Huge advances in computer hardware, software and Internet technologies have irreversibly transformed our societies. It is now difficult to imagine an economic agent without computers, the Internet or mobile devices. The speed at which it is developing provides great opportunities to expand the customer base, introduce new products or improve existing ones, and increase efficiency in a relatively short time. On the other hand, if companies miss out on the current IT wave, they may soon be overtaken by events. Among the various IT breakthroughs of recent years, advances in AI are particularly noteworthy. In short, AI refers to computers that have cognitive skills similar to those of humans, which can result in efficiency gains for firms and their customers alike. The financial sector has been one of the early experimenters with AI technologies, not least because of its potential contribution to stronger profitability. It is therefore necessary to take a closer look at the potential role of AI in the digital transformation of banks.
Artificial Intelligence: A Giant Step Beyond Standard IT Applications
To date, IT solutions in the business world have focused on automating repetitive tasks that would otherwise require human involvement. The limits for these IT applications have been set by their developers, and by design these solutions have been limited in their capabilities. They have been largely static and unable to understand or act on their own. With technology rapidly evolving, however, this is rapidly changing. https://www.ibm.com/think/topics/ai-in-banking
Artificial Intelligence is the ability and power of various types of computer programs to learn and apply different kinds of knowledge without human intervention and interaction. By observing the world around them and analyzing the data by themselves, AI systems draw conclusions and appropriate responses. They learn from their previous decisions and, depending on the level of accuracy, they improve their performance over time.
Artificial Intelligence as a concept was initially coined in 19561 at the Dartmouth Conference and thus is not very recent. Of late, some recent developments in it have allowed such acceleration of its potential:
i) The rise in employment of the Internet has led to the creation and storage of huge amounts of digital data. In the space of some 10 years, the amount of data generated worldwide increased some 17-fold. Estimates suggest an additional five-fold expansion for 2025. This vast resource of information, which has been tidied up and sorted (i.e. Big Data), lies at the origins of data-led decision making.
ii) There has been a vast increase in computer processing power. One of the measures of that, the number of transistors, has increased 10m times since the 1970s. The speed of central processing units, another measure of processing power, increased by a factor of 6,750 over the same period. This is to say that algorithms are able to process information much faster and that helps in the accuracy of their decision making. https://intellipaat.com/blog/artificial-intelligence-in-banking/
ii) There has been a vast increase in the processing power of computers. One of the standard measures of that, the number of transistors, has increased 10m times since the 1970s. The speed of the central processing unit, another component that contributes to processing power, increased by a factor of 6,750 over the same period. This enables algorithms to handle information much faster and that also improves their decision making.
iii) Other innovations – for example, falling data storage costs, advances in data mining processes or an increase in the number of IT specialists – have also increased the viability and scale of AI. While the cost of hard drives per gigabyte has fallen from around USD 5,000 in 1990 to some USD 0.025, for example, the number of IT professionals in the Eurozone has increased by 50% between 2007-2017.
Big data as input, data identification methods such as machine learning and the greater affordability of these tools have been the driving factors in AI’s recent rapid successes in understanding languages, recognising objects and sounds, and autonomously perceiving and solving problems. https://www.geeksforgeeks.org/ai-in-banking/
Artificial Intelligence Investments on the Rise
Thanks to its rapid development in recent years, AI is being experimented with and implemented in many sectors. Due to measurement issues, however, quantifying its deployment is hardly a straightforward task. In fact, firms may deploy AI to increase efficiency in their processes, which is not directly observable for analysis. Furthermore, it is sometimes difficult to distinguish between more standard IT solutions and stand-alone AI applications. To partially overcome these shortcomings, information on venture capital (VC) investments in AI start-up firms can be useful. In 2018, AI start-ups received a staggering USD 24 bn in funding globally, up from less than USD 2 bn in 2013. The growth in VC investment has been particularly strong in the past two to three years. AI firms have also increasingly become acquisition targets. Over the past 20 years, a total of 434 companies in the AI sector have been acquired, 220 of them since 2016 alone.
Of the total VC volume in 2018, nearly USD 15 bn went to AI start-ups in the US, and another USD 6.5 bn went to Chinese firms. In 2017 and 2018, the number of VC deals flattened. Yet the average volume of VC investments rose, a sign of VCs flowing into more mature AI firms, whose capital requirements are larger than those of typical seed stage start-ups. For example, in China, Sensitime Group, a computer vision and deep learning technology developer, raised USD 1.6 bn in VC funding in 2018. With the new capital, the company’s value rose to over 6 WAS BN, making it the world’s most valuable AI unicorn. Meanwhile, it is mainly large tech firms that invest in AI start-ups.
For VC investors, AI appears to be a truly transformative technology with significant potential, just like the internet and mobile revolutions in the past decades. How do AI start-ups use the money they receive? First observations indicate that they tend to find new AI talent (which proves to be expensive and difficult) and expand their services. So investors may need to wait a while before they see a meaningful return on their investment.
Artificial Intelligence and Intellectual Property Rights
A technological sector is usually more useful and has more value to the economy in the coming years if the number of patents filed in this particular sector increases substantially. In 2016 there were some 20,000 patent applications in AI-related technologies, double the 2010 figure. About 50% were accounted for by AI patents in computer vision (this technology is mostly used in self-driving cars and shows how intense competition is with respect to countries in this field), the US accounted for about a third, a more or less stable share since 2010. Within the US, it was the tech giants that filed the largest number of AI patents. China made up more than 10% of the applications in 2015, up from 2010. Japan and the EU-28 each had a share of 14%, both down from around 20%. China is rapidly replacing the EU and Japan in AI research and development, with potentially significant implications for the future.
Within the EU, half of all AI patent applications originated in Germany and France. Four countries account for the lion’s share, along with the UK (16%) and Sweden (8%). Given that patents create a legal monopoly, they introduce significant first-mover advantages. Taking into account the potentially huge economies of scale, countries unable to implement AI now may be at risk of falling behind in the long term.
In light of the intense global competition in IT in general and AI in particular, the European Commission proposed in March 2019 a budget to fund research and innovation projects in Europe. Horizon Europe is the successor to Horizon 2020, with a volume of EUR 77 BN between 2014 and 2020. EUR 9 BN to be invested specifically in high-performance computing and data, AI, cybersecurity and advanced digital skills projects. Even though Horizon Europe represents an important step in scaling up AI technology in Europe, its ability to drive successful AI projects remains to be seen. Indeed, its predecessor received 115,000 innovation and research proposals between 2014 and 2016, yet only 14,000 proposals were selected for funding, a very low success rate. The high rate of over-subscription is evidence of strong demand for funding. But the large number of rejected applications points to some underlying problems. Alternative solutions, such as increasing IT literacy at early ages or improving IT infrastructure, may be needed to increase the number of high-quality AI and innovation projects.
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