High Recall but too low Precision result in imbalanced data

I was training a model using XGBoost Classifier on heavy imbalanced data base with 232:1 of binary class. Because my training data contains 750k rows and 320 features (after doing many feature engineering, feature correlation filtering and low variance filtering), I prefer to use scale_pos_weight to dealing with imbalanced rather than oversampling data. After parameter tuning using Bayesian optimization to optimize PR AUC with 5 fold cross validation, I got the best cross validation score as below:
PR AUC = 4.87%, ROC AUC = 78.5%, Precision = 1.49%, and Recall = 80.4%
and when I tried to implement the result to a testing dataset the result is below:

accuracy: 0.562
roc_auc: 0.776293
pr_auc: 0.032544
log_loss: 0.706263
F1: 0.713779
Confusion Matrix:    
[[9946 7804]
 [  18   84]]
          precision    recall  f1-score   support

       0       1.00      0.56      0.72     17750
       1       0.01      0.82      0.02       102

    accuracy                           0.56     17852
   macro avg       0.50      0.69      0.37     17852
weighted avg       0.99      0.56      0.71     17852

My parameter range to be optimize (consume 2-3 days with 100 iteration) is:

{'learning_rate':(0.001,0.2),'min_split_loss':(0,20),'max_depth':(3,10),'min_child_weight':(0,50),'max_delta_step':(0,10),'subsample':(0.5,1),'colsample_bytree':(0.5,1),'colsample_bynode':(0.5,1),'colsample_bylevel':(0.5,1),'reg_lambda':(1e-5,100),'reg_alpha':(0,1), 'objective':'binary:logistic','booster':'gbtree','scale_pos_weight':232,'n_estimators':200}

According to business request, we have more consideration to high recall (to save those in positive class), however I am frustrated by too low precision result (this is impact to the cost to save positive class). Is there any solution to increase the precision at least to be 10% without hurting the Recall?