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Alveo Reviews Costs and Use Cases of AI in Financial Data Management

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Adoption of AI across financial services is causing a cost shift from operations to technology and data, with 63% of decision makers expecting AI to result in an increase in the cost of data within their organisation. The cost of hardware and software licences is also likely to rise in response to AI, and 50% of decision makers note technological limitations among the biggest barriers to implementing AI in financial data management, 46% reference a lack of skilled personnel.

On the upside, according to research commissioned by Alveo that surveyed senior decision-makers at financial services organisations in the UK, US and DACH region (Germany, Austria and Switzerland), AI offers huge potential to drive productivity across data management with 53% of the sample ranking data quality management as the area of data management where AI will have the greatest impact.

In terms of today’s use of AI, the survey found financial services firms using AI for different aspects of financial data management, with 55% of firms using it for risk data management, 49% for client data management, 47% for portfolio data management and 46% for master data management.

Commenting on the results, Martijn Groot, vice president of marketing and strategy at Alveo, says: “As the human element in data workflows diminishes due to the next wave of automation, there is a large premium on good quality data. To achieve and maintain the high standard of data quality necessary for effective AI implementation, firms will need financial data management expertise to design, oversee, and refine the infrastructure and processes that feed into AI systems, and ensure all data is accurate, relevant, and timely.”

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