AI training data may contain historical social biases, which poses the risk of unfair evaluation of minority groups. In
Posted: Wed Feb 19, 2025 3:20 am
Ethical issues in AI scoring and measures against bias
In AI credit scoring, ethical issues and measures against bias are important points of discussion.
these issues, the following efforts are being made. First, an AI team is formed with members from diverse backgrounds to eliminate bias from the development stage. Second, AI is trained with data that fairly represents all social groups. In addition, the model is regularly monitored and adjusted to prevent the occurrence of bias.
In this way, by addressing the ethical issues of AI scoring, we aim to realize fairer and more reliable financial services.
assignment countermeasure
Training data bias Collecting and verifying various data
Social bias Development by diverse teams
Unfairness in evaluation Regular model monitoring and adjustment
4-3. Evolution of next-generation financial services and lebanon whatsapp number data future prospects for AI technology
AI technology in the financial industry is expected to evolve further by 2025. Particularly noteworthy is the fusion of quantum computing and AI, which has the potential to improve the accuracy of market analysis by more than 10 times. In addition
, the development of self-learning AI algorithms is also advancing the practical application of systems that predict market fluctuations in real time and automatically make optimal investment decisions. The construction of AI-based transaction monitoring systems is also accelerating in preparation for the practical application of central bank digital currencies (CBDCs). This is expected to lead to a detection rate of fraudulent transactions of more than 99%.
Furthermore, the spread of open banking will lead to the personalization of financial services using AI, making it possible to propose products and asset management advice optimized for each individual customer.
In AI credit scoring, ethical issues and measures against bias are important points of discussion.
these issues, the following efforts are being made. First, an AI team is formed with members from diverse backgrounds to eliminate bias from the development stage. Second, AI is trained with data that fairly represents all social groups. In addition, the model is regularly monitored and adjusted to prevent the occurrence of bias.
In this way, by addressing the ethical issues of AI scoring, we aim to realize fairer and more reliable financial services.
assignment countermeasure
Training data bias Collecting and verifying various data
Social bias Development by diverse teams
Unfairness in evaluation Regular model monitoring and adjustment
4-3. Evolution of next-generation financial services and lebanon whatsapp number data future prospects for AI technology
AI technology in the financial industry is expected to evolve further by 2025. Particularly noteworthy is the fusion of quantum computing and AI, which has the potential to improve the accuracy of market analysis by more than 10 times. In addition
, the development of self-learning AI algorithms is also advancing the practical application of systems that predict market fluctuations in real time and automatically make optimal investment decisions. The construction of AI-based transaction monitoring systems is also accelerating in preparation for the practical application of central bank digital currencies (CBDCs). This is expected to lead to a detection rate of fraudulent transactions of more than 99%.
Furthermore, the spread of open banking will lead to the personalization of financial services using AI, making it possible to propose products and asset management advice optimized for each individual customer.