Replace human judgements with mathematical approach/theories
When it comes to choosing a threshold, I can see 3 approaches:


Make an educated guess


This is what you are currently doing. You pick a value and would need to argue why this is a reasonable threshold. Obviously, the argument is as strong as the assumptions you make.


Unsupervised way


If you compute the matching score for all pairs between A and B, you can analyze the histogram of these scores. Most likely, you will have quite a lot of scores near 0 and a few scores near 100. Then, you can decide to pick a threshold. This itself can be done in different ways. You can pick the median/mean matching score, this becomes close to option 1 but at least, the number comes from a specific mathematical concept. You can use the Jenks-Fisher algorithm (here is a Python implementation). In summary, the algorithm will find a threshold that splits your data into clusters which minimize the intra-cluster deviation. This would be better than the median/mean since it's expected that the data will be quite skewed.


Supervised way 


If you somehow have access to which matches are confirmed/overturned by humans, you could use entropy/information gain to find the best threshold. This is similar to building a decision tree of depth 1 (a decision stump), where your input is the matching score and the target is a binary variable (whether or not a human says this is a match).

So your data would be something like this:

score, is_match
0.0, 0
0.1, 0
0.2, 1
0.3, 0
0.4, 1
0.5, 0
0.6, 0
0.7, 0
0.8, 1
0.9, 1
1.0, 1




My personal recommendation would be to use the Jenks-Fisher algorithm (option 2)