Oso Mudslides and BigData

There was much todo about google’s bigdata bad flu forecasts recently in the news. google had tried to forecast flu rates in the US based on search data. That’s a hard issue  to forecast well but doing better will have public benefits by giving public officials and others information to identify pro-active actions.

Lets also think about other places where bigdata, in a non-corporate, non-figure-out-what-customers-will-buy-next way, could also help.

Let’s think about Oso, Washington (Oso landslide area on google maps)

Given my background in geophysics (and a bit of geology), you can look at Oslo, Washington and think…yeah…that was a candidate for a mudslide. Using google earth, its easy to look at the pictures and see the line in the forest where the earth has given way over the years. It looks like the geology of the area is mostly sand and it was mentioned it was glacier related. All this makes sense.

We also know that homeowner’s insurance tries to estimate the risk of a policy before its issued and its safe to assume that the policies either did not cover mudslides or catastrophes of this nature for exactly this reason.

All of this is good hind-sight. How do we do better?

Its pretty clear from the aerial photography that the land across the river was ripe for a slide. The think sandy line, the sparse vegetation and other visual aspects from google earth/maps shows that detail. Its a classic geological situation. I’ll also bet the lithography of the area is sand, alot of sand, and more sand possible on top of hard rock at the base.

So lets propose that bigdata should help give homeowners a risk assessment of their house which they can monitor over time and use to evaluate the potential devastation that could come from a future house purchase. Insurance costs alone should not prevent homeowners from assessing their risks. Even “alerts” from local government officials sometimes fall on deaf ears.

Here’s the setup:

  • Use google earth maps to interpret the images along rivers, lakes and ocean fronts
  • Use geological studies. Its little known that universities and the government have conducted extensive studies in most areas of the US and we could, in theory, make that information more accessible and usable
  • Use aerial photography analysis to evaluate vegetation density and surface features
  • Use land data to understand the terrain e.g. gradients and funnels
  • Align the data with fault lines, historical analysis of events and other factors.
  • Calculate risk scores for each home or identify homes in an area of heightened risk.

Do this and repeat monthly for every home in the US at risk and create a report for homeowners to read.

Now that would be bigdata in action!

This is a really hard problem to solve but if the bigdata “industry” wants to prove that its good at data fusion on a really hard problem that mixes an extremely complex and large amount of disparate data and has public benefit, this would be it.

Yanukovych, Money Laundering and a Probe: The Rise of Network Analytics

I have been working in the Anti-Money Laundering (AML) for awhile. Compared to healthcare or the more general Customer Relationship Management (CRM) space, the AML and Bank Secrecy Act (BSA) is really the “shady” side of the customer–or at least it assumes that some customer are a shady and tries to find them or prevent their actions. Some estimates suggest that the aggregate impact of BSA/AML (and Fraud) regulations is only 10-20% of the total amount of dollar flow in the world so we know that while regulators and prosecutors do catch some of the bad guys alot of dollars remain on the table.

Take the recent case of the Ukraine. It’s been reported that the Swiss are launching a money-laundering probe into ousted president Viktor Yanukovich and his son Oleksander. They think the money laundering could amount to tens of billions. All told, over 20 Ukrainians are listed as targets of the Swiss probe.

In BSA/AML, Yanukovichs (father and son) is a clearly a Politically Exposed Person (PEP). And apparently the son had a company established that was doing quite well. That information usually leads to flags that up the risk score of a customer at a bank. So an investigation and PEP indicators are all good things.

Officials estimate that $70 billion disappeared from the government almost overnight. Of course, Yanukovich WAS the president of Ukraine and he was on the run up until last week. But an investigation into money laundering on tens of billions that suddenly just happened?

Recently, I attended an ACAMs event in NYC. Both Benjamin Lawsky (regulator side) and Preet Bharara (prosecution side) spoke. One of their comments was that to have a real impact on money-laundering, you have to create disincentives so that people do not break the law in the future. You can sue companies, people and levy fines. These create disincentives and disincentives are the only scalable way to reduce money-laundering–stop it before it starts. The ACAMs event was US based, but the ideas are valid everywhere. The Swiss have always had issues with shielding bad people’s money but they are playing better than before.

But the real issue is that the conduits, the pathways, were already setup to make this happen. And most likely, there have been many dollars siphoned off with the list $70 billion being the end of the train. So the focus needs to be on active monitoring of the conduits and the pathways, with the BSA/AML components being one part of monitoring those paths. After all, the BSA/AML regulations motivate a relatively narrow view of the “network” with an organization’s boundaries.

If we want to really crack down on the large scale movement of funds, it will not be enough to have the financial institutions–which have limited views into corporations–use traditional BSA/AML and Fraud techniques. A layer of network analysis is needed at the cross-bank level that goes beyond filing a suspicious activity report (SAR) or a currency transaction report (CTR). And this network analytical layer needs to be intensely and actively monitored at all times and not just during periods of prosecution. While the Fed uses the data sent back from a company’s SAR and CTR (and other reports) and in theory acts at the larger network level, it is not clear that such limited sampling can produce a cohesive view. Today, social media companies (like Facebook) and shopping sites (like Amazon) collect an amazing amount of information at a detailed level. NSA tried to collect just phone metadata and was pounced on. So the information available in the commercial world is vast, that which the government receives is tiny.

In other words, the beginnings of an analytical network is clearly present in the current regulations, but the intensity and breadth of the activity needs to match the scale of the problem so that the disincentives dramatically increase. And while it is very difficult to make this happen across borders or even politically within the US, its pretty clear that until the “network analysis” scale either increases its “resolution” or another solution is found, large scale money laundering will continue to thrive and most enforcement efforts will continually lag.

Its a balancing act. Too much ongoing monitoring is both political anathema to some in the US and it can be very costly. Too little and the level of disincentives may not deter future crimes.

Pop!…another $70 billion just disappeared.