How to deal with potential data falsification? TL;DR

We cannot reproduce very good simulation results of our paper and the person who was responsible for writing the code and running the simulations does not have the code anymore and cannot explain how the results were obtained. How should we deal with potential data falsification?

Long version

I co-authored a paper that included some Monte Carlo simulations. One of the coauthors was responsible for the simulations, allegedly wrote the code and gave us the results of the simulations. Later on, I wanted to reuse the code for another project. He reluctantly sent me the code after a long delay. However, the code did not generate the results that we reported in the paper. He said that he lost the code that he used to generate the results of the paper. The code that he sent me is some code that he used at some point of writing the code for the paper, but might not be the final one. He could not explain how the results of the paper were obtained.

We were also working on another project with the same person. He was also responsible for simulations of that project. He again lost the code and could not explain how the results were obtained. The paper of the second project was rejected by the journal and we are going to be able to rerun the simulations and report the correct results.

I suspect that this person just falsified the results.

We are thinking about removing this person from the project and removing this person from the list of authors when we resubmit that paper. Even though the person cannot explain how the results were obtain, there is still a chance that he somehow made some mistake that led to very good results. However, I think it is unlikely. Removing this person from the list of the authors would probably affect his career in a bad way so I would not want to do that without enough evidence. However, I do not think that we can continue working with this person because he lost our trust.

What should we do? How should we deal with this potential data falsification by a coauthor?
In science, a lack of reproducibility leaves you all on the hook. But, I would probably recommend getting a third expert in Monte Carlo to review the code. I'll assume this is you.
First, it can be the case that a small change in code makes big differences, permutation testing for instance can do this. But, you can run the simulation lots of times, look at the results, and find what quintile the original results are in. This corresponds to a p value for falsification, or as I will call it: a possible mistake.
Nonetheless, Monte Carlo is not very complicated, and in the paper regardless you will have to explain how the answers were obtained. If you cannot do this, you cannot push paper into community. So, inspect, conclude, rework. 
For their sake, I hope falsification is not the case. That's a very very ugly way to go about research, and it will crop up.