Based on the feature description "savings_status" and the task of predicting whether a person receives credit or not, we can analyze the relationship between this feature and the target variable.

Intuitively, the savings_status may be an important indicator of the person's financial stability and ability to fulfill credit obligations. We can make some assumptions about how different savings_status categories might influence the likelihood of receiving credit:

- "no known savings": People with no known savings might be considered riskier for granting credit as they have no financial buffer.
- "<100": This category suggests individuals with savings below 100 Deutsche Mark. They might also be seen as financially vulnerable and potentially less likely to receive credit.
- "500<=X<1000": This category indicates individuals with savings between 500 and 1000 Deutsche Mark. It may suggest moderate financial stability, which could increase the chances of receiving credit.
- ">=1000": People with savings higher than or equal to 1000 Deutsche Mark might be seen as financially stable and reliable, increasing their likelihood of receiving credit.
- "100<=X<500": This category suggests individuals with savings between 100 and 500 Deutsche Mark. They might have some financial stability, but not as strong as those in the ">=1000" category.

Based on this analysis, we can create the corresponding dictionary:

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

Note that we have included the savings_status categories that are more likely to impact the target variable and have excluded those that are hard to predict.