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Home ยป Machine learning, deep learning and AI generation deliver the most value in finance: KPMG report
AI in Finance

Machine learning, deep learning and AI generation deliver the most value in finance: KPMG report

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Organizations are increasingly deploying artificial intelligence in their financial operations, realizing ROI and benefits such as better data and decisions, faster insights and reporting, reduced costs and operational efficiencies, shows a KPMG report.

According to the report, while investments are being made across a range of AI technologies, organizations are getting the most out of machine learning, deep learning and generative AI. The return on investment for these technologies meets or exceeds expectations.

The research covered 2,900 organizations in 23 countries. KPMG has identified a cohort of leaders who are more advanced and mature in AI deployment. About 24% of the organizations were leaders, while 58% were middle performers and 18% were beginners.

More than seven in 10 organizations (71%) use AI to some extent in their financial operations. Currently, 41% use AI to a moderate or significant degree, and this figure is expected to rise to 83% over the next three years.

The use of AI generation has also grown. The percentage of companies that do not intend to use Generation AI has now fallen from 6% to 1%. Generation AI has become a key focus and top priority for the future, with 95% of executives and 39% of others expecting to adopt it in financial reporting over the next three years.

Among emerging markets, India and China are ahead in the use of AI. Most industries have a similar percentage of leaders, although financial services leads the way (29%), while healthcare lags behind (16%). Additionally, companies with larger revenues are more advanced in AI.

In corporate finance, AI is primarily deployed in financial reporting, with nearly two-thirds of companies testing or using AI for reporting, accounting and financial planning. Nearly half of companies are also testing or using AI for treasury and risk management. This has the potential to drive better debt management, cash flow forecasting, fraud detection, credit risk assessment and scenario analysis in treasury and risk management functions.

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