scholarly journals Challenges of Financial Risk Management: AI Applications

2021 ◽  
Author(s):  
Vesna Bogojevic Arsic
Keyword(s):  
Artificial Intelligence ◽  
Risk Management ◽  
Credit Risk ◽  
Financial Risk ◽  
Market Risk ◽  
Credit Risk Management ◽  
Financial Industry ◽  
Financial Risk Management ◽  
Artificial Intelligence Techniques ◽  
Financial Technology

Research Question: This paper reviews different artificial intelligence (AI) techniques application in financial risk management. Motivation: Financial technology has significantly changed the business operations which required transformation of financial industry. The financial risk management needs to be restructured because the methods that have been used in the past became low effective. The artificial intelligence techniques proved their efficiency and contributed to fast, low–cost and improved financial risk management in both financial institutions and companies. Idea: The aim of this paper is to present a state of AI techniques application in financial risk management, as well as to point out the direction in which further application and development could be expected. Data: The analysis was conducted by reviewing various papers, books and reports on AI applications in financial risk management. Tools: The relevant literature systematization was used to provide answers to the question to what extent AI techniques (especially machine learning) could be used in managing financial risk management. Findings: Artificial intelligence largely improved the market risk and credit risk management through data preparation, modelling risk, stress testing and model validation. Artificial intelligence techniques can be useful in data quality assurance, text-mining for data augmentation and fraud detection. The financial technology will continue to affect the financial sector through requiring the adaption to new environment and new business models. Because of that, it could be expected that artificial intelligence will become part of the financial risk management framework. Contribution: This paper provides a review of artificial intelligence applications in market risk management, credit risk management and operational risk management. The paper identified the key AI techniques that could be used for financial risk management improvement because of financial industry transformation.

scholarly journals Explainable Machine Learning in Credit Risk Management

2020 ◽  
Author(s):  
Niklas Bussmann ◽  
Paolo Giudici ◽  
Dimitri Marinelli ◽  
Jochen Papenbrock
Keyword(s):  
Artificial Intelligence ◽  
Risk Management ◽  
Credit Risk ◽  
Credit Risk Management ◽  
Credit Score ◽  
Correlation Networks ◽  
Explainable Artificial Intelligence ◽  
Shapley Values ◽  
Financial Characteristics ◽  
The Empirical Analysis

Abstract The paper proposes an explainable Artificial Intelligence model that can be used in credit risk management and, in particular, in measuring the risks that arise when credit is borrowed employing peer to peer lending platforms. The model applies correlation networks to Shapley values so that Artificial Intelligence predictions are grouped according to the similarity in the underlying explanations. The empirical analysis of 15,000 small and medium companies asking for credit reveals that both risky and not risky borrowers can be grouped according to a set of similar financial characteristics, which can be employed to explain their credit score and, therefore, to predict their future behaviour.

Retail Credit Risk Management in Indian Public Sector Banks

2012 ◽  
Vol 3 (8) ◽  
pp. 31-37
Author(s):  
Nayan J. Nayan J. ◽  
◽  
Dr. M. Kumaraswamy Dr. M. Kumaraswamy
Keyword(s):  
Risk Management ◽  
Public Sector ◽  
Credit Risk ◽  
Credit Risk Management ◽  
Public Sector Banks ◽  
Retail Credit

scholarly journals Modeling of Bank Credit Risk Management Using the Cost Risk Model

2021 ◽  
Vol 14 (5) ◽  
pp. 211
Author(s):  
Iryna Yanenkova ◽  
Yuliia Nehoda ◽  
Svetlana Drobyazko ◽  
Andrii Zavhorodnii ◽  
Lyudmyla Berezovska
Keyword(s):  
Risk Management ◽  
Decision Support ◽  
Credit Risk ◽  
Risk Model ◽  
Bank Credit ◽  
Credit Risk Management ◽  
Loan Default ◽  
Increased Risk ◽  
Credit Portfolio ◽  
Cost Risk

This article deals with the issue of managing bank credit risk using a cost risk model. Modeling of bank credit risk management was proposed based on neural-cell technologies, which expand the possibilities of modeling complex objects and processes and provide high reliability of credit risk determination. The purpose of the article is to improve and develop methodical support and practical recommendations for reducing the level of risk based on the value-at-risk (VaR) methodology and its subsequent combination with methods of fuzzy programming and symbiotic methodical support. The model makes it possible to create decision support subsystems for nonperforming loan management based on the neuro-fuzzy approach. For this paper, economic and mathematical tools (based on the VaR methodology) were used, which made it possible to analyze and forecast the dynamics of overdue payment; assess the quality of the credit portfolio of the bank; determine possible trends in bank development. A scientific and practical approach is taken to assess and forecast the degree of credit problematicity by qualitative criteria using a mathematical model based on a fuzzy technology, which can forecast the increased risk of loan default at an early stage in the process of monitoring the loan portfolio and model forecasting changes in the degree of credit problematicity on change of indicators. A methodology is proposed for the analysis and forecasting of indicators of troubled loan debt, which should be implemented as software and included in the decision support system during the process of monitoring the risk of the bank’s credit portfolio.

An Investigation of Credit Risk Management Strategies of Private Commercial Banks of Bangladesh

2014 ◽  
Vol 2 (2) ◽  
pp. 77-87
Author(s):  
Abu Hanifa Md. Noman ◽  
Md. Amzad Hossain ◽  
Sajeda Pervin
Keyword(s):  
Risk Management ◽  
Credit Risk ◽  
Commercial Banks ◽  
Management Practices ◽  
Management Practice ◽  
Management Strategies ◽  
Third Party ◽  
Primary Data ◽  
Credit Risk Management ◽  
Risk Management Strategies

Objective - The study aims to investigate credit risk management practices and credit risk management strategies of the local private commercial banks in Bangladesh. Methodology -The investigation is conducted based on primary data collected from a set of both closed end and open end questionnaire from 23 out of 39 local private commercial banks in Bangladesh. Descriptive statistics has been used in processing the data and interpreting the results. Findings - The results reveal that credit risk management practice of the sample banks is sound which is attributed to the appropriate implementation of Basel II and credit risk management guidelines the country's central bank. The findings further show that use of Credit risk grading is most popular and effective criteria for measuring the borrowing capacity of the borrowers. In order to control credit risk and preventing losses from credit exposure banks give more focus on collateralization, accurate loan pricing and third party guarantee. Loan is monitored properly and credit reminder is given to the client if principal and interest remain outstanding for three months. The study further reveals that lack of experienced and trained credit officers, lack of genuine market information and Lack of awareness regarding non-genuine borrower are the most important problems of current credit risk management practices in Bangladesh. Novelty - To the best of the knowledge of the authors the study is the first that investigates credit risk management strategies of private commercial banks, especially on Bangladesh. Type of Paper - Empirical Keyword : Bangladesh; Commercial Bank; Credit risk; Credit risk management; Credit risk management strategies.

Credit Risk Management for College Students: An Analysis of Influencing Factors Based on 5C Approach

2021 ◽  
Vol 12 (1) ◽  
pp. 105-105
Author(s):  
Xiang Zou ◽  
Jinting Zhao ◽  
Yun Tong
Keyword(s):  
College Students ◽  
Risk Management ◽  
Credit Risk ◽  
Evaluation System ◽  
Least Squares Method ◽  
Credit Risk Management ◽  
Credit Evaluation ◽  
Personal Credit ◽  
Depth Analysis ◽  
Credit Evaluation System

This paper focuses on the construction of college students' credit evaluation system and credit risk management under the background of big data. Firstly, based on the 5C approach, this paper evaluates the personal credit of college students from 5 dimensions and 24 indicators, which finally contribute to the establishment of the credit evaluation system for college students. Then, the partial least squares method is used to build the structural equation model to evaluate the effectiveness of the credit evaluation system for college students. According to the in-depth analysis of PSL-SEM, the factors that affect the credit risk of college students are effectively evaluated, and it has contributed to the establishment and improvement of the credit system of college students. Keywords: Personal Credit, Credit Evaluation, Credit Risk, 5C Approach, PLS-SEM.

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