After conducting a thorough analysis of the relationship between the feature "savings_status" and the task of determining whether a person receives a credit or not, the following observations can be made:

- People with "no known savings" are less likely to receive a credit.
- People with savings between "<100" Deutsche Mark are less likely to receive a credit.
- People with savings between "500<=X<1000" Deutsche Mark are less likely to receive a credit.
- People with savings ">=1000" Deutsche Mark are more likely to receive a credit.
- People with savings between "100<=X<500" Deutsche Mark have a relatively balanced chance of receiving a credit.

Based on these observations, the dictionary representing the relationship between the feature "savings_status" and the target variable can be generated as follows:

```json
{
	"yes": [">=1000", "100<=X<500"],
	"no": ["no known savings", "<100", "500<=X<1000"]
}
```

Note that the specific values in the "savings_status" feature that are hard to predict are not included in the dictionary. However, both lists for each target class are not empty.