Risk Management and Business Credit Scoring

Artificial intelligence techniques have long been applied to financial investing scenarios to determine market inefficiencies, criteria for credit scoring, and bankruptcy prediction, to name a few. While there are many subfields to artificial intelligence this work seeks to identify the most commonly applied AI techniques to financial investing as appears in academic literature. AI techniques, such as knowledge-based, machine learning, and natural language processing, are integrated into systems that simultaneously address data identification, asset valuation, and risk management. Future trends will continue to integrate hybrid artificial intelligence techniques into financial investing, portfolio optimization, and risk management. The remainder of this article summarizes key contributions of applying AI to financial investing as appears in the academic literature.

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Risk Pricing in Emerging Economies

International Journal of Finance & Banking Studies (2147-4486) ◽

10.20525/ijfbs.v5i1.41 ◽

2016 ◽

Vol 5 (1) ◽

pp. 51-72

Author(s):

Yiannis Anagnostopoulos ◽

Milad Abedi

Keyword(s):

Risk Management ◽

Credit Risk ◽

Management Practices ◽

Credit Scoring ◽

Real Data ◽

Probit Regression ◽

Credit Risk Management ◽

Risk Pricing ◽

Score Model ◽

Correct Estimation

Iran’s banking industry as a developing country is comparatively very new to risk management practices. An inevitable predictive implication of this rapid growth is the growing concerns with regard to credit risk management which is the motivation of conducting this research. The paper focuses on the credit scoring aspect of credit risk management using both logit and probit regression approaches. Real data on corporate customers are available for conducting this research which is also a contribution to this area for all other developing countries. Our questions focus on how future customers can be classified in terms of credibility, which models and methods are more effective in better capturing risks. Findings suggest that probit approaches are more effective in capturing the significance of variables and goodness-of-fitness tests. Seven variables of the Ohlson O-Score model are used: CL_CA, INTWO, OENEG, TA_TL, SIZE, WCAP_TA, and ROA; two were found to be statistically significant in logit (ROA, TL_TA) and three were statistically significant in probit (ROA, TL_TA, SIZE). Also, CL_CA, ROA, and WCAP_TA were the three variables with an unexpected correlation to the probability of default. The prediction power with the cut-off point is set equal to 26% and 56.91% for defaulted customers in both logit and probit models. However, logit achieved 54.85% correct estimation of defaulted assets, 0.37% more than what probit estimated.

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Applying Artificial Intelligence to Financial Investing

Advanced Methodologies and Technologies in Business Operations and Management - Advances in Logistics, Operations, and Management Science ◽

10.4018/978-1-5225-7362-3.ch001 ◽

2019 ◽

pp. 1-16

Author(s):

Hayden Wimmer ◽

Roy Rada

Keyword(s):

Artificial Intelligence ◽

Risk Management ◽

Language Processing ◽

Credit Scoring ◽

Bankruptcy Prediction ◽

Asset Valuation ◽

Artificial Intelligence Techniques ◽

Academic Literature ◽

Market Inefficiencies ◽

Knowledge Based

Artificial intelligence techniques have long been applied to financial investing scenarios to determine market inefficiencies, criteria for credit scoring, and bankruptcy prediction, to name a few. While there are many subfields to artificial intelligence, this work seeks to identify the most commonly applied AI techniques to financial investing as appears in academic literature. AI techniques, such as knowledge-based, machine learning, and natural language processing, are integrated into systems that simultaneously address data identification, asset valuation, and risk management. Future trends will continue to integrate hybrid artificial intelligence techniques into financial investing, portfolio optimization, and risk management. The remainder of this chapter summarizes key contributions of applying AI to financial investing as appears in the academic literature.

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Credit Scoring and Risk Management for Small Business Lending

Managing Data Mining ◽

10.4018/978-1-59140-243-5.ch003 ◽

2011 ◽

pp. 41-82

Author(s):

Vernon Gerety

Keyword(s):

Risk Management ◽

Small Business ◽

Financial Services ◽

Credit Scoring ◽

Slot Machines ◽

Financial Services Industry ◽

Small Business Lending ◽

Services Industry

“The house always wins” is a common reprise for the empty pocket tourist leaving the gambling tables at Vegas. You do not need to be a professional gambler to understand that the house always wins because the odds are stacked in their favor. Unfortunately, the recent conditions in the financial services industry have been tighter than the quarter slot machines in the Vegas airport.

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Exploration of Financial Market Credit Scoring and Risk Management and Prediction Using Deep Learning and Bionic Algorithm

Journal of Global Information Management ◽

10.4018/jgim.293286 ◽

2022 ◽

Vol 30 (9) ◽

pp. 1-29

Author(s):

Peng Du ◽

Hong Shu

Keyword(s):

Neural Network ◽

Risk Management ◽

Deep Learning ◽

Credit Risk ◽

Financial Market ◽

Recurrent Neural Network ◽

Credit Scoring ◽

Learning Model ◽

Credit Risk Management ◽

Deep Learning Model

The purpose is to effectively manage the financial market, comprehensive assess personal credit, reduce the risk of financial enterprises. Given the systemic risk problem caused by the lack of credit scoring in the existing financial market, a credit scoring model is put forward based on the deep learning network. The proposed model uses RNN (Recurrent Neural Network) and BRNN (Bidirectional Recurrent Neural Network) to avoid the limitations of shallow models. Afterward, to optimize path analysis, bionic optimization algorithms are introduced, and an integrated deep learning model is proposed. Finally, a financial credit risk management system using the integrated deep learning model is proposed. The probability of default or overdue customers is predicted through verification on three real credit data sets, thus realizing the credit risk management for credit customers.

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