AI helps clients of Japan’s Jibun Bank make use of automatic foreign currency deposits

Maria Nikolova

An AI system will alert the savings accounts’ owners of the moment when it is best to make a deposit in foreign currencies.

Clients of Japan’s Jubun Bank, a joint venture of Bank of Tokyo-Mitsubishi UFJ, Ltd. and KDDI, now have access to a new service harnessing the capabilities of artificial intelligence (AI). The new solution, developed jointly with fintech firm Alpaca, targets those who would like to make use of foreign currency deposits and would like to do so at the right moment, that is, when the exchange rates are most favourable.

The new AI-based solution aims to determine when the exchange rates are best and alerts the customer about it. Based on account settings pre-defined by the customer, the AI system makes a deposit from one’s JPY account to one’s foreign currency savings account. As per the default settings, the system will make such a transfer automatically once a month, when the AI engine determines that the rates are the best. The customers have the right the determine upper and lower limits for the exchange rates they deem acceptable.

The new service works with several foreign currencies, including USD, EUR and AUD.

This is not the first time that Alpaca partners with Jibun bank. Back in June 2017, the companies released released “FX forecast AI” for Jibun Bank’s smartphone application.

And in the spring of 2017, online brokerage Kabu.com Securities, a subsidiary of Mitsubishi UFJ Financial Group Inc (TYO:8306), announced that it will launch a special AI-powered solution for its clients. The solution, developed together with Alpaca, targets stock traders.

The AI system scans the share prices of Japan-listed companies in order to identify shares exhibiting similar trends. It detects correlations that human traders cannot usually notice and thus signals trading opportunities. In addition, the product identifies overvalued and undervalued stocks by identifying correlating stock charts. These findings are used for a trading strategy where investors go long on the underperforming shares and go short on the overperforming shares in a correlated pair.

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