Based on my prior knowledge, it is likely that individuals with a higher number of existing credits at the bank are less likely to receive a credit. To analyze the relationship between the feature "existing_credits" and the task of whether a person receives a credit or not, I will compare the distribution of "existing_credits" for the two target classes (yes and no) by selecting five typical existing_credits values for each target class.

Here is the dictionary with the specific details:

```json
{
	"yes": [0.0, 1.0, 2.0, 3.0, 4.0],
	"no": [1.0, 2.0, 3.0, 4.0, 5.0]
}
```

This suggests that individuals with 0 to 4 existing credits at the bank are more likely to receive a credit, while those with 1 to 5 existing credits are more likely to be rejected. These values can serve as a starting point to further explore the relationship between "existing_credits" and the target variable.