In a world that often seems to be drowning in data masquerading as information, how is “alpha” or an “edge” to be found?
There seem to be two main alternative routes: one either has to have better information than one’s competitors; or, with the same information, a superior ability to identify and exploit patterns in it to identify both risks and opportunities.
It is obvious that there is an escalating “arms race” in acquiring “better” information, with the term “alternative data” now widely used in business and financial circles. For example, the use of data from commercial satellites is becoming increasingly common for hedge funds and others as a means to acquire “non-public” data. Yet, paradoxically, this simply leads towards a scenario in which all those who can afford it, have it. The edge is increasingly blunted. This then leads to the search for the next “alternative” source, but begs the question of whether “the next big thing” has any real value.
So, what about superior pattern recognition? Human beings are, after all, programmed by evolution to look for patterns in what their senses perceive as a means to avoid the lion lurking in the underbrush. What began as a mechanism necessary for survival has become a dominant trait, with the ability to recognize patterns, for example, visually/spatially considered an essential component of intelligence.
In the world of credit and risk analysis, the ability to understand and forecast what may happen in respect of a particular obligor or scenario is essential. To a large extent, this involves the ability to discern patterns that one knows from experience and acquired knowledge are likely to lead to a particular outcome, good or bad- for example, over-leverage, or insufficient liquidity. However, it also involves being able to distinguish between patterns that are meaningful (a signal) and those which are merely distracting noise, as well as to recognize that there may be a new pattern or paradigm, because one can be lulled into a false sense of comfort by failing to question what one perceives or “knows”.
Naturally, the growth of AI has led to something of a frenzy in terms of interrogating data for patterns that no-one else has yet discovered. Within certain parameters, specialized AI (for that is all that exists at present), backed by ever-rising processing and computing power has the potential ability to see things quicker, or differently from human beings, no matter how experienced or skilled. One only has to look at the fact that AI systems can now overwhelm even the best human players of chess or Go (to mention only two examples) to understand that.
However, the world is a complex, non-linear place, which means that, for now at least, even if the existential risk from AI to the role of (re)insurance underwriters in high-volume, commoditized product lines looms ever nearer, in the more complex areas in which being able to understand causation, correlation, constraints and the nuances of game theory and human behaviour are critical, the pattern-recognition abilities of human operators should prosper for much longer.
While we are eternally paranoid at Awbury about mistaking noise for a signal, or to our thesis being simply wrong, we believe that there is hope for us yet, given our relentless focus on complex, non-standard risks!
The Awbury Team