This study examines the spillover effect of bank financial misconduct on the uninsured deposits of peer banks within local markets. We first validate that misconduct banks experience an increase in deposit spreads and a corresponding outflow of deposits following the misconduct. We then show local peer banks exhibit divergent deposit responses, contingent on how misconduct is perceived by information recipients in different economic contexts. During normal periods, depositors receiving a negative signal about bank misconduct reallocate their funds from misconduct banks to local peers, a local reallocation effect that decreases deposit spreads and increases deposit inflows for peer banks. Cross‐sectional analysis further reveals that this local reallocation effect is more pronounced for financially sophisticated depositors, amplified when peer banks have strong fundamentals, but attenuated when misconduct banks are financially sound. During financial crisis periods, however, bank misconduct leads to withdrawals from both misconduct banks and their peer banks, a local contagion effect whereby local peer banks face increased deposit spreads and deposit outflows following the misconduct

We investigate how stakeholder-centric corporate misconduct (CM) influences firms’ financial policies. CM is associated with higher cash holdings and lower dividend payouts and debt financing. These effects are more pronounced in firms with stronger governance. We further show that high cash holdings in CM firms are associated with higher firm value and lower implied cost of capital. Firms that replace their CEOs following CM adopt more conservative financial policies. Our evidence supports the precautionary motive for cash holdings, indicating that such reserves are unlikely to result from agency conflicts or increased managerial discretion in CM firms

This study demonstrates that banking organizations with higher artificial intelligence (AI) investments are exposed to more operational risk. Using comprehensive supervisory data on operational losses from large U.S. bank holding companies (BHCs) combined with detailed company-level data on AI-skilled human capital, we show that BHCs with more AI investments suffer higher operational losses per dollar of total assets. The impact of AI investments on operational losses significantly varies by loss type and is driven by external fraud, client-related issues, and system failures. These losses stem not only from small, frequent incidents but also from severe, tail-risk events. The risk-enhancing effect of AI is more pronounced for BHCs with weaker risk management practices. Our findings have important implications for banking performance, risk, and supervision