Few studies have examined the impact that central bank indirect intervention has on exchange rates. Efficient market theory predicts that new information within central bank communication will become a component of information used by currency traders. This study applies a novel methodology to examine whether information contained within Bank of Canada and the Reserve Bank of Australia communications does in fact get embedded within the information reported on the financial newswires. The primary data are speeches that are made public by the two central banks and from news as reported by Reuters from 1995 to 2009. Applying content analysis and an innovative use of information science theoretic measures, we demonstrate the flow-through of information contained within central bank communications to the information set used by traders.

Traditionally, central banks have used direct intervention in currency markets when the exchange rate has moved away from equilibrium or when the volatility has been excessive and the literature on the effects of indirect intervention is sparse. We examine whether indirect intervention has any impact on the exchange rate levels by examining the central bank verbal communications in Australia and Canada. We find evidence that the Bank of Canada’s (BOC’s) speeches reduce the mean exchange rate returns but not the Reserve Bank of Australia’s (RBA’s) speeches. Our results show that the socio-economic similarities between countries do not guarantee a similar impact of indirect intervention.

The banking, financial services, and insurance (BFSI) sector is one of the earliest and most prominent adopters of artificial intelligence (AI). However, academic research substantially lags behind the adoption of AI in practice. At the beginning of this century, AI research has been centered on the sector's credit risk. In the 2010s decade, expert systems were increasingly replaced by data‐driven, “algorithmic” AI. Big data enjoyed much hype in that decade, which diminished later mostly due to unsuccessful implementations. Much published research on big data actually relates to machine and deep learning but not to big data per se. These terms are often found to be conflated in research and practice. The insurance sector is substantially underrepresented in published AI research, and current research is dominated by banking and investments. Governance frameworks for “responsible AI” (RAI) are yet to be incorporated into practice by fintech companies as well as incumbent organizations. RAI is a particular issue for decentralized finance (DeFi). The most successful implementations of AI in BFSI practice, as well as dominant academic research areas, are in investments, securities, market making, customer relationships, lending, risk management, and compliance.