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« Payment revolution needs identity revolution | Main | Whispers »

Heavy weather

By Dave Birch posted Jun 23 2009 at 10:57 AM

[Dave Birch] One of the very first projects that Consult Hyperion worked on was the Bank of England Central Gilts Office (CGO), way back in the days of "Big Bang". We were subsequently chosen by the Bank of England to work on the Central Moneymarkets Office (CMO) and then CREST, the equity settlement system that they created when the Stock Exchange's TAURUS project collapsed. These are now part of Euroclear, which makes the card scheme networks look like loose change: it currently handles an average of HALF-A-TRILLION pounds per day in gilt, equity and money market trades. At times of stress, these systems are critical to the economy.

There’s also the interesting fact that on separate occasions during September and October 2008, the UK’s Continuous Linked Settlement system broke its previous record daily volume by over 35 per cent; ‘Crest’ saw a 33 per cent increase in the highest value settled before September 2008; and ‘Swift’ saw new record volume days on four occasions. Essentially, a lot of people moving a lot of money in a very short period of time. [From FT Alphaville » Blog Archive » Financial crisis, UK payment system edition]

All of the systems stayed up, which is a testament to the designers. These systems generate a huge amount of data as a by-product of their operations and I wonder if this data is captured and used effectively? I mentioned before about using the data from payment systems to create a kind of weather map for regulators and supervisors so that they can pick out major "fronts" and see storms brewing. Perhaps the web 2.0 way forward is to anonymise the data in some way and then just put it out on the Internet so that people can see (and mash up) the financial weather for themselves: a picture of where the moving is moving through Europe in real-time might be fun. By the way, we talked about the money forecast, but here's a money map of Europe that I just love.

Just as people have realised that google searches may have a predictive value as an emergent property of their ecosystem, so there may be kinds of payment data that we don't currently exploit but it we opened it up then perhaps other people from outside the industry might find value-adding ways of reusing it in some way.

By tracking searches for terms such as 'cough', 'fever' and 'aches and pains' it claims to be able to accurately estimate where flu is circulating. Google tested the idea in nine regions of the US and found it could accurately predict flu outbreaks between seven and 14 days earlier than the federal centres for disease control and prevention.

[From Google hits to warn of flu epidemics - The Guardian - Science | Eureka! Science News]

I don't know anything about marketing, but I would have thought that (for example) a real-time map of credit card applications, colour-coded in some way, might be a useful visual for the Chancellor to glance at over his cornflakes. Google's chief economist Hal Varian said, a couple of years ago, that

For decades, many of the brightest graduates in economics sought their fortune in finance. In coming years, they will seek it in marketing, as the Internet gives all companies the information-rich environment once available only in financial markets.

[From Economics According to Google - Real Time Economics - WSJ]

This was before the crash, remember, so the intelligence and direction of Hal's statement is notable. A key impact of the transition from physical money to digital money is the availability of additional transaction data that can help to inform decision-making at all levels, not just for marketing purposes.

These opinions are my own (I think) and presented solely in my capacity as an interested member of the general public [posted with ecto]


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Great idea - open it all up, complete the process of democratising the financial markets.

Interesting. Google has shown you can predict more than flu outbreaks using search trends. A recent paper from them shows you can make significant improvements to models that predict economic indicators like car sales using search data http://sn.im/kpke5

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