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Information is key to making the right decisions and as in other industries, in banking, the available data could become overwhelming. To overcome such a potential pitfall, the Philippines’ central bank is exploring Artificial Intelligence and Machine Learning (ML) to enhance its operations in the process.

The country’s central bank, Bangko Sentral ng Pilipinas (BSP), is exploring applications of machine learning (ML) techniques, particularly in the areas of natural language processing, nowcasting, and banking supervision.

Central banks’ interest in ML has been increasing over the years, mainly due to its potential to enhance the existing tools used for regular monitoring as well as its ability to uncover underlying relationships between data to better understand the economy and the financial system.

– Benjamin Diokno, Governor, Bangko Sentral ng Pilipinas

ML is very useful in extracting patterns in sets of data. In that sense, it can extract knowledge from raw data. At its core, machine learning is a subfield of AI that involves algorithms that deliver output based on patterns learned from data. Natural language processing at the BSP is used to convert text into data to produce a quantitative summary. A good example of this is the news sentiment index and economic policy uncertainty index that is currently being developed.

The BSP also employs ML approaches to generate nowcasts of regional inflation and domestic liquidity. These models supplement the BSP’s existing suite of models for macroeconomic forecasting.

Nowcasting may sound foreign to non-bankers but the word, one borrowed from weather forecasters, means the prediction of possible events from raw data. These events could be in the present, the very near future, and the very recent past state of an economic indicator.

In banking supervision, the BSP aims to utilise ML techniques to enhance its data validation processes and better identify atypical data. Diokno said ML offers diverse opportunities in central banking, especially when combined with techniques from other disciplines, such as econometrics and network science.

He also highlighted several challenges associated with ML processes. The most often-cited limitation is the black-box approach to ML, which could result in difficulties in interpreting the causal relationships in ML models. Like traditional econometric techniques, ML algorithms may also encounter some challenges in accurately predicting tail risk or low likelihood events. The adoption of ML models would also necessitate investments in Information Technology (IT) infrastructure and capacity building, as well as a change in the organisational mindset.

As the country builds a stronger and more technologically advanced Philippine economy, the BSP will continue to explore ML applications that can be useful in the conduct of its key functions, while carefully taking note of the associated challenges.

The country, an archipelago of over 7,000 islands, is looking at space technology to enhance people’s ability to connect to the internet. Recently, all space-related assets were turned over to the national space agency.

Indeed, it is important that the country invest heavily in its digitalisation. As one industry expert noted, technology is key for the country to become a high-income nation as reported on OpenGov Asia.

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