Research on the Application of Computer Data Mining Technology in the Financial Risk Management

2014 ◽  
Vol 513-517 ◽  
pp. 1940-1943
Author(s):  
Li Hong Yu ◽  
Ya Li Xu ◽  
Lin Dai
Keyword(s):  
Data Mining ◽  
Risk Management ◽  
Financial Risk ◽  
Customer Relationship ◽  
Financial Industry ◽  
Mining Technology ◽  
Typical Application ◽  
Computer Data ◽  
Financial Risk Management ◽  
Implicit Data

The computer data mining technology plays an important role in the financial risk management. It can extract the implicit data that people don't know in advance, in the mean time, and potentially useful information and knowledge for managers to provide decision-making reference. This paper introduces the concept of data mining, the process and main technology first, and then introduces the typical application of data mining in the financial risk management, such as customer relationship management, credit risk assessment and financial crisis early warning analysis. At last, it has a summary to provide the risk management for the financial industry.

API Standardization Efforts for Data Mining

2011 ◽  
pp. 39-43
Author(s):  
Jaroslav Zendulka
Keyword(s):  
Data Mining ◽  
Risk Management ◽  
Customer Relationship Management ◽  
Real World ◽  
Relationship Management ◽  
Scientific Data ◽  
Customer Relationship ◽  
Mining Technology ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Data mining technology just recently became actually usable in real-world scenarios. At present, the data mining models generated by commercial data mining and statistical applications are often used as components in other systems in such fields as customer relationship management, risk management or processing scientific data. Therefore, it seems to be natural that most data mining products concentrate on data mining technology rather than on the easy-to-use, scalability, or portability. It is evident that employing common standards greatly simplifies the integration, updating, and maintenance of applications and systems containing components provided by other producers (Grossman, Hornick, & Meyer, 2002). Data mining models generated by data mining algorithms are good examples of such components.

scholarly journals A Big Data Mining Approach of PSO-Based BP Neural Network for Financial Risk Management With IoT

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 154035-154043 ◽  
Author(s):  
Hangjun Zhou ◽  
Guang Sun ◽  
Sha Fu ◽  
Jing Liu ◽  
Xingxing Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Risk Management ◽  
Big Data ◽  
Bp Neural Network ◽  
Financial Risk ◽  
Financial Risk Management ◽  
Big Data Mining ◽  
Data Mining Approach

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.

FINANCIAL RISK MANAGEMENT IN ENTREPRENEURIAL ACTIVITY: CONCEPT, SYSTEM AND PRINCIPLE OF BUILDING

2020 ◽  
Vol 2 (4) ◽  
pp. 62-67
Author(s):  
M. M. KHAYTANOVA ◽  
Keyword(s):  
Risk Management ◽  
Financial Risk ◽  
Entrepreneurial Activity ◽  
Financial Risks ◽  
Study Methods ◽  
Financial Risk Management ◽  
Concept System

The article reveals: theoretical justifications of the concept of “financial risk” in relation to the sphere of entrepreneurship; methods for its identification and processing. Financial risk management is the activity of identification, assessment, control and monitoring of risks. In the course of the study, methods for managing financial risks in entrepreneurial activity and their classification were identified.

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