Scenes from a Dive – what’s big data got to do with fighting poverty and fraud? by Prasanna Lal Das.
From the post:
A more detailed recap will follow soon but here’s a very quick hats off to the about 150 data scientists, civic hackers, visual analytics savants, poverty specialists, and fraud/anti-corruption experts that made the Big Data Exploration at Washington DC over the weekend such an eye-opener.We invite you to explore the work that the volunteers did (these are rough documents and will likely change as you read them so it’s okay to hold off if you would rather wait for a ‘final’ consolidated document). The projects that the volunteers worked on include:
- Predicting Small-Scale Poverty Measures from Night Illumination – can freely available satellite imagery, showing average nighttime illumination, serve as a reasonable poverty measurement proxy?
- Scraping Websites to Collect Consumption and Price Data – what can researchers studying poverty in countries learn from openly available crowdsourced daily price data, and by scraping price data from supermarket websites?
- Latin America Poverty Analysis from Mobile Surveys – how does the quality of data from mobile survey compare with what is collected through traditional household surveys?
- Measuring Socioeconomic Indicators in Arabic Tweets – can Twitter data help you understand socio-economic trends in countries?
- Analyzing the World Bank’s Project Data for ‘Signals’ – do successful or unsuccessful projects (or projects reporting corruption and the ones that don’t) share any characteristics?
- Analyzing World Bank Supplier Profiles – can the Bank and other agencies include publicly available data to gain a broader, more comprehensive understanding of their suppliers and use the information as proxies for risk management?
- UNDP Resource Allocation – can UNDP use staffing and program budget data to infer what skillsets mix and match the best in projects?
- Social networking analysis for risk measurement – can you forecast project risk using social networking analysis tools?
- Can you use simple heuristic auditing to sniff out discrepancies in expenditure data – what do you do when you have the information but don’t know if it contains signals about potential fraud and corruption related risk?
Here are some visualizations that some project teams built. A few photos from the event are here (thanks @neilfantom). More coming soon (and yes, videos too!). Thanks @francisgagnon for the first blog about the event. The event hashtag was #data4good (follow @datakind and @WBopenfinances for more updates on Twitter).
Great meeting and projects but I would suggest a different sort of “big data”
Requiring recipients to grant reporting access to all bank accounts where funds will be transferred and requiring the same for any entity paid out of those accounts to the point where transfers over 90 days are less than $1,000 for any entity (or related entity), would be a better start.
With the exception of the “related entity” information, banks already keep transfer of funds information as a matter of routine business. It would be “big data” that is rich in potential for spotting fraud and waste.
The reporting banks should also be required to deliver other banking records they have on the accounts where funds are transferred and other activity in those accounts.
Before crying “invasion of privacy,” remember World Bank funding is voluntary.
As is acceptance of payment from World Bank funded projects. Anyone and everyone is free to decline such funding and avoid the proposed reporting requirements.
“Big data” to track fraud and waste is already collected by the banking industry.
The question is whether we will use that “big data” to effectively track fraud and waste or wait for particularly egregious cases to come to light?