The Speed of Trust

It’s not easy being a professional poker player. On the plus side, admittedly there is an unparalleled amount of autonomy and an absence of accountability: no boss, no clients, no shareholders to report to. On the other hand, it’s one of the few jobs where you can do everything right and come home down overall on the day. It’s a world in which you can win at the table only to be mugged on the way back to your hotel room with your winnings in your money belt. The risks extend way beyond those which you can logically calculate in a 52 card deck. No wonder it was once described as a hard way to make an easy living!

The most trying tribulations are often the little things that you wouldn’t necessarily think about – like what you put as your profession on your passport if you want to go on holiday to Dubai. Getting car insurance is difficult, income replacement is obviously impossible! This week, as if all that were not enough, I was refused a bank account.

The meeting was embarrassing and awkward in the way that we’re all used to in the call centre economy of 2012. Obviously the person I was talking to was not to blame. He was not the decision maker in the way that he would have been 20 years ago. He just typed my details into a computer and it dutifully said “no”.

With the exponentially increasing processing power of computers, such impersonal and data-based decisions make perfect sense. On the aggregate, they trump intuition and, of course, they’re cheaper to churn out. The behavioural biases of human judgement are replaced by the cold hard calculations of an algorithm which does not demand sick-pay or annual leave.

Somewhere, a computer processed my credit score – a function of my living arrangements and financial repayments in the last 6 years – and reckoned that there was a high probability that I would run up an overdraft and abscond. It doesn’t know who I am or what I’ll actually do any more than Amazon knows what I want to read when it recommends books that I might enjoy. But – like Amazon – it compared the data it has on me with lots of people like me and made a probability assessment which, in my case, was not good.

The link between such an assessment and the world of gambling is, historically, a close one. In the 16th Century, renaissance Italian mathematician, physician and compulsive gambler Geronimo Cardano laid the foundations of the language of probability in a work which gave more attention to his recommendations for ways to cheat at games of chance than it did to the new and revolutionary branch of mathematics that he’d produced. More famously, the creation of expectation theory was Fermat and Pascal’s solution to the Problem of the Points, posed to them by another gambling addict, in this case their friend the Chevalier de Méré.

It’s hard to overstate the importance of such works. In the creation and application of the language of probability, Cardano, Fermat and Pascal along with numerous others in the years that followed (Laplace, Huygens, Bernoulli) laid the foundations for the industrial development of the instruments of modern finance that have profoundly shaped the world in which we live. It is a long time since their formulations found their apotheosis in the world of high finance rather than the lowest of all low culture. So what on earth does the skillset of a professional poker player have to bring to the world of credit assessment which so thwarted me this month?

Ultimately of course, in a hand of poker, I’m trying to do exactly the same job as the bank manager or the algorithm that has replaced him: I’m trying to assess the honesty of the person sitting opposite me. It’s significant of course that the word credit itself derives from the Latin credo, meaning trust, and poker is a rare – perhaps unique – activity in that it legalises lying, so assessments of honesty are fundamental to success. In this way though, poker just distils a process of probability assessment in which we’re engaged with each and every inter-personal encounter: does this person threaten us as we encounter them in the street; do we trust that they’ll pay us back if we lend them money?

As with most assessments that we make, the majority of the time we do it subconsciously: we match the patterns of a person’s dress, behaviour and facial movements with our database of information built up over the course of a lifetime – in the same way that Amazon matches our purchasing records with similar customers in order to assess what we’re likely to buy.

Sometimes, though, even Amazon gets it wrong and so do we. The other day I thought a guy was going to mug me on the way to my hotel room, only to find that he was running towards me to return my phone that I had dropped at the poker table! It all happened very quickly and I based my first assessment on a limited amount of information. As more “data” became available I revised my initial assessment and was more accurate as a result.

As accurate as my bank’s algorithm is in the aggregate, in my case it also got it wrong. Far from posing a credit risk, I have no desire to take on any kind of debt and every intention of depositing a six figure sum to open the account! I tried explaining this but the man on the end of the line had no ability to converse with a computer than could not take on new information.

While we call this age of big data, the amount of information contained in my credit score is actually a tiny proportion of the whole. This is not just personally frustrating for me, it is affecting the profits of companies who are relying on such algorithm assessments for their income. Sure, they’re better than the assessments made by the relatively low pay-grade personnel who inhabited these roles 20 years ago. But they can be better still – and they will be. The question is who is going to steal a march on the competition and really open up this kind of assessment to a revolution by using the kind of granular and detailed information about me that exists out there in cyberspace.

In a world of scarcity, decisions must be made that maximise ROI and reduce risk and some people are definitely better “bets” than others. But turning me – and others who exist outside the norms of a system – down is like folding to a bluff. It’s poor play and players who can exploit this inefficiency can make a lot of money.

Posted 01:44pm by Caspar and filed in Decision Making, Risk