diff --git a/fieldExtraction/src/investment/one_to_one_funcs.py b/fieldExtraction/src/investment/one_to_one_funcs.py
index 0113d3c..e363869 100644
--- a/fieldExtraction/src/investment/one_to_one_funcs.py
+++ b/fieldExtraction/src/investment/one_to_one_funcs.py
@@ -19,6 +19,11 @@ def run_regex_fields(one_to_one_fields: FieldSet,
 
     # One to One TIN Fields 
     tin_answers = get_tin_fields(filename, text_dict, top_sheet_dict)
+    if tin_answers['PROV_GROUP_TIN_SIGNATORY'] != 'N/A':
+        if tin_answers['PROV_TIN_OTHER'] != 'N/A':
+            tin_answers['PROV_TIN_OTHER'] = tin_answers['PROV_GROUP_TIN_SIGNATORY'] + ', ' + tin_answers['PROV_TIN_OTHER']
+        else:
+            tin_answers['PROV_TIN_OTHER'] = tin_answers['PROV_GROUP_TIN_SIGNATORY']
     tin_answers = {k:v for k, v in tin_answers.items() if k in ['PROV_GROUP_TIN', 'PROV_TIN_OTHER']}
     tin_answers['PROV_GROUP_TIN'] = str(tin_answers['PROV_GROUP_TIN']).replace("-","")
     tin_answers['PROV_TIN_OTHER'] = str(tin_answers['PROV_TIN_OTHER']).replace("-","")
diff --git a/fieldExtraction/src/postprocessing_funcs.py b/fieldExtraction/src/postprocessing_funcs.py
index d3d5998..b57a564 100644
--- a/fieldExtraction/src/postprocessing_funcs.py
+++ b/fieldExtraction/src/postprocessing_funcs.py
@@ -752,10 +752,14 @@ def clean_tin_npi_other(final_df: pd.DataFrame) -> pd.DataFrame:
         pd.DataFrame: The cleaned DataFrame with square brackets removed from the specified columns.
     """
     if "PROV_TIN_OTHER" in final_df and "PROV_GROUP_TIN" in final_df:
-        final_df["PROV_TIN_OTHER"] = final_df.apply(lambda row: row.PROV_TIN_OTHER.replace(row.PROV_GROUP_TIN+', ','').replace(row.PROV_GROUP_TIN,''), axis=1)
+        # if PROV_GROUP_TIN is still N/A, pick first value from tin_other
+        final_df["PROV_GROUP_TIN"] = final_df.apply(lambda row: row.PROV_TIN_OTHER[:9] if row.PROV_GROUP_TIN == 'N/A' and len(row.PROV_TIN_OTHER) >= 9 else row.PROV_GROUP_TIN, axis=1)
+        final_df["PROV_TIN_OTHER"] = final_df.apply(lambda row: row.PROV_TIN_OTHER.replace(row.PROV_GROUP_TIN+', ','').replace(', '+row.PROV_GROUP_TIN,'').replace(row.PROV_GROUP_TIN,''), axis=1)
 
     if "PROV_NPI_OTHER" in final_df and "PROV_GROUP_NPI" in final_df:
-        final_df["PROV_NPI_OTHER"] = final_df.apply(lambda row: row.PROV_NPI_OTHER.replace(row.PROV_GROUP_NPI+', ','').replace(row.PROV_GROUP_NPI,''), axis=1)
+        # if PROV_GROUP_NPI is still N/A, pick first value from npi_other
+        final_df["PROV_GROUP_NPI"] = final_df.apply(lambda row: row.PROV_NPI_OTHER[:10] if row.PROV_GROUP_NPI == 'N/A' and len(row.PROV_NPI_OTHER) >= 10 else row.PROV_GROUP_NPI, axis=1)
+        final_df["PROV_NPI_OTHER"] = final_df.apply(lambda row: row.PROV_NPI_OTHER.replace(row.PROV_GROUP_NPI+', ','').replace(', '+row.PROV_GROUP_NPI,'').replace(row.PROV_GROUP_NPI,''), axis=1)
 
     if "PROV_TIN_OTHER" in final_df:
         final_df["PROV_TIN_OTHER"] = (