  The existence of asymmetric information has always been a major concern for
financial institutions. Financial intermediaries such as commercial banks need
to study the quality of potential borrowers in order to make their decision on
corporate loans. Classical methods model the default probability by financial
ratios using the logistic regression. As one of the major commercial banks in
France, we have access to the the account activities of corporate clients. We
show that this transactional data outperforms classical financial ratios in
predicting the default event. As the new data reflects the real time status of
cash flow, this result confirms our intuition that liquidity plays an important
role in the phenomenon of default. Moreover, the two data sets are
supplementary to each other to a certain extent: the merged data has a better
prediction power than each individual data. We have adopted some advanced
machine learning methods and analyzed their characteristics. The correct use of
these methods helps us to acquire a deeper understanding of the role of central
factors in the phenomenon of default, such as credit line violations and cash
inflows.
