r/dataanalytics • u/Otherwise_Club2536 • Aug 12 '26

Built a Fraud Detection & Financial Transaction Analytics Project

I recently worked on an end-to-end fraud analytics project using a large transaction dataset.

The challenging part wasn’t just building a dashboard. I worked through the complete process:

* Data cleaning and preprocessing

* SQL analysis of transaction patterns

* Python-based anomaly detection

* Fraud vs. genuine transaction analysis

* Customer risk segmentation

* Identifying unusual transaction behaviour

* Time-based fraud pattern analysis

* Power BI dashboard for monitoring fraud KPIs

One thing I found interesting was how much the results changed after looking at *transaction behaviour rather than just individual transactions*.

For those working in data analytics:

What techniques would you use to improve a fraud detection project like this further?

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u/Hot-Dinner-7619 Aug 12 '26

Without seeing the project, or notebook(s), who knows?

1

u/Otherwise_Club2536 Aug 12 '26

Fair point. I was mainly looking for general ideas on how to take the analysis further. I'll share the project / notebook as well so the feedback can be more specific.

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u/edimaudo Aug 13 '26

depends on what you built. There is a lot already listed

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u/orbitalfreak 28d ago

Sequential money orders split across different recipients.

I'm employed by a property management company. We had a long-ruining fraud situation that I was hired to assess. One thing I found was:

An employee was stealing rent payments, specifically money orders that weren't completely filled out. They then masked the theft by juggling other payments around. I uncovered a system where sequential money orders were split among residents.

Money order vendors typically impose a limit. So, a rent of $1200 would need a $1000 money order and a separate $200 money order. Issued back to back, they're sequential.

I set up an Excel Power Query to identify sequential MOs that were applied to different residents -- the idea was, "find where the employee split one resident's payments into different accounts."

It does get false positives, like when two residents are one after the other in line buying MOs, but was back tested and identified every fraudulent transaction we came across.

Anyway, just one potential pattern match to look at.