diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst
index b71d294b97f9a..92a99098db05c 100644
--- a/doc/source/whatsnew/v1.5.0.rst
+++ b/doc/source/whatsnew/v1.5.0.rst
@@ -1075,6 +1075,7 @@ Groupby/resample/rolling
 - Bug in :meth:`.GroupBy.cummin` and :meth:`.GroupBy.cummax` with nullable dtypes incorrectly altering the original data in place (:issue:`46220`)
 - Bug in :meth:`DataFrame.groupby` raising error when ``None`` is in first level of :class:`MultiIndex` (:issue:`47348`)
 - Bug in :meth:`.GroupBy.cummax` with ``int64`` dtype with leading value being the smallest possible int64 (:issue:`46382`)
+- Bug in :meth:`GroupBy.cumprod` ``NaN`` influences calculation in different columns with ``skipna=False`` (:issue:`48064`)
 - Bug in :meth:`.GroupBy.max` with empty groups and ``uint64`` dtype incorrectly raising ``RuntimeError`` (:issue:`46408`)
 - Bug in :meth:`.GroupBy.apply` would fail when ``func`` was a string and args or kwargs were supplied (:issue:`46479`)
 - Bug in :meth:`SeriesGroupBy.apply` would incorrectly name its result when there was a unique group (:issue:`46369`)
diff --git a/pandas/_libs/groupby.pyx b/pandas/_libs/groupby.pyx
index 6e2b79a320dd7..e33733ed6d14f 100644
--- a/pandas/_libs/groupby.pyx
+++ b/pandas/_libs/groupby.pyx
@@ -204,7 +204,6 @@ def group_cumprod_float64(
                     out[i, j] = NaN
                     if not skipna:
                         accum[lab, j] = NaN
-                        break
 
 
 @cython.boundscheck(False)
diff --git a/pandas/tests/groupby/test_function.py b/pandas/tests/groupby/test_function.py
index 93e9b5bb776ab..04c364fab63cf 100644
--- a/pandas/tests/groupby/test_function.py
+++ b/pandas/tests/groupby/test_function.py
@@ -650,6 +650,20 @@ def test_groupby_cumprod():
     tm.assert_series_equal(actual, expected)
 
 
+def test_groupby_cumprod_nan_influences_other_columns():
+    # GH#48064
+    df = DataFrame(
+        {
+            "a": 1,
+            "b": [1, np.nan, 2],
+            "c": [1, 2, 3.0],
+        }
+    )
+    result = df.groupby("a").cumprod(numeric_only=True, skipna=False)
+    expected = DataFrame({"b": [1, np.nan, np.nan], "c": [1, 2, 6.0]})
+    tm.assert_frame_equal(result, expected)
+
+
 def scipy_sem(*args, **kwargs):
     from scipy.stats import sem