The senior curator for MarketsMuse.com Tech Talk section was so inspired by a recent article “A Cynic’s Guide to Fintech” by Dan Davies, the Senior Research Advisor at Frontline Analysts and published via Medium.com, we wanted to share the opening elements with our audience..For those of you following the various forays into fixed income electronic trading platforms, Davies has a pernicky point of view worth considering. Dan Davies’ twitter feed is worth following as well.

A Cynic’s Guide To Fintech

Several business models that are bound to fail — and a few that might have a chance.

A pal working in and around the VC industry asked me the other week what I thought about financial technology, or as the unlovely abbreviation has it, “fintech”. Here are my edited thoughts, from the point of view of someone who spent many years as a banks and diversified financials analyst, and who has some fairly strong prejudices about what works and what doesn’t work in financial services industry. In my view, the portmanteau term “fintech” groups together a number of different business models; I haven’t included “something something Bitcoin” in the list because that’s a slightly different debate. Here’s my partial list …

Fintech business model #1. Reinventing past mistakes of the banking industry because you don’t know about adverse selection

There are a lot of people out there who have expertise in data science, and who think that the incumbents in the industry don’t have sophisticated risk-based pricing because their technological skills aren’t up to the task of identifying risks. These people tend to think that they can go into the credit cards business, or the payday lending business or even the car insurance business, and pick up market share from the dumb old banks by using algorithms! and social media data! and so on.

This is not true. It is true that banking IT is generally terrible, but actually, if you look into the digital archives of any large incumbent player, you will tend to find an extremely sophisticated, cutting-edge algorithmic risk pricing system which was thrown away a couple of years ago because it worked great in testing and then fell apart really badly in the real world.

There are two reasons why fine-grained risk based pricing has been such a catalogue of failure. First, banks almost never lose money on bad risks. They lose money on good risks, which go bad. The nature of algorithm-driven pricing is that you are searching out profitable niches, Moneyball style, in the form of customers which have some set of characteristics in common which marks them out as statistically better than the average. Unfortunately, this tends to mean that you get a book of business which has loads of little concentrations in them — you’ve got all the mixed-race dentists in Yorkshire, or something. And this, in turn, means that when the world changes, your risks tend to be very correlated and you lose years’ worth of profit in one lump. Continue reading →