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Imagine an entity with a money fund. This entity defines some budgets which it annually distributes to different applicants.

Example data set:

| party | year | funding, $K | 
| A     | 2018 | 0           |
| B     | 2018 | 10          |

So in the two records we see that in 2018 applicant A got no funding and applicant B got $10K funding.

How would you search for possible wrongdoings in such a data set?

Are there any specific/advanced algorithms to do so?

I am asking to learn whether there is more to it than just the simple heuristics like "are there entities getting significant portions of funding all the time".

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  • $\begingroup$ "wrongdoings in an according data set" -- do you mean accounting? $\endgroup$ – amdopt Aug 29 '19 at 11:45
  • $\begingroup$ no just statistical distribution of funding between entities over time. $\endgroup$ – J. Doe Aug 29 '19 at 12:01
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You could start with the law of anomalous numbers: Benford's Law. I'm not sure if it is the exact answer you are looking for, though it has been used in many forms to detect anomalies within financial data. It could at least be a starting point. Many number sequences even ones that appear random follow Benford's. One's that are manipulated by humans tend to diverge from it, making the tool potentially useful for cases like the one you are describing. The Wikipedia link above has a decent description of the types of data sets that are known to adhere to the law as well as ones that violate it.

Other References:

A New Tool to Detect Financial Reporting Irregularities

Financial Statement Errors: Evidence from the Distributional Properties of Financial Statement Numbers

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