Bravura Solutions Limited (ASX:BVS) (Bravura): Artificial intelligence and machine learning are already impacting financial services firms and make the process of giving advice smoother, say delegates from a recent round table event, hosted by Bravura Solutions.
A summary paper of the discussion, “Artificial Intelligence and Machine Learning in Financial Services – Where might we go next?”, predicts technology will continue to drive structural changes in the advice industry.
Simon Clare, Head of Technology and Innovation at Bravura Solutions said:
The opportunities for financial services companies from artificial intelligence (AI) and machine learning (ML) are boundless. It will reduce cost, improve the product and drive customer engagement.
In particular, the report predicts that AI and ML will help advisers:
- Dramatically improve risk-profiling by using large data sets to identify patterns and characteristics to determine the best asset allocation for clients
- Free up time by automating many back-office tasks such as report building, fact finds and suitability letters
- scale-up cost effectively by achieving technology-driven efficiencies
- Offer a more personalised service for customers
While there could be a level of up-front cost to implementing an AI strategy, Clare believes the savings in the long-term make it worthwhile for advice firms.
Clearly the cost of implementing these kinds of technologies shouldn’t be underestimated. ML increases the scope for automation in areas that couldn’t previously be automated, ultimately bringing costs down. The long-term benefits of these technologies should outweigh any short-term hurdles such as the cost of implementation. Ultimately, firms will save on operational and development costs allowing for greater potential future profit.
One of the most talked about possibilities that ML offers financial services is the ability to drive data-centric decisions, rather than decision making based on human instinct or experience, which is often fallible.
AI has the possibility to provide a service that is less open to failure, can adapt and learn from itself and is ultimately operationally efficient for both the customer and company.
In particular, we see opportunities for smaller firms who can perhaps take a more nimble approach in adopting ML and AI efficiencies to scale-up and make ground on larger more monolithic rivals.
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