In credit risk modeling, what does the statistical analysis predict?

The correct choice focuses on the central aspect of credit risk modeling, which is the likelihood of a borrower defaulting on a loan. Credit risk modeling employs statistical techniques to analyze historical data about borrowers, their financial conditions, and economic factors. This analysis generates estimates of the probability of default (PD) for individual borrowers or groups of borrowers.

By predicting the likelihood of default, financial institutions can assess the risk associated with lending to a particular borrower. This insight allows them to set appropriate interest rates, establish lending limits, and create effective risk management strategies. Understanding default probabilities is fundamental for assessing credit risk as it directly impacts the financial institution's potential losses and overall risk exposure.

The other options are related to credit risk but do not encapsulate the primary focus of what statistical analysis seeks to predict in the context of credit risk modeling. For instance, while the diversification potential of a loan portfolio is relevant, it is a broader concept that relates to the benefits of mixing various loans to reduce risk rather than directly predicting default probabilities. Similarly, expected profits from loan origination and the market value of a loan over time involve financial forecasting and valuation, rather than the specific statistical analysis of default likelihood. Thus, option B distinctly highlights the core function of credit risk statistical analysis.