[P] Issue with Fraud detection Pipeline

Hello everyone im currently doing an internship as an ML intern and I'm working on fraud detection with 100ms inference time. The issue I'm facing is that the class imbalance in the data is causing issues with precision and recall. My class imbalance is as follows:

Is Fraudulent 0 1119291 1 59070 

I have done feature engineering on my dataset and i have a total of 51 features. There are no null values and i have removed the outliers. To handle class imbalance I have tried versions of SMOTE , mixed architecture of various under samplers and over samplers. I have implemented TabGAN and WGAN with gradient penalty to generate synthetic data and trained multiple models such as XGBoost, LightGBM, and a Voting classifier too but the issue persists. I am thinking of implementing a genetic algorithm to generate some more accurate samples but that is taking too much of time. I even tried duplicating the minority data 3 times and the recall was 56% and precision was 36%.
Can anyone guide me to handle this issue?
Any advice would be appreciated !