Критерии создания регламента проверки контрагентов
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Federal state institution IK-4 Ufsin of the Russian Federation on of the Penza region
OGRN: 1025801104823
TIN: 5834014747
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OKVED2
84.23.4
Activities for management and exploitation of prisons, corrective colonies and other places of imprisonment, also for rendering rehabilitation help to former prisoners
Monitoring
Notes
Reports
Contact information
Revenue
No data
Chief of institution
Knyazev Andrei Veniaminovich
81
Reliability Score
Chief of institution
Knyazev Andrei Veniaminovich
28
years
Established
May 29, 1996
City Penza
Revenue
No data
580
Connections
+1
last week
Employees
No data
Stats Over Time
| Participation | Total number of cases | For the last year |
|---|---|---|
|
✖
Plaintiff
|
23 cases (▒▒▒▒▒ ₽) | 5 cases (▒▒▒▒▒ ₽) |
|
✖
Defendant
|
30 cases (▒▒▒▒▒ ₽) | 1 case (▒▒▒▒▒ ₽) |
|
✖
Third party
|
8 cases (▒▒▒▒▒ ₽) | 1 case (▒▒▒▒▒ ₽) |
|
✖
Other party
|
3 cases (▒▒▒▒▒ ₽) | 0 cases (▒▒▒▒▒ ₽) |
Positive outcome — ▒▒%
▒▒▒▒▒₽
- 11 cases (▒▒▒▒▒ ₽) won
- 1 case (▒▒▒▒▒ ₽) partially won
- 4 cases (▒▒▒▒▒ ₽) not lost
Negative outcome — ▒▒%
▒▒▒▒▒₽
- 13 cases (▒▒▒▒▒ ₽) lost
- 2 cases (▒▒▒▒▒ ₽) partially lost
- 3 cases (▒▒▒▒▒ ₽) not won
Type of dispute
%
Administrative
%
Civil
%
Bankruptcy-related
%
Other
Current cases
As plaintiff (3)
Claims amount: ▒▒▒▒▒ ₽
Date/Number
Complexity
Object
Plaintiff
Defendant
Instance
Claim amount
As third party (1)
Claims amount: ▒▒▒▒▒ ₽
Date/Number
Complexity
Object
Plaintiff
Defendant
Instance
Claim amount
Completed cases
As plaintiff (20)
Claims amount: ▒▒▒▒▒ ₽
Date/Number
Complexity
Object
Plaintiff
Defendant
Instance
Claim amount
As defendant (30)
Claims amount: ▒▒▒▒▒ ₽
Date/Number
Complexity
Object
Plaintiff
Defendant
Instance
Claim amount
As third party (7)
Claims amount: ▒▒▒▒▒ ₽
Date/Number
Complexity
Object
Plaintiff
Defendant
Instance
Claim amount
As other party (3)
Claims amount: ▒▒▒▒▒ ₽
Date/Number
Complexity
Object
Plaintiff
Defendant
Instance
Claim amount
Data sources
Information about company arbitration cases is collected from public sources.
Accurate binding to a company can be created only if the information source contains its TIN and OGRN values. If identification details are not specified, the binding is created by using linguistic algorithms. Although these algorithms are being constantly improved, some information may be absent.
If you have noticed an error or lack of information, please let us know about it. Let's improve Seldon.Basis together!
Data sources
Information about company arbitration cases is collected from public sources.
Accurate binding to a company can be created only if the information source contains its TIN and OGRN values. If identification details are not specified, the binding is created by using linguistic algorithms. Although these algorithms are being constantly improved, some information may be absent.
If you have noticed an error or lack of information, please let us know about it. Let's improve Seldon.Basis together!
