I read a McKinsey article the other day about cultural invisibility – that tipping point when new technologies become so common they’re taken for granted and become “invisible.” Electricity, steam engines, and other twentieth century inventions typically seemed to take about 80 years to make the transition. Computers haven’t faded from view just yet, but are likely to do so by about 2040. And machine learning isn’t expected to take much longer to recede into the background either.
Machine learning first came into its own in the late ’90s. With the proliferation of unmanageable data volumes and complexity, data scientists realised that they could stop building finished rules-based models and instead train computers to self-learn from data.
Most of us are already familiar with the concept of a self-driving car (machine learning in action). As it drives, it assesses risk much faster than humans’ response times, taking evasive action to avoid collisions – even something happening 400 yards behind it. It remembers all the years of history and variations of conditions, such as tire pressure, rain, ice, snow, specific junctions, and rush hour. And of course, at any time, the driver can just grab the wheel and take over.
Banks have the equivalent of their own “self-driving” systems. Machine learning – in the form of complex predictive analytics, knowledge extraction, artificial intelligence, and reasoning – is starting to perform tasks faster and more accurately than any human being. Just like specific driving conditions, these systems can instantly recall and process patterns faster and make accurate predictions or automated decisions for us.
In the same way humans will still be involved in the journeys of self-driving cars and can take the wheel at any time, human banking staff will be involved with their systems – but much later in the process. I’ve outlined a few examples below:
- Robo investing – Machines invest in specified risk tolerance markets, taking evasive action when necessary (e.g. interest rates go up and oil prices drop)
- Fraud analysis – Track historical patterns, extract information, and identify red flags that humans can’t find in fraud analysis, money laundering, and risk management – the banking equivalent of avoiding accidents on the road
- Automating payments – Machine learning automatically matches PO numbers with invoices, with staff getting involved once this matching has taken place
- Customer service – Anticipating needs by intelligently tagging and clustering inbound social media posts, emails, etc., e.g., triggering follow-up action if a customer gets married
Now that machine learning intelligence is increasingly embedded in business processes, accurate predictions, unprecedented insights, and automated self-learning routines will inevitably become culturally invisible.
In Europe, analysts say, more than a dozen banks are already profiting from machine learning by replacing their outdated statistical-modeling approaches. Some banks have seen 20% savings in capital expenditures, 20% increases in cash collections, as well as 20% declines in customer churn. Examples include building micro-targeted models that more accurately forecast who will attrite or default on their loans and determine how and when it’s best to intervene.
Historically, reasoning has been a mostly human skill, but it is now developing into a machine skill – and one that can’t be ignored.