Artificial Intelligence, two words that can mean a dozen different things to a dozen different people, words that can either be an opportunity, a threat, or both.

At its most basic, AI is helping credit and collections businesses to use algorithms to solve issues that they would have ordinarily have dealt with manually. It enables them to analyse customer data and behaviours, both to determine patterns and inform how future ‘products’ and ‘services’ are developed.

It provides insights that can help determine an individual’s propensity to engage, in the case of collections, and a better assessment of risk, when determining to whom to extend credit. And outside of credit, AI can look at promotional and advertising campaigns, to determine their effectiveness and response levels, giving the marketing manager greater visibility on ROI.

What AI achieves goes far beyond the capacity of the human brain. The principal shortcoming of the human brain is that it is finite in the volume of data that it can process, and the speed with which the results can be formulated. There is also a limit on how much information it can store at any one time. AI – perhaps more accurately described as ‘machine learning’ – has no such limitations. It can process and store information faster, and in larger volumes. It also analyses the data dispassionately, with an air of objectivity that cannot be replicated by the human mind.

The ability to analyse large sets of data increases the accuracy of the outcome, and the very best algorithms are even capable of predicting what those outcomes will be. Whereas the human brain can only ‘reason’ within its own limited parameters, AI is not so incumbered, and capable of finding ‘unexpected connections’ – connections that the brain does not have the capacity to compute or identify, and that our instinct does not have the willingness to accept. Put another way, AI feeds a new model to the world, rather than the human brain which allows the world to feed the model.

AI identifies patterns that humans are not programmed to be looking for, removing what is known as our own ‘confirmation bias’, where we only look to confirm thoughts that we already have (especially about people and demographics) and doctrines and processes that are already understood and accepted practice. It enables its masters, the Head of Collections or Director of Risk, for example, to recommend, use and learn based on solid data, and with a very high degree of accuracy.

In terms of the customer interaction, it will ultimately lead to a company recommending products and services to their customers that they don’t even realise they need, but that the data proves that they do. It will identify targets (including direct targets and influencers) and opportunities that companies don’t even know currently exist.

Andrew Georgiou, Founder, FILED.com