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.
Shenzhen, China’s southern metropolis, is named after the rice paddy drains, or ‘zhen’, that once stretched across the landscape. Today, where mere decades ago sat a small fishing village, Shenzhen is a city of over 11 million inhabitants, known as China’s answer to Silicon Valley.
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.
In a recent speech Benoît Cœuré, Head of the Bank for International Settlements (BIS) Innovation Hub, asked the listener to imagine a world where regulators have access to big data sets of high granularity, diversity, and frequency.
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.
Amongst several fascinating presentations at the Eurofiling Innovation Day this week was an interesting demonstration on how XBRL reports can be used as the basis of explainable AI for bankruptcy prediction.
Central banks and national statistical offices have increasingly been looking to big data sets and analytics to provide new insights – but managing data of this magnitude requires new data platforms. This week a Bank for International Settlements (BIS) report breaks down how best to deal with big data. Novel big data sets – such […]
XBRL is now the standard for financial reporting, providing machine-readable data with vastly improved accuracy and quality to regulators worldwide. With significant amounts of structured data increasingly available, how can AI be used to turn that information into insight? While some AI programmes are designed to be able to deal with data that is unstructured, […]
A recent Centre for Finance Technology and Entrepreneurship (CFTE) paper develops a regulatory roadmap for addressing the increasing role of AI in finance, focusing on how human involvement can deal with ‘black box’ issues. The paper identifies three regulatory challenges from AI in finance. Firstly, AI algorithms increase information asymmetries between various users and developers. […]