At the end of 2012, the chief information officer (CIO) at UCB, a global pharmaceutical company based in Brussels, started to implement analytics as a service. Between 2012 and 2016, he put this vision into practice, introducing agile sprints and proving the competence of analytics within the organization, and at the beginning of 2016, he felt the company was ready to upgrade its analytics capability. As he prepared to meet with UCB’s chief executive officer in March 2016, the CIO considered how to advise the board as the organization worked to make an impact with analytics and big data against the backdrop of digital turbulence in its strategic environment. How could UCB balance empowerment and bottom-up experimentation with enterprise focus and control? What was the best location for analytics roles and responsibilities within the organization?

In this paper we use corporate syndicated loan data to study the presence of a bank risk-taking channel of unconventional monetary policy in the United States over the period 2008-2015. To account for both actual policy decisions and anticipation effects, we measure the stance of monetary policy by estimating a financial VAR model. We find that accommodative monetary conditions are associated with overall lower loan spreads. Controlling for borrower creditworthiness, we show that the spread reduction is lower for riskier firms, indicating that risk is appropriately priced during the period of unconventional monetary policy. Banks with low non-performing loan ratios and banks characterized by high revenue diversification offer larger loan spread discounts compared to banks with a large amount of non-performing loans and banks with less income diversification. We also find that banks with low capital ratios, less profitable banks and smaller banks more aggressively reduce the corporate loan spreads following an expansionary monetary policy shock, but only for the safest firms. Our findings indicate that unconventional monetary policy actions of the Federal Reserve are not associated with excessive risk taking by banks in the syndicated loan market.

In the aftermath of the financial crisis, this study investigates which underlying determinants cause bank rating transitions. We develop survival analysis models to explain credit transition hazards using macroeconomic factors and the rating history. We find that there exists a significant dependence of rating upgrade or rating downgrade transition hazards on rating-specific covariates and macro-economic covariates. Our results confirm the momentum effect, meaning that a financial institution that has been recently upgraded/downgraded has a higher chance of being upgraded/downgraded again. The predictive performance of the developed models turns out to be satisfactory.