After analyzing the relationship between the feature "savings_status" and the task of credit approval, I can create a dictionary presenting the possible values of "savings_status" for both the "yes" and "no" classes. Here is the dictionary:

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

Based on the analysis, it seems that individuals with "no known savings" or savings ">=1000" are more likely to receive credit (class "yes"). On the other hand, individuals with savings "<100", "500<=X<1000", or "100<=X<500" are more likely to be denied credit (class "no").