When people talk about machine learning they are a referring to a type of artificial intelligence that has the ability to learn without explicit programming. These programs can grow and change on their own once they know how to learn and some financial companies are beginning to use machine learning for a variety of different reasons.
According to Fortune Magazine, some of the top financial technology trends for 2017 are consumer trust, buying behavior, mobile banking, blockchain, cybersecurity, and access. And machine learning could be applied towards many of those already (for example: using data to optimize for consumer buying behavior). But some financial companies are already using machine learning to solve some of these fintech problems today.
Standard Bank was established in 1862 and it is now one of South Africa’s largest financial services groups, operating in 20 countries across Africa and other key markets around the world.
Their ATM-CIT Route Optimization Project Navigator used artificial intelligence (machine learning algorithms) to manage risk, cost, and service in the distribution and collection of physical cash in the ATM supply chain. An employee built a bespoke tool (dubbed ‘Navigator’) that used a highly-customized optimization algorithm to optimize service levels and reduce costs based on user-input and new techniques for the route optimization problem were developed in the process of creating this tool. But perhaps best of all this prototype tool reduced failure demand (i.e. downtime) significantly (between 20% – 23%).
This idea was shared in an internal innovation program called Up Squad. Up Squad is an annual competition open to all PBB SA, G2, RoA, Channel and PBB Enabling Functions employees, during which they knowledge share their implemented innovations that they are using to change life in the Bank. This knowledge sharing meant the gains in this one prototype could be shared elsewhere in the bank – a great opportunity for those hoping to optimize financial services.