Fi-Comp Challenges.
This notebook involves proactive analysis into transaction reporting patterns. The structured example involves downloading, processing, exploring data for all reports then analysing for reporting pattern anomolies. The unstructured extension component involves the analysis of transaction reporting delay.
Jun 6, 2021
This notebook involves reactive analysis into transaction reporting patterns. The challenge is to analyse the reporting patterns related to countries newly added to the Grey List (as identified by the Financial Action Task Force). For the purposes of this analysis we will be using the grey list countries added in Feburary 2021 which were Burkina Faso, the Cayman Islands, Morocco, and Senegal. The latest list can be found on the FATF website. The structured example involves determining the higher risk Reporting Entities servicing these countries. The unstructured component of the challenge involves identifying any significant change in reporting related to these countries over the year.
Jun 6, 2021
This challenge involves proactive analysis into transaction parties, basically identifying parties that meet certain profiles of activity. Competition participants are required to replicate the example profile analysis below, which involves identifying parties associated to threshold transactions as well as large international fund transfers.
Jun 6, 2021
This challenge involves reactive analysis into transaction parties, specifically processing a reactive data matching request. Competition participants are required to replicate this analysis. The supplied list of parties is required to be submitted against the party search API to identify matching parties within the dataset. The unstrucutured extension for this analysis involves micro analysis into the top hit (by transaction reporting value) to determine this party's complete footprint in the data. Please note the source data is regularly changed thus your output will be different.
Jun 6, 2021
This notebook steps through extracting all the transaction reports from the API, then generating a network/graph of reported parties (using the networkx graph library). Once the graph is created high frequency (poor linkage characteristics are removed) and node identifiers less than a specified number of characters. The notebook then goes on to loop through the nodes contracting nodes with more than two linkages. This is the type of notebook that is required to be produced as part of ```FI-Comp``` thus coding has been removed.
Jun 6, 2021
This notebook analyzes the volume of Betashares on issue to determine public interest and judge trend in liquidity.
Mar 25, 2022
In this notebook we load up the ASIC dataset and perform some basic analysis.
Jan 3, 2020
Simple Notebook used to generate python diagrams.
Jan 1, 2020
Simple Notebook to clean notebook code cells.
Jan 1, 2020
This is an example of macro Financial Intelligence analysis, based on Australian IFTI data that was published under a Freedom of Information (FOI) request. The source data was originally published here.
Jan 1, 2020