Does the application of artificial intelligence to automate credit scoring processes of potential borrowers allow to improve credit risk management processes and increase profitability of commercial banks' lending activities?
Does the application of specific artificial intelligence technologies for the automation of risk analysis processes, the execution of credit scoring processes for potential individual credit transactions, the ongoing monitoring of open credit transactions and the analysis of changes in the level of credit risk significantly allow the improvement of credit risk management processes and the optimisation of credit processes in the context of improving the profitability of commercial banks' lending activities?
Commercial banks operating according to the classic deposit and credit banking model generate revenues and profits mainly from lending activities. On the one hand, the formation of the quality of the bank loan portfolio and the level of financial results are determined by external factors, i.e. the economic situation in the economy, the economic environment of the bank's customers, borrowers taking bank loans and depositors placing their financial surpluses on bank deposits. On the other hand, the efficiency of the lending business and the development of financial results are also influenced by internal factors, which primarily include the efficiency of the credit risk management process. The credit risk management process is carried out on a stand-alone basis in terms of examining the creditworthiness of the potential or current borrower (ongoing monitoring of the loan granted) and the credit risk the bank accepts in the situation of granting a loan. The credit risk management process is also carried out in banks on a portfolio basis with regard to the entire portfolio of loans granted and by type of loan. Both in terms of the individual and portfolio risk management process, banks are seeking to improve and optimise these processes through the involvement of new information technologies and Industry 4.0. Thanks to these new technologies, banks have the possibility to transfer part of their risk management processes to the bank's internal IT systems and to offer loans also and increasingly via the Internet. Loans of relatively low amounts, consumer loans, instalment loans, i.e. mainly granted to the public, and working capital loans to businesses can already be entirely remote communication with the customer via the Internet. In the case of home and business loans, including investment loans, banks require the borrower to provide various business-related documents to carry out a creditworthiness analysis and examine a number of different economic, financial, operational, investment risk factors, etc., which can be used as a basis for the creditworthiness analysis. As a result, the process of granting these types of business, investment and housing loans, which are usually also for relatively higher amounts, is not yet fully feasible via the Internet when the process of granting these loans itself is not expected to generate high operational risks for banks. However, ongoing technological advances may also change this significantly in the future. At present, banks are trying to implement new Industry 4.0 technologies into their lending activities in order to improve and optimise their costs. The use of new Industry 4.0 technologies in banks is also determined by the need to improve computerised cyber-security systems with a view to constantly improving cyber-security. Recently, key Industry 4.0 technologies that banks are implementing into their operations include artificial intelligence, machine learning technology, deep learning, Big Data Analytics, cloud computing, Internet of Things, Blockchain, multi-criteria simulation models, digital twins, etc. Particularly new opportunities arise in terms of improving both remote marketing communication techniques, optimising banking procedures, reducing the scale of the bank's operational risks and also improving credit procedures and the credit risk management process by involving artificial intelligence in the bank's operations.
In view of the above, I address the following question to the esteemed community of scientists and researchers:
Does the application of specific artificial intelligence technologies for the automation of risk analysis processes, the execution of credit scoring processes for potential individual credit transactions, the ongoing monitoring of open credit transactions and the analysis of changes in the level of credit risk make it possible to improve credit risk management processes and the optimisation of credit processes to a significant extent in the context of improving the profitability of commercial banks' lending activities?
And what is your opinion on this topic?
What is your opinion on this subject?
Please respond,
I invite you all to discuss,
Thank you very much,
Best wishes,
Dariusz Prokopowicz
In my opinion, the application of specific artificial intelligence technologies to automate risk analysis processes, credit scoring processes, and ongoing monitoring of open credit transactions can improve credit risk management processes and increase the profitability of commercial banks' lending activities to a significant extent. AI-driven credit scoring processes can help banks in assessing the creditworthiness of potential borrowers by analyzing a large amount of data efficiently and accurately. This can enable banks to identify the right borrowers and approve loans with lower credit risk, reducing the risk of default and increasing the profitability of the loan portfolio. Additionally, AI-driven monitoring of open credit transactions can help banks in identifying potential changes in credit risk and alerting them to potential risks, thus helping them in taking timely action to mitigate the risks and protect their loan portfolios. AI-driven analysis of changes in credit risk can also help banks in better managing their loan portfolios and optimizing their lending activities to further increase profitability.
Thanks for such an invaluable discussion.
Yes, it is just a play of coding for automating such applications and obviosly enhaces every banjng related processes, such as lending, investing, crediting etc.
We have to wait a little longer to know. It is angering customers with seemingly random card declines- stinks.
Yes, the use of artificial intelligence (AI) to automate credit scoring processes can potentially improve the profitability of commercial banks' lending activities. Here are some reasons why:
AI-powered credit scoring models can analyze vast amounts of data with high precision, allowing for more accurate assessment of credit risk. This can reduce the chances of granting loans to high-risk borrowers and minimize credit losses, thereby improving the profitability of lending activities for commercial banks.
AI can process credit applications quickly and efficiently, reducing the time taken for credit assessments. This can speed up the lending process, allowing banks to serve more customers in less time and potentially generate more loan volume, leading to improved profitability.
Automating credit scoring processes with AI can reduce the need for manual labor and streamline credit assessment workflows. This can result in cost savings for commercial banks by reducing staffing requirements and operational expenses, contributing to improved profitability.
AI-powered credit scoring models can help banks identify and mitigate credit risk more effectively. By leveraging advanced data analytics and machine learning algorithms, banks can better assess creditworthiness, identify potential default risks, and take proactive measures to mitigate them. This can reduce credit losses and improve overall profitability.
AI-powered credit scoring models can enable personalized lending decisions based on individual borrower characteristics, risk profiles, and financial needs. This can result in more tailored loan offers, improved customer satisfaction, and increased loyalty, leading to potential cross-selling opportunities and improved profitability for commercial banks.
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