In an effort to address its historical underestimation of inflation and to refine policy decisions, the European Central Bank (ECB) is delving into the realm of artificial intelligence (AI). This move aligns the ECB with the growing list of organizations harnessing AI for data analysis and decision-making.
For years, the ECB has grappled with underestimating inflation, leading to delays in implementing significant policy adjustments. To tackle this issue, the ECB is now exploring innovative ways to process and analyze an extensive array of data, including publicly available price data, corporate statistics, news articles, and bank supervisory documents. The aim is to obtain more accurate insights to better inform policy decisions.
In a recent blog post, the ECB underscored the potential of AI, stating, “AI offers new ways for us to collect, clean, analyze, and interpret this wealth of available data so that the insights can feed into the work of areas like statistics, risk management, banking supervision, and monetary policy analysis.”
One of the key AI initiatives at the ECB is to deepen its comprehension of price-setting behavior and the dynamics of inflation. Although the bank can amass substantial real-time price data through web scraping, these figures often lack structure and are unsuitable for precise inflation calculations. Consequently, the ECB seeks to leverage AI to structure this data, enhancing its analytical capabilities.
Furthermore, the ECB plans to automate the classification process for data originating from numerous firms, banks, and public sector entities. This automation will enable the bank to gain a more comprehensive understanding of its financial position.
Beyond data analysis, the ECB also aims to streamline its communication efforts. Critics have previously highlighted the bank’s use of overly complex, technical language that can be challenging for the general public to grasp. To address this concern, the ECB intends to employ AI-driven language models to enhance the clarity and accessibility of its communications.
“A large language model can also help improve texts being written by staff members, making the ECB’s communication easier to understand for the public,” noted the bank in its blog post. Additionally, the ECB has been utilizing neural network machine translations to communicate with European citizens in their native languages, further exemplifying its commitment to enhancing communication and transparency.
The ECB’s foray into AI represents a pivotal step toward leveraging cutting-edge technology to bolster its understanding of economic indicators, improve policy decisions, and foster more accessible communication with the public. As AI continues to evolve, it is anticipated that the ECB’s efforts in this field will play a pivotal role in shaping its future policy strategies.