Businesses declare bankruptcy to either eliminate or repay their debts under the guidance and protection of a bankruptcy court. As a result, businesses are either liquidated entirely reorganized. Bankruptcy data contains all public details about the legal status of a business as a result of its declared bankruptcy. This may include the business’s financial statements, debts, creditors, and more.
Bankruptcy information comes from a number of sources. The best source is, of course, a government database. Governments record accurate and reliable information but the data may not be complete. Therefore, your should also try other sources, such as one of the data vendors found on our site.
Media outlets are also good sources of this data: a business going bankrupt is become public information.
A database that records information about entities that have just filed for bankruptcy will have the company name, the date of the bankruptcy declaration, and its current status. In contrast, though, a larger database that collects bankruptcy information over a period of years will have more detail, such as the specific bankruptcy procedure. Additional information may include the scope of the debts and the details of repayment, when applicable.
Bankruptcy data dramatically affects the future of a company and all parties who are related to it. You can consult bankruptcy data before signing a business deal, for example, so you can make a fully informed decision about the risks of doing so.
You can also use the data to study the best practices for your venture. By learning from the mistakes of others, you can improve the chances of your own success.
The best way to check the quality of bankruptcy data is to consider the source. Government agencies, for example, generally provide good quality data—though they do not always provide it freely.
You should also ensure that the data is frequently and recently updated. The more up-to-date the information, the more meaningful decisions you can make.
BankruptcyData
Eurofund: Restructuring in bankruptcy: recent national case examples
Historically, bankruptcies have closely tracked the business cycle and contemporaneous unemployment rates. However, this relationship has reversed during the COVID-19 crisis thus far. While aggregate filing rates were very similar to 2019 levels prior to the severe onset of the pandemic, filings by consumers and small businesses dropped dramatically starting in mid-March, contrary to media reports and many experts’ expectations. The total number of bankruptcy filings is down by 27 percent year-over-year between January and August.
The online fitness industry consists of videos, video-based classes, personalized coaching, wearable technology, and more. An online fitness program uses data generated by users themselves to create a custom fitness coaching experience.
Online fitness has experienced a massive growth over the past year with lockdowns and is likely to stay very popular.
Insurance claims management is the process of managing a claim from reception to settlement. The insurance claim process is particularly suited to machine learning solutions as much can be done to cut time and costs, leading to speedier resolution of claims to the satisfaction of both insurer and insured.
Insurance fraud can be committed by either the buyer or the seller of an insurance policy.
The seller may offer policies from non-existent companies, fail to submit premiums, and churn policies to create more commissions. The buyer exaggerate claims, falsify medical history, post-date policies, sell their policy to for cash when they are diagnosed with a terminal disease, or fake their death or kidnapping. We will focus on the buyer insurance fraud in this post.
Historically, insurers have relied on linear regression of a small number of risk factors, largely reported by the policy-holder on a trust basis, to determine an insurance premium. However, a good prediction model of individual future insurance costs is becoming a business essential as competition in the insurance industry and low customer switching costs have become key drivers for insurers to build a pricing structure which covers their incurred costs.
Most if not all companies and organizations seek to establish themselves in social media platforms such as Facebook, Twitter, and Instagram in addition to their official website. Online and social media performance tracking is meant to analyze the effectiveness of a company’s social media presence. Are they generating new leads? Are they promoting brand awareness? These questions are important to assess the efficiency of the use of these social media platforms.
Sales forecasting is the system by which future sales volumes are estimated—at what price, during which time, and in which market. Product performance forecasting enables businesses to make informed decisions in both the short term and the long term. Machine learning software is continually improved, providing businesses with better analysis all the time—faster and more reliably than manual methods.
Promotional planning is the process of optimizing marketing tools, strategies, and resources to promote a product or service to generate demand and meet set objectives. Artificial intelligence (AI) can be used to effectively plan promotional events, measuring their outcomes, and adjusting as necessary to achieve growth.
The shelf is a dynamic environment, where shoppers select items purposefully and on impulse, where store owners showcase products to entice customers in the store and online.
Inventory management does not just entail having the right stock but also ensuring said stock was effectively sourced, stored, and sold at the right price, at the right time.
Organizations must satisfy the unique demands of their customers. The good old days of mass marketing and predictions on limited mixture of products in one location are gone. Conversely, the use of artificial intelligence in inventory generates huge dividends to companies willing to reshape their supply chain orders.
This technological development results from the availability of massive amounts of real-time data now routinely generated by enterprise software systems and smart products. By collecting this data, organizing it, and interpreting it, artificial intelligence and machine learning have introduced an entirely new level of data processing leading to deeper business insights.
Targeted marketing is a marketing strategy which identifies an audience that is most likely to buy a product or service and subsequently creates a marketing campaign designed specifically to advertise said product or service to the target audience, using advertisements and promotional messages. This allows different companies to hone in on certain market segments, creating a “specialty” and possibly lowering competition with similar companies.
Nowadays, the abundance of data and the advances in Machine Learning and big data applications reduce the need for top-down segmentation of customers. Smart customer clustering based on many commonalities help companies better address customer needs to provide the right experience and divide resources efficiently.
Credit scoring is a statistical analysis performed by lenders and financial institutions to assess a person’s creditworthiness for mortgages, credit cards, and private loans. Credit scoring is used by lenders to decide whether to extend or deny credit.
Traditionally, a person’s credit score determined by credit bureaus is a number between 300 and 850 with 850 being the highest credit rating possible. As new types of lenders and insurers emerge, however, the traditional credit score becomes just one parameter joined with a large variety of alternative data that helps determine a person’s creditworthiness.
Portfolio management is the management of investments to meet long-term financial objectives.
Today, machine learning models and external data are used in order to help companies and individuals better manage, diversify, and maintain their assets and take on less risk for higher reward.
Today, machine learning models and external data are used in order to help companies and individuals better manage, diversify, and maintain their assets and take on less risk for higher reward. Portfolio management models and use cases include any collection of investment instruments like shares, mutual funds, bonds, FDs and other cash equivalents, etc.
Many companies supply goods, loans, and services based on business and trade credit, either invoicing customers for payment at a later date or providing B2B loans. Business credit risk management assists companies with lending decisions based on a client’s financial health as well as other parameters that may indicate how likely they are to pay on time. Providing the right amount of credit will reduce the risk of late payments or defaults, which expose the vendor to financial risk.
Opta Sports provides granular, real-time data and analytics on a range of sports. This includes data on players, teams, managers, and on-field action. For Opta Sports Ice Hockey, they collect specific stats for ice hockey, like penalty minutes or a player’s plus-minus rating.
Further, while their data feeds, widgets, and other services suffice for most users, Opta also offers help from experts to help craft bespoke data solutions.
Opta Sports provides granular, real-time data and analytics on a range of sports. This includes data on players, teams, managers, and on-field action. The Opta Sports Basketball data set collects basketball-specific stats like dunks and 3-pointers
Further, while their data feeds, widgets, and other services suffice for most users, Opta also offers help from experts to help craft bespoke data solutions.
Opta Sports provides granular, real-time data and analytics on a range of sports. This includes data on players, teams, managers, and on-field action. The Opta Sports American Football data set collects American football-specific stats like touchdowns.
Further, while their data feeds, widgets, and other services suffice for most users, Opta also offers help from experts to help craft bespoke data solutions.