We're meeting for lunch at a restaurant in Canary Wharf, where many of the major global banks are located. He is a man in his late 40s, inconspicuously dressed, and in possession of a firm handshake. He orders a Coke, and then a pasta dish he will dig in with great relish. In his volunteer email he said he was with a software firm (working in investment banking). When asked for a job description, he simply says he is a "quant".

"My parents discovered that I was of a mathematical bent aged three when I was apparently lining up my toys in order of size and then colour. I was one of these terrible, precocious kids who did their mathematics O-level aged 12. After a long academic career I ended up doing theoretical physics for my PhD, and spent a couple of years at Cern in Geneva. Many people I know from back then are still at universities, doing research and climbing the slippery slope to professorships and fellowships. They work the same astonishing long hours as I do, yet get paid a fraction and, from a purely scientific perspective, get to do some really, really interesting science. I often say (only half jokingly) that I "sold my soul" – I make a little over £200,000 a year, including my bonus.

"I am in a world of data, and I build all sorts of models for banks. For instance, one that helps a bank decide whom to lend a mortgage to. You have all this data about the person who is applying, and then the model works out the risk of lending to that person. You look at both the probability of this happening, and at the size of the loss in such an event.

"No model is perfect and before the current crisis banks might allow for one in five decisions in residential mortgages to be potentially wrong, for example. In the crisis many got burned and the true extent of the losses that will be suffered by the banks will only become clear in perhaps five or 10 years' time, and now they might want the failure rate to be only one in 25. So you need to adjust the acceptance level of the model (same model, same predictors – irrespective of the economics) – but a risk that you might take on the margins before, you wouldn't take now – hence the relative closure of the residential mortgage market.

"By the way, you should always have people doing random checks on the computer models decisions. One should never leave a self-learning algorithm to make decisions on its own. This isn't a worry about the algorithm becoming a character from The Terminator – a model simply needs constant testing; in fact, this testing goes on throughout the model's life.

"This week I am working on a specific fraud detection model. The key element here is the sheer volume of data. When you pay something with a plastic card – either credit or debit – a whole range of data is created. In statistical terms each of these data items is considered an 'observation', for instance, who you paid, when and from where, whether the transaction was swiped through a machine or was a chip and pin transaction (and whether you got the pin right!), the amount, the currency, the exchange rate, the type of business the merchant is in…

"The model I'm doing this week, I've got perhaps 30m individual card transactions and multiple observations per transaction like this, and I'm building a neural network to identify fraudulent transactions. The key here is to ensure that it's a self-learning algorithm which has the advantage of reducing the false positive rate – a false positive is when a model suggest that a transaction is suspicious, you check it and it proves not to be.

"Maybe the best way to describe a neural network is say you buy your wife a £30 bouquet of flowers every Friday at 1pm in London. Now if you do the same thing next Friday, the model will begin to understand that this is normal behaviour for the card. If you then suddenly try to withdraw €3,000 from an ATM two minutes later in Mozambique, the model will generate an alert which an agent in a bank will then investigate and contact the cardholder. The cardholder is protected from anxiety, the bank is protected from loss and – assuming that the banks fraud team are on the ball – the person who has fraudulently obtained the card details might find themselves coming to the attention of the police. Everyone's happy.

"From a development perspective how it works in hyper-simplified terms, is that I have a subset of data points from confirmed fraud cases and build this as a portfolio model: from this, the neural net finds patterns in individual cards and builds a model that will itself learn to construct an ever more sophisticated pattern of clients' behaviours with each activity the cardholder takes.

"The most difficult thing in this work: making sure the client knows what the hell they want; you'd be surprised how often banks don't really understand what they want from their data to the nth degree: for example, they might say 'I want to reduce default' – well great, but how do you define default and what portfolio do you want to use for this, are there any exceptions from the modelling sample?

"We live in a world of enormous amounts of data. The number of approvals for payments that a major global bank processes on any given day can run into tens of millions. Do we still have an overview? For the non-financial data streams, it's mainly controlled 'by exception', as the expression goes. What this means is that the data streams themselves remain essentially unseen, but there are all sorts of built-in checks that throw out exceptions, which generate alerts in case of something unusual.

"In their business processes banks pre-define what legitimate data is, and these pass through the streams unhindered (such as a field in which the value will always be a number: in the event that it's a letter then that's thrown out). There is a risk that data seen as legitimate may prove not to be so, true but there's an enormous amount of work being done in the financial sector to prevent such a thing, though and so I wouldn't recommend being too paranoid about it!

"Also, there have been massive redundancies recently. Usually when people are fired, in the unlikely event a scam had been in preparation, it would fall apart, or whistleblowers come forward. That this hasn't happened suggests that the models are OK. That's not to say that I'm giving anyone a hospital pass – and I'm certainly not creating a hostage to fortune.

"I said I was a quant, derived from the word 'quantitative'. We're the people of a definite mathematical bent, and if you're looking for a warrior-like analogy, we are perhaps the "armourers" of the financial industry, or, let me think … Traders are the warriors of our world; they go out and fight. I think of them as 'egos on legs'. Sharp suits, looking very smart… We quants are the trader's brain. It's our model that defines not only the risks the trader can take, the model also calculates how much risk he is taking with his particular trades at any given moment and we also predict future movements in valuation, pricing and the like.

"Philosophically, mathematics is a common language by which to describe the world. It's a language I love, almost too much for my own good. If I look out my window and I see three boats coming down a river, I am going to calculate without thinking about it how they'll avoid collision, which will pass who, when and where. In traffic I drive my partner insane by doing manoeuvres that she finds terrifying, because she hasn't made the calculations about other cars' speed and direction that I have. In airports I study check-in or passport lines for a while, dividing each into subsets of people, analysing them for the presence of children, lots of baggage, single or travelling together … Only then will I chose which line to stand in. Most of the time (>90%), I'm right about the queue I choose which gives me a quiet, albeit, somewhat geeky, smile of self-satisfaction.

"I have been in banking for over 20 years, and for several years I was with one of the major international investment banks. I discovered that I am just not enough of an arsehole to make it there. Why the top people at investment banks are like that? Well you have a thousand vice-presidents vying for 10 managing director posts. What do you think will happen? People will do anything to get ahead, back-biting, back-stabbing, the whole nine yards. For those of us who find life surrounded by other people difficult enough as it is, the requirement to network is hellish.

"Is there a point about banks I would like to see more broadly discussed in public? Well, about the current crisis. First, banks were falling over themselves to lend and I don't remember much complaint or criticism from the people the banks were lending to? This was not just the banks' fault, so if someone were to think 'hypocrisy' about the attitude of the public then – yeah, I'll buy this.

"Furthermore, I think it's a mistake that after the bigger items of the crisis that nobody was sent to prison. That would have sent a signal, especially if some of the more overwhelming acts of avarice (ABN takeover by RBS, for example) were punished not only by the failure of the bank but by there being criminal charges levelled against the CEO and chairman of the organisations concerned.

"You ask why there hasn't been more of an internal revolt, after so many people inside finance who had nothing to do with the causes of the crisis got burned? I'd say there is a great degree of coalescence. People may whine and complain but they get things off their chests and have now morphed into this amorphous blob. It's a group that sticks together. Call it fatalistic. It can be over any moment so I am going to try and grab what I can.

"It can be dangerous, having a mind for data. If you want an example, see the film A Beautiful Mind about Dr John Forbes Nash, Jr, the former 'ghost of Fine Hall' at Princeton. For my part, I'm nowhere near as smart as Nash, but I find it hard to switch off. A few years ago I found myself taking advantage of psychiatric services; as a consequence of which, in my case, I'm taking a large dose of medication on a daily basis to try to stabilise myself and I'm conscious that I sacrificed a marriage on the altar of work. It's particularly hard for me, with mild Asperger's, which in my case wasn't picked up until adulthood. Suffice to say, it's very tempting to just stay in the world where everything can be understood in mathematical language.

"Not sure though that I'd voluntarily swap IQ points for EQ – even though I'm certain that I'm going to end up as one of the single old blokes that you might occasionally come across – nice, big house in the country, lots of dogs, materially comfortable and yet utterly alone and mad as a fish.

Later, when asked to elaborate on that final point, he responds via email:

"I've long been aware of the prospect (with some 'tongue-in-cheek') of becoming mad as a fish, and the attractiveness of the current imbalance between EQ and IQ is that I know that my biggest, deepest fear is failure. With the current imbalance, I know that the risk of failure is reduced to its current level: eg, small but still real. That fear of failure drives me and means that I know I'm giving up anything approaching EQ in pursuit of avoidance of failure."