Does your data have a purpose?

Discover novel financial insights and effectively predict the likelihood an SEC filing will require amendment.

Organizations have access to more data than ever. Making sense of this data and providing actionable insights, however, can be a challenge.

Our next-generation application employs predictive analytics to drive real business outcomes from data. Combining our proprietary XBRL, Artificial Intelligence, and Machine Learning technologies, the application examines historical data and applies statistical analysis to identify patterns. Now, accountants are able to forecast the likelihood an SEC filing will require amendment.

Interested in learning more?


How do you calculate the likelihood of financial re-statement?

We use a collection of machine learning techniques that examine previous statements and determine the patterns related to subsequent amendments due to misstatement and AAERs.

What is the difference between 10K and 10Q?

10-Ks are released annually; 10-Qs are released quarterly.

How often are US public companies expected to file their returns?

Companies on the US stock market typically release three 10-Qs per year (Q1, Q2, and Q3) and one 10-K, which includes the Q4 information.

Where is this data from? And how current is it?

All data used here is publically available from the SEC. Data is collected daily.

Why can’t I see my organization?

We require a set of key financial information to make predictions. If this necessary data is not available or malformed in the XBRL filing, then that organization is omitted from our database. As we collect data on a daily basis, more organizations could be made available once we have the necessary data to make predictions.

Can the predictions change?

Yes. As we gather more data and continue refining the models, our predictions related to any given company may change. As well, given changes in the economy, laws, competition, and so on, the machine learning model predictions will need to change to reflect these.

Can I use this app to check my own financial statements?

As of today, the model is trained strictly on public US data. The patterns found may not apply to private companies or companies in other countries and the accuracy will vary. However, it can identify issues - which may not be predictive of misstatement or changes in ratios - but may nevertheless be concerns. As more data becomes available, the models can potentially be applied to private companies around the world.