In this talk proposal, we will discuss the chain of fraudulent transactions and help the investigation agencies to fight money laundering with the help of Python programming language and packages.
The working of the proposed solution is described below
Step 1: The investigation officer obtains data of suspicious accounts across banks.
Step 2: Using Benford’s Law the accounts data will be checked for possible fraud and marked for further analytics.
Step 3: The account details will also be matched with Politically Exposed Persons(PEP), Relatives and Close Associates (RCA), and Sanctions Data. If a match is found then it increases the probability of possible money laundering.
Step 4: Generate graphs showing the links between transactions of different bank accounts for step 2 and step 3.
Step 5: Apply Graph Machine Learning techniques and graph algorithms to identify the fraudulent chains between depositor and receiver accounts.
Step 6: Find a correlation between transactions and bank accounts to form a fraudulent chain.
Step 7: Generate results in the form of reports and interactive visualizations
Step 8: Verify the result for genuineness and false positive rate.
Step 9: Keep track of all the activities and tasks executed from steps 2 through 8.
Step 10: Generate a report for step 9 in a human-readable and understandable form.

The application has been developed using Scipy, numpy, pandas, matplotlib, NetworkX, Altair, scikit-learn, and Dash packages.

The participants will learn about a new use case of python in crime investigation.