The natural limits of artificial intelligence
Artificial intelligence will transform customer interactions, all users – across companies, market segments and individual customers – will value different things.
But two issues will be common to everyone, regardless of where they fall in financial services industry: privacy and risk. For financial institutions that integrate AI into business process, these need to be at the top of any priority list.
AI has huge potential because of its ability to learn and adapt. But this introduces new kinds of risks, depending on how much autonomy the systems are given when they make decisions.
Accordingly, it is critical to have a clear strategy on what role AI will have and what checks and balances to maintain over your systems.
This raises some important questions: will humans need to make ultimate decisions outside of certain parameters? Will there be spot checks and AI decisions? How will AI systems be iteratively tested and upgraded?
From circuit – breakers to configurators, systems can spiral out of control quickly without paper rules. These are complex issues; to strike the right.
Protect Privacy, or else.
Privacy is another critical concern. Customers must feel their information will be used to benefit them, and not in a way that intrudes upon their private lives.
This is a sensitive topic; infact, If handled poorly, privacy violations could invite a heavy-handed regulatory response.
Companies will need strong operational controls in place so that data is not being misused in – or across – business units. Infact, as we have seen, regulators are getting more comfortable with how they use technology to enhance supervision.
The US CEPB for example, recently announced its first enforcement action (against a Fintech payment company) related to privacy and cyber-security, and regulators are likely to step up these efforts in the future.
Use the power of analytics to give a customer more
As data proliferates, we encourages executives to explore how to get the most value out of what they hold: what information is already being collected, what form it is in, how (or if) it is being analysed today, how it can be adapted to address the issue of customer need and address costs and more.
These are the key and ongoing questions for any business, and it is why many financial services firms are appointing chief data officers (CDOs).
When used properly, AI and data analytics together can help financial institutions understand their customers more than ever before.
These new tools provide access to rich, compelling and personal service to customers. The service is personal because it draws on individualized data about a customer’s behaviours which is then used to customize product offerings for that specific user.
The days of designing products based on broad demographics or survey groups are fading fast.
This user experience will occur anywhere: either in branch, online, or through a customers mobile and wearable devices.
Infact, because digital has become mainstream, we see consumers starting to turn to noble devices as their preferred tool for making online and proximity payments, rather than credit or banks.
These new capabilities, with seamless transitions among them are the essence of what we mean by omnichannel experience: customers will be able to start a transaction in a browser on their laptop, continue it on a mobile device, walk Into a branch with their tablet, and be greeted by name without needing to explain why they come in.
These tools go far beyond the customer interface; they even have implications for existing physical infrastructure. For example, as a bank, it is important to consider which locations will house your ‘flagship’ and full service branches and where to locate the assisted, self-service ( or even robot-enabled) branches.
The long leases on many branch sites mean that this service map needs to be drawn up today to be ready by 2020.
Finally, as these technologies advance, financial institutions will quickly into some human capital limitations. AI already has practical implications and companies need to be investing in it today. We are already seek g high demand for AI experts. Companies will need to consider how they will address this talent gap in he short-term, even develop strategies for the long-term.