Lately, there seems to be a frenzy well fed by consulting firms in the enterprise world about digitisation and the necessity to “digitise” companies’ business models and operations.
As the landscape of financial services continues to change, it’s critical to stay ahead of the game. ATMs were groundbreaking achievements once upon a time, while more recently, mobile baking was the logical next step in banking’s maturation.
Combating money laundering is no longer a choice but a must for banks. But the effort that must go into fighting it is daunting. How can technology, especially artificial intelligence and machine learning, battle the costs and drains on monetary and human resources required for AML compliance, making the whole process a lot easier and more effective? Can AI be trusted to do the job right?
Rarely has a technology been met with the excitement and trepidation that AI has. Because artificial intelligence not only matches but can surpass human intelligence, it is exciting as a means to improve speed, save cost and maximize accuracy—but menacing for its potential to displace human workers. Banks are embracing AI for its staggering benefits, while also acknowledging that it creates a few wrinkles that need ironing out.
Banks are spending $20 billion on compliance in an effort to combat money laundering, yet only one per cent of illicit financial flows are seized by authorities every year. While regulations have been introduced to crack-down on money laundering, so far they have had a limited effect.
How can banks and financial institutions get through to the generation born in the 1980s and 1990s, the so-called “millennials”, also known as Generation Y, given their shorter attention spans and distrust of brand loyalty?
Anyone working in banking knows that customer expectations are charging ahead at full throttle, fuelled by technology advances. Fortunately banks can use innovations such as AI and IoT to meet customers where they are at, and a recent Fujitsu report shows they are doing—or planning to do—just that. So what can we reasonably expect banking to become as a result of this transformative process?
Machines are capable of “thinking” faster than their human creators, but that doesn’t necessarily mean they think better. Computers have their place in helping humans, but that doesn’t mean it is wise to let them take their place. Machines make mistakes, too, although theirs are a little different than the ones humans make. Is there a middle ground at which computers and humans can work together effectively?
Simply mentioning the topic of artificial intelligence in finance usually elicits a mix of excitement (“AI is amazing and can solve every problem”) and fear (“Will we all lose our jobs? Will robots cause the next market crash?”).
What a huge advance it is that the financial sector now has robots to relieve the ever-growing pressure of regulation. Almost everyone handling or processing personal data now faces vastly increased compliance requirements once the European Union’s General Data Protection Regulation
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