  We introduce new approaches for forecasting IBNR (Incurred But Not Reported)
frequencies by leveraging individual claims data, which includes accident date,
reporting delay, and possibly additional features for every reported claim. A
key element of our proposal involves computing development factors, which may
be influenced by both the accident date and other features. These development
factors serve as the basis for predictions. While we assume close to continuous
observations of accident date and reporting delay, the development factors can
be expressed at any level of granularity, such as months, quarters, or year and
predictions across different granularity levels exhibit coherence. The
calculation of development factors relies on the estimation of a hazard
function in reverse development time, and we present three distinct methods for
estimating this function: the Cox proportional hazard model, a feed-forward
neural network, and xgboost (eXtreme gradient boosting). In all three cases,
estimation is based on the same partial likelihood that accommodates left
truncation and ties in the data. While the first case is a semi-parametric
model that assumes in parts a log linear structure, the two machine learning
approaches only assume that the baseline and the other factors are
multiplicatively separable. Through an extensive simulation study and
real-world data application, our approach demonstrates promising results. This
paper comes with an accompanying R-package, $\texttt{ReSurv}$, which can be
accessed at \url{https://github.com/edhofman/ReSurv}
