Machine learning compares the data set by looking at Row 1 and its columns vs Row 2 and its column data. Then, Row 3, etc. The MindBridge Ai-Auditor ingests 100% of the file. It then analyzes 100% of the file with 100% of the rest of the file. This is hard to explain as this is the background of how the MindBridge Ai-Auditor works, but it takes data from the GL and breaks them down into ‘nodes’ where the machine learning occurs.  

We use this approach in all of our financial risk assessment services and examine every transaction.

Data Set — An Example

 The example follows a simple use-case: CITY POPULATION. Imagine every dot represents 1 person in a city.

Data Set

Those circled by Green show that it’s within normality. In that, the large part of the population lives near to one another. There isn’t anything odd about where they live. That’s not to say that once you dive in you won’t find anything (there might be cases of crime, theft, etc), but from a high-level, it looks normal.

Then, Orange is starting to show that the distribution of people is starting to scatter. This is odd. Why is that? Why are they living slightly further away from the general population?

Now, those in Red are real outliers. Why are they so far from not only the general population but also from one another? What is the story behind these dots?

This is what you would consider a High-Risk %, and would need further analysis.

Think of MindBridge Ai-Auditor like that when it breaks the file down. Think of machine learning as everything a human cannot do. There is absolutely no way a human would be able to ingest 100% of the file for 100,000 Journal Entries. More so, a person would not be able to properly map out every single entry into a format that makes sense.