Monetary policy operations were made more market based and active, especially after the introduction of the independent float of the exchange rate in 2001. Monetary policy has concentrated on stability, while promoting economic growth. However, monetary expansion was somewhat above the expected path in 2004. The relatively higher monetary expansion during 2004 was mainly driven by the higher expansion in credit to the private sector as well as to the public sector. Responding to these developments, the Central Bank tightened its monetary policy by raising policy rates in the fourth quarter of 2004. The policy focus was on market stability in the immediate aftermath of the tsunami. Subsequently, after ensuring market stability, the Central Bank has conducted open market operations aggressively to mop up excess liquidity. The Central Bank will continue to mop up liquidity in the coming months. Monetary growth is expected to be contained to 15 per cent in 2005. Monetary policy will focus on restraining inflationary pressure. An area that has experienced rapid growth since liberalisation is the financial sector. This has not only contributed to economic growth in its own right, but has also facilitated growth in other sectors. The Central Bank has actively promoted financial sector stability and development by assisting in the deepening of markets, creation of new instruments and strengthening of the regulatory framework. Prospects and challenges The foregoing forms the background for the economic policies of the government. I would now like to highlight several challenges that have to be faced.
Allow me to return to the first topic of the day and say a few considerations about Artificial Intelligence, Machine Learning and Big Data. The interconnected use of new technologies is 1/2  BIS central bankers' speeches  already a common practice in many operational fields, such as the defense industry, the automotive industry or the healthcare sector. It is, therefore, natural that new technologies would penetrate the financial sector. Their applications are diversified in design and scope, examples of which are: algorithmic trading, portfolio optimization, scoring and pricing models. Moreover, the use of these new technologies receives more and more attention in the financial and economic areas, as the transition from classical banking sector products and services is growing exponentially. In this context, the focus moves from the individual to more general issues and the question arises about the effects that the trinity described by artificial intelligence, machine learning and big data analytics, have on the financial system. Implicit answers to this question are formulated as considerations of financial stability. In other words, the key question is related to the costs and benefits that the new technologies listed above have on financial stability. The answers to this question are quite varied, given that this issue has not been thoroughly investigated up to this moment. Among the best-known benefits, some of the areas where progress can be made by using the specified technologies are: regulatory compliance, systemic risk monitoring, fraud detection and supervisory effectiveness.