Webclass 33: Data Analytics Decision Tree - could it be semi-automatic?

Q1. What three reasons does Claire give for auditors not using data analytics?

Knowledge gap

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Trust

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Confusion

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Technical skills

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Q2. How could we quickly close the knowledge gap and get everyone on the same page?

Use e-learning to cover all main aspects about accounting, risks, data, how to use dashboards, etc.

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Get your auditors to learn python

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Get your auditors to learn SQL

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Q3. What should we reconcile the general ledger to?

The trial balance

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The operational data in the company (sales orders, purchase orders, etc.)

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The list of suppliers

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The list of customers

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Q4. How can you build trust with the auditors that the data analytics dashboards are correct?

Show the auditors that we have done the reconciliation to the financial statements

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Remind the auditors on all dashboards what the total values were

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Reconcile all parts of the entire business process together

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Train the auditors on how to code the dashboard

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Q5. Why might invoices tend to be higher in value than the purchase orders and goods receipts?

Because we can have invoices for things such as tax, utilities, rental, for which we don't do a purchase order

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Because of fraud in the company

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Q6. How can a decision tree help the auditors to not be confused?

The decision tree can guide the auditor when they are reviewing the dashboard

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Helps the auditors to feel a little bit more relaxed when faced with a complicated dashboard

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Helps the auditors to not miss something that is critical

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Helps the auditor to ask relevant questions of the dashboard

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The decision tree will give the answer to the auditor so that they do not need to spend too much time reviewing the dashboard

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Q7. What simple tool could we use instead of Excel in order to guide auditors through the dashboard with our decision tree?

A chatbot

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Artificial intelligence

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Q8. How could an Isolation Forest help us?

It helps us to easily identify combinations in the data that are unusual compared to the rest of the data

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For the "outliers" identified, we can go into the isolation forest and then review the tree so that we can then assess if we think that the unusual case is a possible fraud scenario or not

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The auditors might not be able to see all of the outliers in the results sets and data sets easily, the isolation forest helps to see "anomalies" or "unusual patterns"… that could be fraudulent, because of their unusual nature

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The isolation forest will pinpoint the fraud automatically

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Q9. What data would you INPUT to the isolation forest?

All of the results from all of the data analytics tests (all of the indicators) and maybe even some of the raw data

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Individual indicators one-by-one

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Q10. How could isolation forest help with storytelling with data?

The "outliers" identified, that are considered by the auditor to be potential fraud cases, and that are then reviewed to be actual fraud cases, can then be converted into a decision tree visualization automatically, that can then be used in an auditor's report to show the thought process by which the "outlier" was identified.

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The isolation forest will confirm for the auditor which outliers are fraudulent cases

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WC033 – Data analytics decision tree – could it be semi-automatic?

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Participants 13

Webclass 33: Data Analytics Decision Tree – could it be semi-automatic?

admin June 9, 2026

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