BUILDING TRUST INTO DATA ANALYTICS AND AI With both financial and nonfinancial information key to transparency, nonfinancial data should be as credible and trusted as financial data. However, there are several challenges faced by finance leaders when developing a trusted approach to nonfinancial data.
A key challenge is the ability to exploit nonfinancial data while also managing any associated risks, with concerns around data protection privacy and compliance. At the same time, there is also concern regarding the trust in and the objectivity and credibility of the data behind the disclosures, particularly relating to the use of robotic process automation (RPA) and artificial intelligence (AI) in the capture and analysis of the data. This is particularly relevant given that 60 per cent of group CFOs say that the quality of finance data produced by AI cannot be trusted in the same way as data from existing finance systems.
To build trust in data analytics and AI, there are two priorities:
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