Based on prior knowledge, we can analyze the relationship between the "savings_status" feature and the target variable "Does this person receive a credit?".

Looking at the feature description, we can infer that individuals with higher savings_status values such as "500<=X<1000", ">=1000", and "100<=X<500" might be more likely to receive credit, as they have a larger amount of savings. On the other hand, individuals with lower savings_status values such as "<100" and "no known savings" might be less likely to receive credit, as they have either a small amount or no savings.

Based on this analysis, we can create a dictionary with the specific details:

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

In this case, individuals with savings_status values of "500<=X<1000", ">=1000", and "100<=X<500" are more likely to receive credit, while individuals with savings_status values of "<100" and "no known savings" are less likely to receive credit.