Xavier Fernandes spends his days directing analytics operations at Metapraxis but here takes time out to tell TFT what’s so great about AI (Clue: You’ll never have to use ExCel again…)
How can areas most ready for AI disruption be identified?
Areas with the most repetitive activity are traditionally the ripest for disruption. Businesses should also follow the money by looking at where they made costly mistakes in the past. AI is great at spotting outliers before a human expert would notice them, and this can flag up both impending problems and new sources of revenue.
Where is AI going to make the greatest impact in financial services?
There is increasing focus on maximising customer lifetime value through the use of AI. Being able to predict existing customers’ needs as well as track trends in their financial circumstances is supercharging the old cross-selling approach with testable, predictable outcomes.
AI is already creating an information arms-war as data-savvy customers are more equipped than ever to seek a better price or a return.
AI is great at spotting outliers before a human expert would notice them, and this can flag up both impending problems and new sources of revenue.
What are the greatest barriers to wide-scale implementation of AI?
Financial services firms could begin to face regulatory scrutiny on the fairness and transparency of any automated decisions made by artificial intelligence programs. Regulators’ concern that AI and machine learning are ‘black boxes’ whose inner workings aren’t clearly understood is considered by many to be the main barrier in implementing this technology.
How can barriers to implementation be overcome?
With regulatory scrutiny in particular, companies need to bring in external expertise to ensure their algorithms are compliant both now and in the future as the regulatory landscape around AI changes in the years to come to prevent any reputational and financial damage.
How can the workforce be better prepared for the rise of AI?
We’re already seeing robotics begin to replace more process-oriented tasks, and this will only increase. That being said, for white-collar jobs, automation tends to replace certain tasks in the job, rather than the role in its entirety. Employees should work with the business to proactively identify what areas of their role could be automated, so that they can focus on the areas that add real value to the business’ commercial goals. Employees can be even more proactive by looking into courses on AI to support their role.
Employees should work with the business to proactively identify what areas of their role could be automated, so that they can focus on the areas that add real value to the business’ commercial goals.
Should we be scared of AI, what do you think the biggest risks are?
We shouldn’t be scared of the technology, but of how it is used. Algorithms can run amok if left unchecked, so if they are being used to make business-critical decisions, checks and balances must be incorporated into the business process.
A further risk is unconscious bias. Machine learning algorithms learn from past business data and decisions, and can propagate biases hidden in that data. If algorithms learn to treat certain groups of customers unfairly, not only can this result in the loss of those customers, but can risk damaging the organisation’s reputation if ongoing sample checks aren’t built in.