Archive for November 16th, 2018

Euro at 20: A consortium of West African countries moving towards their own common currency..

November 16, 2018

As Euro celebrates 20 years, we have a currency design competition for another proposed monetary union.

This one is amidst West African countries named Economic Community of West African States.

Basically, in ECOWAS there are two sub-blocks:

  • The West African Economic and Monetary Union (also known by its French-language acronym UEMOA) is an organization of eight, mainly French-speaking, states within the ECOWAS which share a customs union and currency union. Established in 1994 and intended to counterbalance the dominance of English-speaking economies in the bloc (such as Nigeria and Ghana), members of UEMOA are mostly former territories of French West Africa. The currency they all use is the CFA franc, which is pegged to the euro.
  • The West African Monetary Zone (WAMZ), established in 2000, comprises six mainly English-speaking countries within ECOWAS which plan to work towards adopting their own common currency, the eco.

I did post about UEMOA and how France continues to control these countries.

It is WAMZ which is deciding to introduce a common currency. Its members are:

Formed in 2000, the West African Monetary Zone (WAMZ) is a group of six countries within ECOWAS that plan to introduce a common currency called the Eco.[23] The six member states of WAMZ are GambiaGhanaGuineaNigeria and Sierra Leone who founded the organization together in 2000 and Liberia who joined on 16 February 2010. Apart from Guinea, which is Francophone, they are all English-speaking countries. Along with Mauritania, Guinea opted out of the CFA franc currency shared by all other former French colonies in West and Central Africa.

The WAMZ attempts to establish a strong stable currency to rival the CFA franc, whose exchange rate is tied to that of the Euro and is guaranteed by the French Treasury. The eventual goal is for the CFA franc and Eco to merge, giving all of West and Central Africa a single, stable currency. The launch of the new currency is being developed by the West African Monetary Institute based in Accra, Ghana.

The currency’s proposed name is Eco which has similar joining conditions as Euro.

After much dilly dally, action has begun on Eco. Central Bank of Nigeria has floated a competition for the common currency. Apparently, all ECOWAS members have decided to go for this common currency. It even asks for a name for the currency as Eco is not the final name.

Interesting to see this space…


Federal Reserve undergoing changes in banking and monetary policy matters…

November 16, 2018

Several changes underway at Federal Reserve.


What are we learning about Artificial Intelligence in Financial Services?

November 16, 2018

Federal Reserve Governor Lael Brainard takes us through this interesting fascinating topic.

My focus today is the branch of artificial intelligence known as machine learning, which is the basis of many recent advances and commercial applications.2 Modern machine learning applies and refines, or “trains,” a series of algorithms on a large data set by optimizing iteratively as it learns in order to identify patterns and make predictions for new data.3Machine learning essentially imposes much less structure on how data is interpreted compared to conventional approaches in which programmers impose ex ante rule sets to make decisions.

The three key components of AI–algorithms, processing power, and big data–are all increasingly accessible. Due to an early commitment to open-source principles, AI algorithms from some of the largest companies are available to even nascent startups.4 As for processing power, continuing innovation by public cloud providers means that with only a laptop and a credit card, it is possible to tap into some of the world’s most powerful computing systems by paying only for usage time, without having to build out substantial hardware infrastructure. Vendors have made it easy to use these tools for even small businesses and non-technology firms, including in the financial sector. Public cloud companies provide access to pre-trained AI models via developer-friendly application programming interfaces or even “drop and drag” tools for creating sophisticated AI models.5 Most notably, the world is creating data to feed those models at an ever-increasing rate. Whereas in 2013 it was estimated that 90 percent of the world’s data had been created in the prior two years, by 2016, IBM estimated that 90 percent of global data had been created in the prior year alone.6

The pace and ubiquity of AI innovation have surprised even experts. The best AI result on a popular image recognition challenge improved from a 26 percent error rate to 3.5 percent in just four years. That is lower than the human error rate of 5 percent.7 In one study, a combination AI-human approach brought the error rate down even further–to 0.5 percent.

So it is no surprise that many financial services firms are devoting so much money, attention, and time to developing and using AI approaches. Broadly, there is particular interest in at least five capabilities.8 First, firms view AI approaches as potentially having superior ability for pattern recognition, such as identifying relationships among variables that are not intuitive or not revealed by more traditional modeling. Second, firms see potential cost efficiencies where AI approaches may be able to arrive at outcomes more cheaply with no reduction in performance. Third, AI approaches might have greater accuracy in processing because of their greater automation compared to approaches that have more human input and higher “operator error.” Fourth, firms may see better predictive power with AI compared to more traditional approaches–for instance, in improving investment performance or expanding credit access. Finally, AI approaches are better than conventional approaches at accommodating very large and less-structured data sets and processing those data more efficiently and effectively. Some machine learning approaches can be “let loose” on data sets to identify patterns or develop predictions without the need to specify a functional form ex ante.

What do those capabilities mean in terms of how we bank? The Financial Stability Board highlighted four areas where AI could impact banking.9 First, customer-facing uses could combine expanded consumer data sets with new algorithms to assess credit quality or price insurance policies. And chatbots could provide help and even financial advice to consumers, saving them the waiting time to speak with a live operator. Second, there is the potential for strengthening back-office operations, such as advanced models for capital optimization, model risk management, stress testing, and market impact analysis. Third, AI approaches could be applied to trading and investment strategies, from identifying new signals on price movements to using past trading behavior to anticipate a client’s next order. Finally, there are likely to be AI advancements in compliance and risk mitigation by banks. AI solutions are already being used by some firms in areas like fraud detection, capital optimization, and portfolio management.


One believes that sooner than later we will either have technologists in top management at central banks (even banks) or the top management will have to undergo rigorous tech training. This is no more science fiction but day light reality.

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