[Pycon] [new paper] "Martin Ruskov" - Looking through the window of the Azerbaijani Laundromat

[info a pycon.it]

Title: Looking through the window of the Azerbaijani Laundromat
Duration: 45 (includes Q&A)
Q&A Session: 15
Language: en
Type: Talk

Abstract: 2018 will remain in history as the year in which the puzzle of populist souverainism started coming together in a big network of illicit cash flows. Having experience in crime prevention and bank operations, at the end of December 2018 I got hold of my first money laundering data set. The result is an open source project I called [CashWash][1] and this talk is about it - about my journey to explore suspicious cash flows. I will explain the motivation, technologies and uses for it, so that anyone can use and - when necessary - reproduce it.

I will explain how currently data about money laundering is siloed (encapsulated) in different stakeholders and why bringing it together is important to be able to counter illicit meddling in global politics. I will present CoinWash, an early stage python-powered platform that makes it easier to explore bank transactions and the involved organisations and individuals.

As part of this presentation, I will explain: 
1. What data sources I’ve used, what validation techniques I’ve applied for bank and corporate data.
2. What python frameworks, libraries and toolkits I’ve used for data processing.
3. How everyone can use the CoinWash platform to follow the flow of money in their own data.

I’ll demonstrate why and how Python is the ideal platform for data validation, cleansing, integration, exploration and presentation.

Attendees of the talk will learn:
- How to extract banking and corporate entities from the available datasets and validate their real identities. This is useful to anyone who wishes to match two different datasets. In reality these often refer to the same business entities in different ways.
- How to clean transactions and corporate data. Building a clean (as much as possible) foundation allows for better maintenance and integration of new data. Keeping the process reproducible allows to recompile the data once new information arrives.
- How to extract information by linking different data sources. Python is extremely powerful when it comes to transforming data in order to be able to match it to data from other sources and in other formats.
- How to follow the money. For financial and corruption investigations tracking the flow of money is essential. This is not an obvious process.

  [1]: https://github.com/mapto/CashWash

Tags: [u'data-exploration', u'finance', u'open-data']