Impact of AI on the economy and financial market infrastructure

ARTIFICIAL intelligence (AI) is making incredible strides worldwide.

Many establishments, governments and organisations are rapidly shifting from analytical AI models designed for specific tasks to generative AI models capable of producing human-like content.

Global literature on AI interest and application development highlights the growing enthusiasm for generative AI, driving its adoption in various sectors.

A review of this literature reveals that a worldwide survey indicates over two-thirds of reputable institutions are utilising generative AI, while nearly three-quarters of organisations have integrated AI into one or more commercial tasks.

With all these endeavours, based on my study of economist Robert Solow’s economic observations on productivity matters, many of us are still in the early phases of integrating AI to realise its full potential.

AI is a transformative force that can be utilised for a wide range of tasks, from mundane and repetitive tasks to knowledge-based and creative work, according to experts in the field.

Like electricity, computers and steam engines, artificial intelligence (AI) is a general-purpose technology that, if wholly automated, can completely transform our economies over time.

Please understand me when I say that information and communication technology (ICT) is revolutionising the economy and our personal lives at the start of the computer era.

Digitalisation is more pervasive in today’s homes, businesses and social lives than in the past.

We have to recall recently, Bank of Tanzania (BoT) Governor Mr Emmanuel Tutuba was amongst other central governors for their 46th annual meeting of the Association of African Central Bank held early last month in Mauritius, where the focus of the conference among the issues was discussion on ways to address various challenges facing central bank with the emerging technological advancement.

When I examined AI from a central bank’s perspective, the theme of this year’s gathering struck me as it focuses on using Big Data Analytics Technology, central bank interest rates and, importantly, artificial intelligence to combat inflation.

Aware of the benefits and risks associated with AI, I couldn’t resist looking at AI’s effects on macroeconomics and how AI could affect monetary policy.

Tanzania’s Governor, Mr Tutuba, recommended a cautious approach to technology use to prevent issues related to data accuracy.

His comments reminded me of a lecture I attended in 2002 while pursuing my PhD in the UK, where I learned about computer-based forensic analysis.

He used to remind us that the saying “garbage in, garbage out” refers to the idea that information or input that is faulty, biased, or of low quality (“garbage”) results in output or result that is also of low quality (“garbage”).

If I understand Mr Tutuba’s reasoning correctly, the mandate is that data input quality must be improved and correct as AI becomes more widespread.

While there is significant attention on how central banks can leverage AI to set interest rates and combat inflation, as well as its implications for financial sector stability and technological development, it is crucial to recognise that AI can also affect the economy in various areas directly relevant to monetary policy.

I’ll address a few key issues and if you’d like, I can provide further details at the appropriate time.

AI can undoubtedly help address the three factors above.

Banks can more effectively plan capital and liquidity and perform risk asessments using AI.

However, there are risks associated with working in this profession and industry for over 18 years.

Widespread AI adoption could elevate risks such as herd behaviour, market correlation, deception, manipulation and conflicts of interest.

If new AI tools become prevalent in the financial system, particularly with a concentration of AI providers, issues like operational risk, market concentration and “too-big-to-fail” externalities could emerge.

Artificial intelligence has various effects on economic cost pressures, including inflation.

Reflecting on Solow’s observational reasoning, if AI leads to labour substitution and productivity gains, the economy may experience a reduced risk of labour shortages and downward pressure on the growth of labour costs per unit.

Similarly, by enabling real-time measurement of customer demand and price elasticities, AI can facilitate discriminatory pricing and lead to increased prices.

In this context, algorithms can effectively learn to implement collusive pricing that exceeds competitive rates, even without direct communication between them.

This is partly because they exploit well-known biases that diverge from logical customer behaviour.

And this can cause someone to make a poor judgement that could have deadly repercussions.

Second, the use of AI should be approached with caution, as it is likely to create new winners and losers in the labour and capital markets, impacting the distribution of wealth and income.

Unwarranted changes in monetary policy can affect people’s access to credit and their marginal propensity to consume, ultimately influencing how demand responds to shifts in monetary policy.

This is especially relevant from an economic perspective. In summary, AI is likely to influence the natural interest rate.

From an economic perspective, the demand for capital to invest in new technologies and expand production capacity may elevate the natural interest rate if AI enhances productivity growth and potential output.

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However, economies could face downward pressure on the natural interest rate if AI leads to increased labour displacement and rising income disparity.

This could result in higher precautionary savings, which would subsequently boost the availability of loanable funds. I could write forever about why we should exercise caution while using AI.

One of the main advantages of human intelligence is its capacity for self-reflection.

As philosopher Immanuel Kant famously noted, we can cognise things a priori only by what we have put into them.

AI lacks this ability to reflect on itself. It cannot also create defences on its own without assistance from human critical thought.

As a result, we must be conscious of AI’s limitations and effects.

The impact of AI on central banks, as well as how the bank’s governors see the technology’s advancements in big data analytics and how they will revolutionise the monitoring of various economic indicators and enable timely and accurate policy decisions, are still up for debate.

As noted by renowned economist Robert Solow, the macro-economic impact of ICT on productivity has not been as significant as might have been predicted, at least outside of the tech sector.

Given the sensitive nature of central banks’ decisions, ensuring secrecy will remain a crucial requirement for the internal application of AI.

With the technological advancements the world is currently witnessing and cyber security crimes around the corner, AI will unquestionably heighten privacy concerns over data use, necessitating the significance of implementing technology and governance controls and strict measures to adhere to rules.