It seems like you've provided two sets of data related to loan application processes:

The first set includes instances where loan applications result in various outcomes, each with associated frequency counts and performance metrics:

1. Approval process leading up to loan signing (`frequency =` numbers ranging from thousands to hundreds)
2. Application denial (`frequency = `numbers ranging from tens to twenties%)

In contrast, the second set appears to detail specific processes involved irrespective of outcome (approved or denied). It includes steps like scheduling appointments (`request appointment -> set appointment`) followed by credit assessment (`hand in credit application`), verification (`verify borrower's information`) and various additional checks such as collateral evaluation (`make visit to assess collateral`). Some instances result in loan approval (`sign loan agreement`) while others do not (`loan denied`). The performance metrics vary widely suggesting a range of performance values.

The key differences:

- **Frequency counts:** First data set is more about counting occurrences relative to outcomes whereas second data set details steps involved irrespective outcome.
  
- **Outcome focus:** In the first case, outcome (approval or denial) is what matters while the second focuses on detailed process steps regardless of outcome specifics.
  
- **Additional detail:** The second set includes specific procedural steps that are part-of-process for both approved cases (`sign loan agreement`) and denied cases (`loan denied`). This contrasts with focusing purely on outcomes in first data set.

In a nutshell:

First data emphasizes the performance metrics based on the end outcome of various loan applications grouped into two categories: approval and denial. Second data details the specific processes involved irrespective of whether they lead to approval or denial