There’s a seismic shift taking place in the finance industry. It’s happening right before our eyes—but you could miss it unless you have your ears to the ground. The machines are coming, and as they approach, they’re toppling old paradigms in that industry.
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This isn’t about androids taking the place of tellers at the banking halls. It’s about the more subtle, but equally far-reaching take-over of the system by Artificial Intelligence (and its incarnations).
There’s a lot of talk about how radically transformed segments like banking and insurance could be over the next few years thanks to AI. On the one hand, the optimists speak of super-fast service delivery and ultra-personalised products for customers. On the other, pessimists rail on about the possible loss of thousands of human jobs to AI-powered representatives.
We’re not going to dwell on the fears attached to its coming here. Instead, we’ll look at some of the ways it could change finance as we know it.
Perhaps, you haven’t exactly gotten a handle on what AI—Artificial Intelligence –actually is. So we’ll define it first, before going on to explain why it’s such a big deal.
Artificial Intelligence Defined
Artificial Intelligence is simply the acquired ability of machines, especially computers, to do things that we would expect of intelligent humans. We’re referring to things like answering questions, holding conversations, and solving complex logical, mathematical, and modelling problems.
To do this, machines – or specific computer programs running on them—must be able to make sense of the data that it comes across; after all, it ‘grasps’ human communication (numbers, letters, words, and sentences) as a tranche of data.
One subset of AI, Machine Learning (ML), goes beyond just engaging data, to actually learning-by-doing, i.e. learning behaviours and appropriate responses through continuous interaction with humans and the wider environment.
The Adoption Rates Of Artificial Intelligence
It’s not clear how widely AI has been adopted across the world; studies have come up with disparate numbers. Nevertheless, they do show what we would expect: greater uptake among the more technologically developed countries compared to their developing counterparts.
A survey of over 20,000 data professionals across the world (conducted by Kaggle) suggests that the global average adoption of Machine Learning across relevant companies is 45%. Israel (63%), Netherlands (57%), and the United States (56%) showed the highest rates of adoption. Nigeria (23%) had one of the lowest adoption rates in the world.
Still, the numbers indicate that there’s growing enthusiasm for these technologies. Another study from the World Economic Forum suggests that 63% of companies in the financial services industry could be mass-adopting AI in the next two years. At present, China, the US, and the UK were leading the way.
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The Coming Transformation Of Finance
Already, there’s been some adoption of AI in Nigeria’s financial sector. As you would expect, banks and FinTechs are at the forefront of the action. But there’s more to come.
Below, we’ll review the AI-driven changes we’re seeing, as well as the ones we’re going to see in the next couple of years.
Financial institutions, like every other business, want to minimise risks. They can get a lot of help with this from Machine Learning. The ability of ML to model trends (mentioned earlier) comes into play here. ML systems can use the large amounts of data available to financial houses to create precise models of present trends, and expose potential risks in the process.
Those risks may not always be obvious to human analysts, who will require far more time to wade through their data tranches. Working with AI and ML, they can spot financial risks, and find ways to avoid them early enough.
This use case is significant for banking and insurance (and the FinTechs playing in those spaces), where risk determination is a key part of their operational framework.
Chatbots And Virtual Assistance
Developments on this front have been swiftest. We’ve seen banks deploy AI-powered chatbots to engage their customers on digital channels. Examples include UBA’s Leo, Access Bank’s Tamada, and Keystone Bank’s Oxygen.
These assistants can help with a range of tasks, from checking account balances, account opening, checking BVN, fund transfers, and loan requests. Expect these capabilities to expand as demand grows and banks become more comfortable with AI systems.
Fraud Detection And Prevention
With AI systems creating a history of customer transactions, it’s easier to detect fraud when it’s happening, and take steps to curtail it.
For instance, a customer of a financial institution may typically carry out electronic transactions worth less than ₦50,000 on any given day with her debit card. If there are suddenly multiple transactions in a day with that card, and each of them exceeds ₦100,000, AI-powered security systems could spot this and relay the information for necessary action to be taken.
Determining Credit Worthiness
Until fairly recently, banks have relied on human agents to access the creditworthiness of loan-seeking customers. This undertaking is often tedious, and may sometimes be influenced by the lender’s sentiments.
Across the world, AI-powered credit-scoring systems are taking the place of humans in this important activity. Because they are better at modelling client’s habits around financial transactions, they can more accurately determine whether a customer is a right fit for a loan. There’s also no lender sentiment, which makes the process all the better.
FinTechs (particularly loan platforms) are already adopting these credit scoring systems, which enable them to quickly determine that a person qualifies (or does not qualify) for a loan.
Final Words: The Future Is Here
There’s plenty more in store from AI and ML. We could see even more robust and sophisticated applications across finance in the near future. However these things play out, tomorrow’s world will be a vastly different place than today. And thanks to AI, it could run a lot smoother.
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