Diokno: “The BSP continues to explore ML applications that can be useful in the conduct of its key functions.”
BANGKO SENTRAL NG PILIPINAS — BSP Governor Benjamin Diokno said that the central bank’s big data use will increasingly apply machine learning (ML) applications or techniques such as natural language processing, nowcasting, and banking supervision.
Machine learning, while often interchanged with artificial intelligence (AI), is not AI but rather a subfield of AI involving algorithms in order to deliver output based on patterns learned from data.
During an online press briefing on May 5, the BSP chief said that the central bank continues to explore machine learning apps that can be useful in the conduct of its key functions.
“The BSP continues to explore ML applications that can be useful in the conduct of its key functions while carefully taking note of the associated challenges,” he said.
The BSP chief also noted the continued advances in computing power and increases in digitized information that could provide further impetus for machine learning.
Currently, the central bank, while exploring use cases for machine learning, has noted a number of challenges and limitations with its use.
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He identified one of those challenges as the so-called black-box approach to machine learning.
According to the BSP chief, while empirical studies provide evidence that ML techniques can outperform traditional regression analysis in forecasting, there’s a difficulty in interpreting the causal relationships in ML models.
“By contrast, traditional econometric models allow users to make inferences on causation and identify how an explanatory or independent variable influences the dependent variable. This is critical to economic analysis and policy formulation,” he said.
The central bank is also challenged by ML algorithms and its accuracy in predicting tail risk events. According to Diokno, the use of big data in machine learning also have data privacy, ethical data, and modeling practice issues.
In order to address those challenges, Diokno said that the central bank is modernizing its IT infrastructure and enhancing the skills of its human resources. He added that it has also put in place a data governance policy in order to ensure robust data management within the central bank.
The central bank also employs ML approaches to generate nowcasts of regional inflation and domestic liquidity. Those models, according to BSP, supplement the central bank’s existing suite of models for macroeconomic forecasting.
As for banking supervision, the central bank plans to use ML techniques in order to enhance its data validation processes and to better identify atypical data.
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