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Recently, there have been several automations percolating around the Banking Sector. Banks use several automation tools to fasten their banking jobs which makes them cumbersome if not used. Vehicle Insurance Policy is a huge banking section which involves direct involvement of bank employees for vehicle damage verification and customer authentication. Insurance policies do not cover all sorts of vehicle damage, hence the type of damage incurred to the vehicle has to be verified. This process can be automated by Image Recognition using convolutional neural networks (CNNs) which has the capability to detect the type of vehicle damage (bumper dent, tail lamp dent, door scratchetc.,). The required input is an Image of the damaged part of the vehicle. In this paper, several hybrid CNN models are established to show the changes evolved in the accuracy metrics. Experimental results show that transfer learning technique performs a great deep feature learning on the present limited dataset. [ABSTRACT FROM AUTHOR]