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By Julia Smith
The financial services industry is facing down smaller startups with machine learning technology at a faster pace than incumbents in any other field.
But as organizations race to invest more in the technology and adopt new business processes to achieve maximum results, important policies are getting left behind: risk management.
Our new Global CIO Point of View, a survey of 500 CIOs across 11 countries and 25 industries, takes a closer look at 50 CIOs in the financial industry.
Compared to CIOs from other industries, in the financial industry:
23% more CIOs say machine learning is a strategic focus
32% more CIOs are making changes to IT structure to accommodate machine learning
49% more CIOs are developing a road map for future process changes
68% more CIOs are recruiting employees with new skill sets
72% more CIOs are redefining job descriptions to focus on work with machines
In an interview for the report, Matt Potashnick, CIO at AXA UK, the British subsidiary of the $150 billion insurer, said:
“The whole area around AI, robotics, and machine learning is firmly centered in our strategic plan.”
“In terms of underwriting and pricing capabilities, we are moving away from traditional ways of building models, and utilizing machine learning to really enhance that,” he added.
However, despite the financial sector’s early lead in machine learning, many firms are not yet developing processes that address the risk of machine-made decisions. They are also slightly behind other sectors in ensuring that the data used to make decisions are accurate.
For example, 14% of financial services CIOs have developed policies for insuring the accuracy of data compared to 18% of others. Moreover, financial services CIOs are just one percentage point ahead of others (18% vs. 17%) in addressing the legal risk of mistakes made by machines in enterprise policies.
This leaves the industry vulnerable to mistakes, external cybersecurity threats, and unexpected changes to financial markets.
Without appropriate adjustments to processes around automation and risk management for machine learning, financial firms are unlikely to get full value from their investments.