and algorithmic trading, further diversifying financial intermediation. in the field of fintech, the role of it firms and startups has grown, as they harness new information technologies for financial innovation. a notable example is decentralized finance ( defi ), where automated systems provide efficient financial services without the need for a central administrator. the financial industry is set to undergo even more transformation with the recent rise of generative ai. while still in its early stages, institutions in japan and europe are making strides in leveraging this technology. for example, generative ai has been used to improve internal processes, such as minute - taking, document summarization, translations, and compliance checks. in japan, it is also seen as a valuable tool to mitigate labor shortages. additionally, generative ai is beginning to support customer data analysis, transaction monitoring for fraud detection, and other critical operations, all contributing to enhanced profitability and productivity within the financial sector. so far, we have seen how technology has diversified financial intermediation. the reverse is also true : financial intermediation supports technology by providing essential investment for r & d and commercialization. financial entities, not just banks, but also venture capital and private equity and debt funds now play a significant role in fostering innovation. they provide funding across a wide spectrum of industries and risk profiles, creating a positive feedback loop between financial intermediation and technological advancement, essential for economic growth and productivity enhancement, in both japan and europe. ii. challenges for financial intermediation functions it is necessary to keep in mind, however, that technological advancements bring new risks to financial stability. for example, during the banking turmoil of march 2023, we saw how concerns over a bank's credit status could spread rapidly, leading to accelerated deposit withdrawals due to online banking and social media. as financial services grow more diverse and complex, the channels of risk transmission have become less transparent, and current financial regulations may not be fully equipped to manage new types of financial services. this environment underscores the need for operational resilience, including robust management of cybersecurity and third - party risks. with the rise of generative ai, there are also specific challenges, including the risk of " hallucinations " ( that is, non - factual responses ), black box issues, and data protection concerns. it is crucial that central banks and other authorities monitor these evolving financial intermediation functions, encourage relevant entities to establish

ministry of internal affairs and communications.. chart 5 cpi y / y % chg. 2. 0 all items less fresh food ( core cpi ) 1. 5 all items less fresh food and energy 1. 0 0. 5 0. 0 - 0. 5 - 1. 0 cy 2013 / apr. oct. 14 / apr. oct. 15 / apr. oct. 16 / apr. oct. 17 / apr. oct. 18 / apr. oct. 19 / apr. oct. 20 / apr. source : ministry of internal affairs and communications. note : figures exclude the effects of the consumption tax hike in april 2014. chart 6 outlook for economic activity and prices as of july 2020 forecasts of the majority of policy board members y / y % chg. real gdp cpi ( all items less fresh food ) - 5. 7 to - 4. 5 - 0. 6 to - 0. 4 [ - 4. 7 ] [ - 0. 5 ] - 5. 0 to - 3. 0 - 0. 7 to - 0. 3 + 3. 0 to + 4. 0 + 0. 2 to + 0. 5 [ + 3. 3 ] [ + 0. 3 ] + 2. 8 to + 3. 9 0. 0 to + 0. 7 + 1. 3 to + 1. 6 + 0. 5 to + 0. 8 [ + 1. 5 ] [ + 0. 7 ] + 0. 8 to + 1. 6 + 0. 4 to + 1. 0 fiscal 2020 forecasts made in april 2020 fiscal 2021 forecasts made in april 2020 fiscal 2022 forecasts made in april 2020 source : july 2020 outlook for economic activity and prices ( outlook report ), bank of japan. notes : 1. figures in brackets indicate the medians of the policy board members'forecasts ( point estimates ). 2. the forecasts of the majority of the policy board members are constructed as follows : each policy board member's forecast takes the form of a point estimate - - namely, the figure to which he or she attaches the highest probability of realization. these forecasts are then shown as a range, with the highest figure and the lowest figure excluded. the range does not indicate the forecast errors. 3. in the april outlook report, each policy board member made their forecasts as a range and submitted two figures ( i. e., the highest and lowest figures ) within the range of 1. 0 percentage