1,224 research outputs found
Learning Data Quality Analytics for Financial Services
Financial institutions put tremendous efforts on the data analytics work associated with the risk data in recent years. Their analytical reports are yet to be accepted by regulators in financial services industry till early 2019. In particular, the enhancement needs to meet the regulatory requirement the APRA CPG 235. To improve data quality, we assist in the data quality analytics by developing a machine learning model to identify current issues and predict future issues. This helps to remediate data as early as possible for the mitigation of risk of re-occurrence. The analytical dimensions are customer related risks (market, credit, operational & liquidity risks) and business segments (private, wholesale & retail banks). The model is implemented with multiple Long Short-Term Memory ( LSTM ) Recurrent Neural Network ( RNNs ) to find the best one for the quality & prediction analytics. They are evaluated by divergent algorithms and cross-validation techniques
Machine learning applied to banking supervision a literature review
Guerra, P., & Castelli, M. (2021). Machine learning applied to banking supervision a literature review. Risks, 9(7), 1-24. [136]. https://doi.org/10.3390/risks9070136Machine learning (ML) has revolutionised data analysis over the past decade. Like in-numerous other industries heavily reliant on accurate information, banking supervision stands to benefit greatly from this technological advance. The objective of this review is to provide a compre-hensive walk-through of how the most common ML techniques have been applied to risk assessment in banking, focusing on a supervisory perspective. We searched Google Scholar, Springer Link, and ScienceDirect databases for articles including the search terms âmachine learningâ and (âbankâ or âbankingâ or âsupervisionâ). No language, date, or Journal filter was applied. Papers were then screened and selected according to their relevance. The final article base consisted of 41 papers and 2 book chapters, 53% of which were published in the top quartile journals in their field. Results are presented in a timeline according to the publication date and categorised by time slots. Credit risk assessment and stress testing are highlighted topics as well as other risk perspectives, with some references to ML application surveys. The most relevant ML techniques encompass k-nearest neigh-bours (KNN), support vector machines (SVM), tree-based models, ensembles, boosting techniques, and artificial neural networks (ANN). Recent trends include developing early warning systems (EWS) for bankruptcy and refining stress testing. One limitation of this study is the paucity of contributions using supervisory data, which justifies the need for additional investigation in this field. However, there is increasing evidence that ML techniques can enhance data analysis and decision making in the banking industry.publishersversionpublishe
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 banksâ 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 econĂŽmica global, que atingiu seu auge no segundo semestre de 2008, e diante
de um mercado abalado pela instabilidade econÎmica, as instituiçÔes financeiras tomaram
medidas para proteger os riscos de inadimplĂȘncia dos bancos, medidas que impactavam diretamente
na forma de anĂĄlise nas instituiçÔes de crĂ©dito para pessoas fĂsicas e jurĂdicas. Para
mitigar o risco dos bancos nas operaçÔes de crédito, a maioria destas instituiçÔes utiliza uma
escala graduada de risco do cliente, que determina a provisĂŁo que os bancos devem fazer de
acordo com os nĂveis de risco padrĂŁo em cada transação de crĂ©dito. A anĂĄlise de crĂ©dito envolve
a capacidade de tomar uma decisão de crédito dentro de um cenårio de incerteza e mudanças
constantes e transformaçÔes incompletas. Essa aptidão depende da capacidade de analisar situaçÔes
lĂłgicas, geralmente complexas e de chegar a uma conclusĂŁo clara, prĂĄtica e praticĂĄvel
de implementar.
Os modelos de Credit Score sĂŁo usados para prever a probabilidade de um cliente
propor crédito e tornar-se inadimplente a qualquer momento, com base em suas informaçÔes
pessoais e financeiras que podem influenciar a capacidade do cliente de pagar a dĂvida. Essa
probabilidade estimada, denominada pontuação, Ă© uma estimativa do risco de inadimplĂȘncia de
um cliente em um determinado perĂodo. A mudança constante afeta vĂĄrias seçÔes bancĂĄrias,
pois impede a capacidade de investigar os dados que sĂŁo produzidos e armazenados em computadores
que frequentemente dependem de técnicas manuais.
Entre as inĂșmeras alternativas utilizadas no mundo para equilibrar esse risco, destacase
o aporte de garantias na formalização dos contratos de crédito. Em tese, a garantia não
âgaranteâ o retorno do crĂ©dito, jĂĄ que nĂŁo Ă© computada como pagamento da obrigação dentro do
projeto. Tem-se ainda, o fato de que esta sĂł terĂĄ algum ĂȘxito se acionada, o que envolve a ĂĄrea
jurĂdica da instituição bancĂĄria. A verdade Ă© que, a garantia Ă© um elemento mitigador do risco
de crédito. As garantias são divididas em dois tipos, uma garantia individual (patrocinadora) e
a garantia do ativo (fiduciårio). Ambos visam aumentar a segurança nas operaçÔes de crédito,
como uma alternativa de pagamento ao titular do crĂ©dito fornecido ao credor, se possĂvel, nĂŁo
puder cumprir suas obrigaçÔes no prazo. Para o credor, gera segurança de liquidez a partir da
operação de recebimento. A mensuração da recuperabilidade do crédito é uma sistemåtica que
avalia a eficiĂȘncia do mecanismo de retorno do capital investido em garantias.
Para tentar identificar a suficiĂȘncia das garantias nas operaçÔes de crĂ©dito, esta tese
apresenta uma avaliação dos classificadores inteligentes que utiliza informaçÔes 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 anĂĄlise comparativa das
soluçÔes de inteligĂȘncia artificial mais relevantes, mostram que os classificadores que usam
garantias como parùmetro para calcular o risco contribuem para o avanço do estado da arte,
aumentando o comprometimento com as instituiçÔes financeiras
A Review and Bibliography of Early Warning Models
This note is intended to share some observations regarding a non-exhaustive collection of the early warning literature from 1971 to 2011. Evolution of the interest in early warning models, methodological spectrum of studies and coverage of economic variables are briefly discussed in addition to providing a bibliography.Early warning systems, bibliometric analysis
Forecasting inflation with thick models and neural networks
This paper applies linear and neural network-based âthickâ models for forecasting inflation based on Phillipsâcurve formulations in the USA, Japan and the euro area. Thick models represent âtrimmed meanâ forecasts from several neural network models. They outperform the best performing linear models for âreal-timeâ and âbootstrapâ forecasts for service indices for the euro area, and do well, sometimes better, for the more general consumer and producer price indices across a variety of countries. JEL Classification: C12, E31bootstrap, Neural Networks, Phillips Curves, real-time forecasting, Thick Models
A novel intelligent system for securing cash levels using Markov random fields
Financial support from the Spanish Ministry of Universities "Disruptive group decision making systems in fuzzy context: Applications in smart energy and people analytics" (PID2019-103880RB-I00), and Junta de Andalucia (SEJ340) is gratefully acknowledged.The maintenance of cash levels under certain security thresholds is key for the health of the banking sector. In this paper, the monitoring process of branch network cash levels is performed using a single intelligent system which should provide an alert when there are cash shortages at any point of the network. Such an integral solution would provide a unified insight that guarantees that branches with similar cash features are secured as a whole. That is to say, a triggered alarm at a specific branch would indicate that attention must also be paid to similar (in-cash-feature) branches. The system also incorporates a (complementary) specific treatment for individual branches. The Early Warning System for securing cash levels presented in this paper (cash level EWS) is deliberately free of local demographic specifications, thereby overcoming the current lack of worldwide definitions for local demographics. This aspect would be particularly valuable for banking institutions with branch networks all over the world. A further benefit is the cost reductions that are a result of replacing several approaches with a single global one. Instead of local demographic parameters, a solid theoretical model based on Markov random fields (MRFs) has been developed. The use of MRFs means a reduction in the amount of information required. This would mean a higher processing speed as well as a significant reduction in the amount of storage capacity required. To the best of the author's knowledge, this is the first time that MRFs have been applied to cash monitoring.Spanish Ministry of Universities
PID2019-103880RB-I00Junta de Andalucia
SEJ34
A Comprehensive Survey on Enterprise Financial Risk Analysis: Problems, Methods, Spotlights and Applications
Enterprise financial risk analysis aims at predicting the enterprises' future
financial risk.Due to the wide application, enterprise financial risk analysis
has always been a core research issue in finance. Although there are already
some valuable and impressive surveys on risk management, these surveys
introduce approaches in a relatively isolated way and lack the recent advances
in enterprise financial risk analysis. Due to the rapid expansion of the
enterprise financial risk analysis, especially from the computer science and
big data perspective, it is both necessary and challenging to comprehensively
review the relevant studies. This survey attempts to connect and systematize
the existing enterprise financial risk researches, as well as to summarize and
interpret the mechanisms and the strategies of enterprise financial risk
analysis in a comprehensive way, which may help readers have a better
understanding of the current research status and ideas. This paper provides a
systematic literature review of over 300 articles published on enterprise risk
analysis modelling over a 50-year period, 1968 to 2022. We first introduce the
formal definition of enterprise risk as well as the related concepts. Then, we
categorized the representative works in terms of risk type and summarized the
three aspects of risk analysis. Finally, we compared the analysis methods used
to model the enterprise financial risk. Our goal is to clarify current
cutting-edge research and its possible future directions to model enterprise
risk, aiming to fully understand the mechanisms of enterprise risk
communication and influence and its application on corporate governance,
financial institution and government regulation
Application of Bayesian networks in analysing tanker shipping bankruptcy risks
Purpose: This study aims to develop an assessment methodology using a Bayesian network (BN) to predict the failure probability of oil tanker shipping firms.
Design/methodology/approach: This paper proposes a bankruptcy prediction model by applying the hybrid of logistic regression and Bayesian probabilistic networks.
Findings: The proposed model shows its potential of contributing to a powerful tool to predict financial bankruptcy of shipping operators, and provides important insights to the maritime community as to what performance measures should be taken to ensure the shipping companiesâ financial soundness under dynamic environments.
Research limitations/implications: The model and its associated variables can be expanded to include more factors for an in-depth analysis in future when the detailed information at firm level becomes available.
Practical implications: The results of this study can be implemented to oil tanker shipping firms as a prediction tool for bankruptcy rate.
Originality/value: Incorporating quantitative statistical measurement, the application of BN in financial risk management provides advantages to develop a powerful early warning system in shipping, which has unique characteristics such as capital intensive and mobile assets, possibly leading to catastrophic consequences
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
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