is it possible to decide model without any data?
This is not a very strange situation in real world companies who nowadays want to build data science applications and other data related stuff, but without enough historical data (or none at all). 
In this case, defining what a model is might help you, so:

are you/your superior considering only machine learning models? In that case, you need the data to train with
are you also considering a less sophisticated approach like rules-based models first? In that case, you can generate (i.e. programm it) such rules based on business knowledge before going directly into the machine learning pipeline, for which you need data

Another option, which I used once to check some ideas in advance (before having data) is to simulate some data based on the known data distributions which you know you might have in a near future; for instance, you might want to simulate clients ages, clientes accounts amounts... from other banks stored in some open-data platform. 
In this case, you can model your data with for instance a kernel density estimator, to generate afterwards some synthetic samples. Below you can find what I onced made with a similar situation, where the orange bars are the open data retrieved with variables similar to what I would eventually have in my company (in this case I needed ages for each marital status, and I found it for a bank of a similar country) and used to generate the kernel density data generator (blue line):