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The main purpose of this paper is to model bank spread behavior under capital regulation and deposit insurance. Comparative static results show that an increase in the capital-to-deposits ratio or the deposit insurance decreases the bank's interest margin or spread. It is also shown that an increase in the equity that implies its opportunity cost of the coupon rate on the fixed leg decreases the margin. Previous research on market-based evaluations of bank equity has modeled the bank as a narrowing banking firm with risky assets and insured liabilities. The equity of the bank is viewed as a call option on its risky assets. No attempt was made to explicitly analyze a synergy between lending and deposit-taking, and, in our view, the equity of the bank is viewed as a swap option on coupon bonds. Synergy banking, particularly in the return to retail banking, is important in distinguishing banks from other lenders such as finance companies and mutual fund institutions. These other lenders call for the breaking up of banks into separate lending and deposit-taking operations, respectively. Our findings provide an insight for synergy banking operations concerning regulated bank spread behavior in the return to retail banking.

There is an increasing influence of machine learning in business applications, with many solutions already implemented and many more being explored. Since the global financial crisis, risk management in banks has gained more prominence, and there has been a constant focus around how risks are being detected, measured, reported and managed. Considerable research in academia and industry has focused on the developments in banking and risk management and the current and emerging challenges. This paper, through a review of the available literature seeks to analyse and evaluate machine-learning techniques that have been researched in the context of banking risk management, and to identify areas or problems in risk management that have been inadequately explored and are potential areas for further research. The review has shown that the application of machine learning in the management of banking risks such as credit risk, market risk, operational risk and liquidity risk has been explored; however, it doesn't appear commensurate with the current industry level of focus on both risk management and machine learning. A large number of areas remain in bank risk management that could significantly benefit from the study of how machine learning can be applied to address specific problems.

In this paper, we develop a contingent claim model to examine the optimal bank interest margin, i.e., the spread between the domestic loan rate and the deposit market rate of an international bank in distress. The framework is used to evaluate the cross-border lending efficiency for a bank that participates in a government capital injection program, a government intervention used in response to the 2008 financial crisis. This paper suggests that government capital injection is an appropriate way to recapitalize the distressed bank, enhancing the bank interest margin and survival probability. Nevertheless, the government capital injection lacks efficiency when the bank's cross-border lending is high. Stringent capital regulation, suggested to prevent future crises by literature, leads to superior lending efficiency when the government capital injection is low.