Best Bankruptcy Datasets, Databases & APIs
Bankruptcy data is information about bankruptcy suits filed by companies and individuals in financial legal processes. It is used by lawyers in for case research and as evidence during court proceedings. Datarade helps you find the best bankruptcy datasets and APIs.Learn more
EDI US Bankruptcy Data updated daily (500K Records)
The Ultimate Guide to Bankruptcy Data 2022
What is Bankruptcy Data?
Bankruptcy is a type of legal process which people may file to gain financial reprieve if they are unable to pay off their debts, whether in part or in full. It is also a legal process that occurs in the event that a person or business has been asked to pay damages to another party, but is unable to make such a payment. Bankruptcy data provides information on this type of legal processes, detailing dates, times, places and individuals involved. As a type of legal data, bankruptcy data is used by lawyers for court and legal proceedings.
How is Bankruptcy Data collected?
Bankruptcy data is collected just like any other type of legal data. The data exists in records, news coverage, journals and broadcast archives as well as court records. This information is then curated, collected and compiled into a dataset, and kept in a bankruptcy database by vendors. Upon request, the information in these datasets are provided to users, such as lawyers and other legal practitioners.
What are the attributes of Bankruptcy Data?
The attributes of bankruptcy data do not differ greatly from other legal data categories. The major difference in bankruptcy data is that it is more specific, detailing only court proceedings and case studies where bankruptcy as a legal process has been factored. In view of this, a bankruptcy dataset should possess information about the proceedings, such as the presiding judge, the plaintiffs and the defendants (if any), attorneys, jurisdiction involved, any rulings, the conditions in which the bankruptcy suit was filed, and any other relevant information on the court proceedings itself.
What is Bankruptcy Data used for?
Bankruptcy data is used just like any other form of legal data. The purpose of data related to legal proceedings is to provide insights on how past cases were handles, with a view to how present and future cases could be handled. Bankruptcy data is no different in this respect. Additionally, bankruptcy data is effective and useful in research, for educational or academic purposes, and for legal analytics, where scrutiny is placed on legal processes.
How can a user assess the quality of Bankruptcy Data?
Data quality assessment is an important aspect of accessing datasets and data providers. Especially as this type of data is sensitive, it is important that it is privacy-compliant. To determine the quality of a bankruptcy dataset, it’s important to cross-check the facts and figures provided against records, or to test the data sample. Another means of assessing data quality for a bankruptcy dataset would be to determine how accurate and reliable it is. You should also consider previous buyer reviews to determine vendor credibility. Should all these boxes be checked, you can be sure your bankruptcy dataset is of good quality.
Where can I buy Bankruptcy Data?
Data providers and vendors listed on Datarade sell Bankruptcy Data products and samples. Popular Bankruptcy Data products and datasets available on our platform are EDI US Bankruptcy Data updated daily (500K Records) by Exchange Data International.
How can I get Bankruptcy Data?
You can get Bankruptcy Data via a range of delivery methods - the right one for you depends on your use case. For example, historical Bankruptcy Data is usually available to download in bulk and delivered using an S3 bucket. On the other hand, if your use case is time-critical, you can buy real-time Bankruptcy Data APIs, feeds and streams to download the most up-to-date intelligence.
What are similar data types to Bankruptcy Data?
What are the most common use cases for Bankruptcy Data?
The top use cases for Bankruptcy Data are Legal Analytics.