Deep learning models, often the bedrock of machine learning and artificial intelligence, are playing an increasingly large role in financial analysis. These models, while powerful, lack transparency – often known as ‘black boxes’ due to the opacity of their algorithms.
In the era of artificial intelligence (AI), there is a growing interest in the use of AI models like ChatGPT to enhance financial reporting processes. However, it is crucial to recognise the importance of structured, machine-readable data in training AI models effectively.
How do reporting firms talk when they know that the machines are listening? A new working paper from the National Bureau of Economic Research (NBER) is the first of its kind to take a look at how increasing machine and AI readership is altering the way companies write their financial reports.
What do your mortgage, your car, and maybe even your fridge have in common? They are all either already or could soon be reliant on AI and machine-learning (ML) algorithms.
Technology is changing the world of audit. From the systems auditors rely on to improve the accuracy of their audits, to the data (soon to include, under ESEF, XBRL tagging) that requires review, tomorrows’ auditors must know how to work digitally.
AI and machine learning (ML) are playing an ever-growing role in financial markets around the world – and while these new technologies can reduce costs and increase speed for firms and investors, they also come with an element of risk.
Machine learning (ML) has been touted as set to transform the financial landscape – but is this hype or reality? A recent Bank of England survey indicated that machine learning – including live projects using complex methods such as natural language processing – are increasingly common in the UK. The data shows that a third […]
We’ve heard a lot about the potential of fintech to open up access to credit by using big data and alternative data sources to assess risk – but how do these techniques actually stack up when compared to traditional credit scoring? A recent working paper from the Bank for International Settlements (BIS) looks at transaction […]
A Bank of England (BoE) report has found that the use of machine learning (ML) is on the rise in financial services. The report is based on a joint survey from BoE and the Financial Conduct Authority in 2019 of 106 firms. The UK economy is increasingly powered by big data, with financial services in particular […]
The UK’s Prudential Regulation Authority (PRA) is working on machine learning techniques to predict bank distress. Using novel data sources, mainly confidential regulatory returns, this still experimental early warning system for predicting bank distress significantly outperformed other models. This development follows the Danish Business Authority’s machine learning-based Early Warning Europe, which uses large data sets from five European countries […]
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XBRL International is a global not for profit operating in the public interest. Our purpose is to improve the accountability and transparency of business performance globally, by providing the open data exchange standard for business reporting.
