Does the application of Big Data Analytics and artificial intelligence technologies in the credit scoring processes of potential borrowers increase the profitability of commercial banks' lending activities?

Does the application of Big Data Analytics and artificial intelligence technologies in the processes of screening the creditworthiness of potential borrowers in order to improve, among other things, credit scoring analytics and credit risk management increase the profitability of commercial banks' lending activities?

In recent years, the scale of application of ICT and Industry 4.0/5.0, including Big Data Analytics and Artificial Intelligence technologies in financial institutions, including commercial banks, has been increasing. The banking sector is among those sectors of the economy where the implementation of new information technologies used to build banking information systems is progressing rapidly. This process in highly developed countries has been taking place since the 1960s. Subsequently, the development of computer science, personal computer technology in the 1970s and 1980s, the development of the Internet and business applications of Internet technology since the 1990s and then the development of technologies typical of Industry 4.0/5.0 set the trends of technological progress, the effects of which in the form of new technological solutions quickly found applications in financial institutions. Commercial banks operating in the model of classic deposit-credit banking usually generate the largest part of their revenues from the sale of bank loans and credits. Large universal banks also develop selected elements of investment banking, in which they finance the construction of housing estates through their own development companies, make financial transactions with securities, financial transactions in foreign exchange markets and other capital markets. In all these areas of activity, the key categories of banking risk that banks manage include credit and interest rate risk and other financial risks, i.e. liquidity risk, debt risk. In addition, the key categories of risk that the bank manages in its banking operations include asset-liability mismatch risk in the balance sheet and various categories of operational risks related to the performance of certain activities at the bank, including personnel operational risk related to the staff employed, technical operational risk related to the technical equipment used, system operational risk related to the IT systems used, etc. On the other hand, risks operating in the bank's environment and affecting the bank's operations and indirectly also the bank's financial performance include market risk of changes in the prices of specific assortments relating to specific markets in which banks operate; foreign exchange risk associated with transactions made using different currencies; investment risk within investment banking; systemic risk associated with the functioning of the financial system; political risk associated with the government's economic policy; risks of high volatility of macroeconomic development of the economy associated with changes in the economy's economic situation in the context of business cycles realized on a multi-year scale, etc. However, in a situation where lending activities are the main types of sources of income for a commercial bank then a particularly important category of banking risk that the bank manages is credit risk. On the other hand, due to the rapid development of electronic, Internet and mobile banking, cyber risk management is also growing in importance. New ICT information technologies and Industry 4.0/5.0, including Big Data Analytics and Artificial Intelligence technologies, can be increasingly helpful in managing each of the aforementioned risk categories. The aforementioned new technologies prove to be particularly helpful in the situation of their effective implementation into banking activities in order to improve the processes of managing, among other things, credit risk. An important element of individual credit risk management, i.e. with regard to individual credit transactions, are the methodologies, procedures, processes, etc. concerning the analysis of a potential borrower's creditworthiness and credit risk arising from a bank loan carried out in commercial banks. In view of the above, the implementation of new technologies to support the implementation of the processes of examining the creditworthiness of potential borrowers and improving, among other things, credit scoring analytics, are particularly important aspects of credit risk management, which may translate into increased profitability of commercial banks' bank lending activities.

I described selected issues of improving credit risk management processes, including the issue of screening the creditworthiness of potential borrowers and credit scoring analytics, in an article of my co-authorship:

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In view of the above, I address the following question to the esteemed community of scientists and researchers:

Does the application of Big Data Analytics and artificial intelligence technologies in the processes of screening the creditworthiness of potential borrowers in order to improve, among other things, credit scoring analytics and credit risk management, result in an increase in the profitability of commercial banks' bank lending activities?

Does the application of Big Data Analytics and artificial intelligence technologies in the credit scoring processes of potential borrowers result in increased profitability of commercial banks' lending business?

Can Big Data Analytics and artificial intelligence help improve credit scoring and increase the profitability of commercial banks' lending activities?

What do you think about this topic?

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best regards,

Dariusz Prokopowicz

The above text is entirely my own work written by me on the basis of my research.

In writing this text, I did not use other sources or automatic text generation systems.

Copyright by Dariusz Prokopowicz