**4.6 Senstivity**

Banks are subject to market risks (i.e., interest rate risk, foreign exchange risk, price risk etc) that can have adverse effects on bank's earnings and capital. AI provides solutions to real world problems [30], through real time, enabling banks to keep up, adapt and respond to constant and dynamic changes in the environment. Thus, improving bank stability and soundness.

The constant influx in business environments, banking, credit and regulatory standards, bank lending strategies, marketing strategies of banks, investor requirements, customer demands and borrowing patterns of customers require frequent revisions [30, 60]. ML systems not only can execute rules and keep up with the change in process, but it is also able to process this information in a few milliseconds [13] as it works on real time.

Neural networks identify interconnected nodes through multi-layered data from multiple disciplines, such as statistics, computer science, biology, psychology, economics i.e., game theory, and applies multitude techniques to derive meaning [40].

ML applies algorithms to sift through hundreds of thousands of factors to proficiently detect [30, 62], decode patterns and linkages in the data by continuously updating "learning" [40]. Deep learning works on pools of high- data to identify patterns of patterns. Pattern recognition uses tools such as natural language processing to classify and interpret data [40].

ML uncovers relationships beyond causal i.e., relationships that is yet to be established by theory. Supervised ML also understands non-linear relationships while unsupervised ML reveals commonalities amongst different groups whilst

**279**

*Artificial Intelligence and Bank Soundness: A Done Deal? - Part 1*

cal models and is a crucial tool in decision making processes.

highlighting outliers. ML not only unravels hidden relationships but also provides additional information about the dataset which can be further used by banks and financial supervisors alike to understand the workings of the financial markets and

AI algorithms work with soft-computing approaches beyond parametric statistical methods (e.g., discriminant analysis and logistic regression) and nonparametric statistical methods (e.g., k nearest neighbour and decision trees) [10]. AI also works with variables that give contradicting signs, model noisy, inconsistent, or incomplete data [63, 64], identify both linear and non-linear relationships [30], work more closely with real world non-linear applications and can handle uncertain behaviours that changes over time [10]. AI's accuracy surpasses traditional statisti-

Advancement in AI methodologies have also enhanced the robustness of predictive models, and thus, its outcomes. As such enabling users to predict future outcomes and make decisions more effectively [62], efficiently and more accurately.

In this chapter we were keen to explore how able are banks to effectively deploy AI into their daily operations to improve CAMELS from a bank's perspective. It has become apparent that AI contribution is limitless, and its uses are infinite [65–67], offering significant possibilities for banks to survive. Government are in joint agreement that AI will not only help banks survive but also contribute to better functioning markets. This is evident in their continuous efforts to fund, invest and support AI related projects. The chapter has successfully portrayed bank soundness in the face of AI through the lens of CAMELS. The taxonomy partitions opportunities into distinct categories of 1 (C), 6(A), 17(M), 16 (E), 3(L), 6(S). The results re-emphasise AI's advantages as being countless and numerous in helping banks to deliver world class services to its customers through efficient and effective processes. However, future study should look to investigate further the use of AI in capital and liquidity aspects as these are the core determinants of bank survival but

for now, AI allows banks to survive and evolve. As such, it is a done deal.

Competition and Survival Nature of Banking Industry Future customers Beyond human capacity

Asset • Unstructured data help increase number of eligible customers • Better evaluator and assessor of credit risk

• Works with large dataset effortlessly

• Creditworthy assessment beyond the norm

• Improve loan process in terms of inconsistency, inaccuracy, biasness, and

• Increases processing speed

*Opportunities*

bureaucracy

*DOI: http://dx.doi.org/10.5772/intechopen.95539*

institutions better [15, 21].

**5. Conclusion**

**Appendix**

**Government Support AI & Banks**

Capital Stress Testing

*Artificial Intelligence and Bank Soundness: A Done Deal? - Part 1 DOI: http://dx.doi.org/10.5772/intechopen.95539*

highlighting outliers. ML not only unravels hidden relationships but also provides additional information about the dataset which can be further used by banks and financial supervisors alike to understand the workings of the financial markets and institutions better [15, 21].

AI algorithms work with soft-computing approaches beyond parametric statistical methods (e.g., discriminant analysis and logistic regression) and nonparametric statistical methods (e.g., k nearest neighbour and decision trees) [10]. AI also works with variables that give contradicting signs, model noisy, inconsistent, or incomplete data [63, 64], identify both linear and non-linear relationships [30], work more closely with real world non-linear applications and can handle uncertain behaviours that changes over time [10]. AI's accuracy surpasses traditional statistical models and is a crucial tool in decision making processes.

Advancement in AI methodologies have also enhanced the robustness of predictive models, and thus, its outcomes. As such enabling users to predict future outcomes and make decisions more effectively [62], efficiently and more accurately.
