Credit Scoring With Artificial Intelligence
Deep Learning Finance The emergence of artificial intelligence has significantly altered computer systems as we know them. Currently, organizations deploying AI systems develop robust business solutions with limited corporate data and human intervention. Furthermore, such remarkable achievements in corporate computing have enabled organizations to maximize profits through effective decision-making frameworks, minimize losses through a comprehensive analysis of market information, and propel corporate growth through adequate data analysis.
In addition, it was Deep Learning Godfather & NYU Courant Professor Yann LeCun who first used deep learning to read checks.
Furthermore, it has enabled these businesses to optimize organizational processes despite limited consumer and market information. One of the business segments where AI has effectively been implemented is in the financial sector. In addition, the artificial intelligence systems enable commercial service providers to offer robust credit services based on a compressive review of the consumers. This essay examines how AI has improved financial services and enhanced consumer satisfaction in the sector.
Artificial intelligent systems are extensively used in the financial sector to provide sound credit decisions.
Moreover, these systems offer a quick and reliable assessment of the consumers which reduces the evaluation costs and offers improved decision-making frameworks that enable the financial service providers to adequately segment the customers (Danenas & Garsva, 2016). The credit-oriented decision support systems provide a comprehensive review of the customers when compared to the traditional credit scoring systems.
Through this approach, financial service providers adequately identify credit risks associated with different consumers based on their scoring outcomes. Moreover, an excellent example is the loan issuing apps and digital banks. These financial service providers rely on machine learning algorithms that analyze consumer data stored on their phones as well as other relevant repositories (Abellan & Castellano, 2017). As a result, by reviewing this data, the apps determine the suitability of the customers and appropriate credit limits. In addition, this assessment approach reduces default risks and optimizes the performance of financial institutions.
In conclusion, artificial intelligence has significantly revolutionized credit operations among financial institutions. Firstly, hese systems enable commercial service providers to adequately review the credit history of their consumers in a reasonable time. Secondly, this credit scoring approach is comprehensive and reliable when compared to conventional methods. Thirdly, the outcome of such credit operations is reduced risks of default and enhanced business performance and productivity. Lastly, thus, the application of artificial intelligence in the sector has improved financial service delivery and effective consumer segmentation.
Deep Learning Finance Written by Richard Harrison
Abellan, J., & Castellano, J. G. (2017). A comparative study on base classifiers in ensemble methods for credit scoring. Expert Systems with Applications, 73, 1-10.Danenas, P., & Garsva, G. (2016). Intelligent credit risk decision support: Architecture and implementations. In C. L. Dunis, P. W. Middleton, A. Karathanasopolous & K. Theofilatos (Eds.). (2016). Artificial intelligence in financial markets: Cutting edge applications for risk management, portfolio optimization, and economics (pp. 179-210). Palgrave: London.