After analyzing the relationship between the feature "savings_status" and the task "Does this person receive a credit?" (Yes or no), we can create a dictionary to summarize the possible values of the feature for each target class.

Here is the dictionary:

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

In this case, individuals who have savings status in the categories "500<=X<1000", ">=1000", or "100<=X<500" are more likely to receive credit (target class "yes"). On the other hand, individuals who have savings status in the categories "no known savings" or "<100" are more likely not to receive credit (target class "no").

Note: The other category, "no savings known", is not included in the dictionary as it is hard to predict its relationship with the target class.