficient. The theoretical analysis of these investigations shows the majority use of parametric techniques. However, more recent research finds that non-parametric techniques have a greater predictive power of non-compliance by microcredit clients. Second, this paper identifies the determinants of default risk analyzed in previous works. The evidence shows the importance of qualitative information about the borrower, the business and the loan, as well as the use of unstructured data.Este trabajo realiza una revisión de las investigaciones empíricas focalizadas en la evaluación del riesgo de crédito en las instituciones de microfinanzas (IMFs), identificando de forma particular aquellos relativos a América Latina. Desde el trabajo pionero de Vigano (1993), la literatura se ha extendido en las últimas dos décadas, abarcando un número relevante de países y con el objetivo principal de evaluar el riesgo de crédito. Así, en primer lugar, este trabajo se centra en identificar el uso de las técnicas de credit scoring en la literatura para evaluar el riesgo de que los microcréditos incurran en algún tipo de atraso costoso. De este modo, la IMF podría establecer medidas orientadas a mitigarlo y ser más eficiente. El análisis teórico de estas investigaciones muestra la utilización mayoritaria de técnicas paramétricas. Sin embargo, las investigaciones más recientes encuentran que las técnicas no paramétricas tienen un mayor poder predictivo del incumplimiento por parte de los clientes de microcréditos. En segundo lugar, este trabajo identifica los determinantes del riesgo de impago analizados en trabajos previos. La evidencia muestra la importancia de la información cualitativa sobre el prestatario, el negocio y el préstamo, y también el uso de datos no estructurados
La evaluación del riesgo de crédito en las instituciones de microfinanzas
This paper reviews the empirical research focused on credit risk assessment in microfinance institutions (MFIs), particularly identifying those related to Latin America. Since the pioneering work of Vigano (1993), literature has spread over the last two decades, covering a significant number of countries and with the main objective of assessing credit risk. First, this work focuses on identifying the use of credit scoring techniques in the literature to assess the risk of microcredit incurring some type of costly delay. In this way, the MFI could establish measures to mitigate and be more efficient. The theoretical analysis of these investigations shows the majority use of parametric techniques. However, more recent research finds that non-parametric techniques have a greater predictive power of non-compliance by microcredit clients. Second, this paper identifies the determinants of default risk analyzed in previous works. The evidence shows the importance of qualitative information about the borrower, the business and the loan, as well as the use of unstructured data.Este trabajo realiza una revisión de las investigaciones empíricas focalizadas en la evaluación del riesgo de crédito en las instituciones de microfinanzas (IMFs), identificando de forma particular aquellos relativos a América Latina. Desde el trabajo pionero de Vigano (1993), la literatura se ha extendido en las últimas dos décadas, abarcando un número relevante de países y con el objetivo principal de evaluar el riesgo de crédito. Así, en primer lugar, este trabajo se centra en identificar el uso de las técnicas de credit scoring en la literatura para evaluar el riesgo de que los microcréditos incurran en algún tipo de atraso costoso. De este modo, la IMF podría establecer medidas orientadas a mitigarlo y ser más eficiente. El análisis teórico de estas investigaciones muestra la utilización mayoritaria de técnicas paramétricas. Sin embargo, las investigaciones más recientes encuentran que las técnicas no paramétricas tienen un mayor poder predictivo del incumplimiento por parte de los clientes de microcréditos. En segundo lugar, este trabajo identifica los determinantes del riesgo de impago analizados en trabajos previos. La evidencia muestra la importancia de la información cualitativa sobre el prestatario, el negocio y el préstamo, y también el uso de datos no estructurados
Essays on Entrepreneurship in Ecuador: Assessing nonpecuniary effects of access to credit for heterogeneous entrepreneurs
This thesis aims to provide empirical evidence about heterogeneity among entrepreneurs and to explore more in depth the multidimensional concept of entrepreneurship in Ecuador. The thesis is structure in four empirical chapters. Chapter I provides an empirical framework to explore heterogeneity among enterprises and shows that microenterprises in Ecuador are highly heterogeneous. Chapter II explore the presence of mission-drift and trade-offs between social and financial. The results show vary depending on the type of microfinance institution. Chapter III explores gender differences among female and male entrepreneurs in the work-family interface. This chapter shows that female and male entrepreneurs make mostly autonomous entrepreneurial decision-making and are more likely to share decisions about household allocation resources but gender differences appear in decision-making over childbearing and child-rearing. Finally, Chapter IV includes the effect of access to credit over the satisfaction with life of entrepreneurs and shows that having access to a credit has a positive but modest effect of the life satisfaction of entrepreneurs but heterogeneity among female entrepreneurs mask the effects of microcredit programs
The Value of Alternative Data in Credit Risk Prediction: Evidence from a Large Field Experiment
Recently, the high penetration of mobile devices and internet access offers a new source of fine-grained user behavior data (aka “alternative data”) to improve the financial credit risk assessment. This paper conducts a comprehensive evaluation of the value of alternative data on microloan platforms with a large field experiment. Our machine-learning-based empirical analyses demonstrate that alternative data can significantly improve the prediction accuracy of borrowers’ default behavior and increase platform profits. Cellphone usage and mobility trace information perform the best among the multiple sources of alternative data. Moreover, we find that our proposed framework helps financial institutions extend their service to more lower-income and less-educated loan applicants from less-developed geographical areas – those historically disadvantaged population who have been largely neglected in the past. Our study demonstrates the tremendous potential of leveraging alternative data to alleviate such inequality in the financial service markets, while in the meantime achieving higher platform revenues
Tree-Based Approaches for Predicting Financial Performance
The lending industry commonly relied on assessing borrowers’ repayment performance to make lending decisions. This is to safeguard their assets and maintain their profitability. With the rise of Artificial Intelligence, lenders resorted to Machine Learning (ML) algorithms to solve this problem.
In this study, the novelty introduced is applying ML’s Tree-based methods to a large dataset and accurately predicting financial repayment performance without using any repayment history, which was utilized in all literature reviewed. Instead, the attributes used were demographics and psychographics of applicants, only. The study’s proprietary US-based dataset comprises an anonymous population whose owner does not wish to be disclosed and it contains the information of about half a million beneficiaries with a very balanced bimodal binary target distribution.
An Area Under the Curve of Receiver Characteristic Operator (ROC-AUC) of 85% was achieved with a binary classification target using CatBoost API. The study also experimented with a given tri-class target. Furthermore, this research used ML to gain insight into which attributes contribute the most to the repayment prediction. The study also tested whether similar results can be achieved with fewer attributes for the sake of the practicality of application by the data owner. The best model was applied to one of the biggest publicly available financial datasets for verification. The original research of said dataset had an accuracy score of 82%, this study achieved 79% using 5-fold Cross-Validation (CV). This result was achieved with Tree-Based models with a complexity of O(log n) compared to O(2n) in the original research, which is a significant efficiency enhancement
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
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