- Ecosystem diversification
- Data scale
- Cloud technologies
- Distribution of niche players
- Personnel transformation
Forecasts from International Data Corporation (IDC) indicate that sales from the worldwide AI industry (including software, hardware, and services) would reach $432.8 billion in 2022, an increase of 19.6% (year over year). In 2023, the market is anticipated to surpass $500 billion.
In 2020, the market for AI in fintech was estimated to be worth $7.91 billion, and by 2026, it is anticipated to be worth $26.67 billion. Additionally, a CAGR of 23.17% is anticipated for the market during the forecast period (2021-2026). The current focus of AI-based business process difficulties includes human empowerment, process optimization, planning, and forecasting, all of which produce better results.
The financial sector has traditionally lagged behind in technology investment, but now financial companies are investing in cognitive technologies more than companies in other industries. According to KPMG, the financial sector’s investments in cognitive technologies amounted to about $10 billion.
AI is transforming the financial services infrastructure, removing market boundaries, and offering new opportunities that may be exploited in unanticipated ways. Let’s pay attention to the main directions of current and future changes.
Financial institutions are already offering new services integrated with current financial products.
For example, the Royal Bank of Canada’s investment in diversifying its digital platform has allowed the bank to expand its service ecosystem. RBC has launched a project for car dealers: a tool to predict car purchase demand based on customer data. By offering this tool and its lending solutions, RBC is incentivizing car dealers to offer RBC lending products more frequently.
Another recent example is from Russia. For customers of the Gazprombank mobile application, the Russian bank Gazprombank and the regional mobile provider GPB Mobile have created a new feature: the operator’s SIM card may now be provided immediately in the bank’s application. The SIM card will be delivered to the client by courier. Additionally, you may access GPB Mobile when you go to a bank branch.
Organizations with developed multi-dimensional ecosystems gain access to large amounts of data, as well as the ability to recognize the necessary behavior patterns, increasing their competitiveness.
For example, Ping An, a Chinese group of companies, has invested in expanding its ecosystem by building a dataset of partners, services, and products in order to reach a huge amount of data beyond financial services. With a set of applications not only in finance, but also in medicine, automobiles, and housing, Ping An can use the data of more than 880 million users, 70 million companies, and 300 partners to manage its core business.
According to the WEF report, future banking technologies are also based on a modular microservice architecture. Both new and existing applications for the financial sector are already moving to cloud and microservice technology.
IDC estimates that by 2022, “hyper-agile” architecture prevails, with 80% of application development performed on cloud platforms using microservices and cloud functions. Cloud infrastructure accounts for a third of all financial services IT spending and is growing at over 20% CAGR. This growth can be explained by the desire of financial institutions to transfer outdated technologies to modern platforms.
Distribution of Niche Players
The AI economy will push market structures to take extreme measures, creating a favorable environment for flexible niche firms and large players at the expense of midsize firms.
The big players have a natural advantage in that they can lower the cost of their services if necessary. This allows them to firmly hold the lead in the fight against medium-sized players. Flexible niche firms will be able to attract customers who are dissatisfied with the service of other companies.
As niche firms and big players increasingly become AI service providers, firms that lack the ability to build such offerings will struggle to compete.
For example, the global Robo Advisory market (consisting of sales of software and related services that provide online financial advice) is expected to grow from $18.71 billion in 2021 to $28.10 billion in 2022 at a CAGR of 50.20%. By 2026, thus, it will already reach $135.11 billion with an average annual growth rate of 48.08%.
The robo-advising sector is being considerably shaped by technological advancements. The efficiency of the robo advisory market is anticipated to rise with the development of technology in fields like advanced analytics, AI in the financial market, and natural language processing. As a result, robo-advisors will be able to increase their effect throughout the value chain and reinforce their value proposition.
In 2021, North America became the largest region in the robotic consultation market. The main players here are Betterment LLC, Charles Schwab & Co. Inc., Wealthfront Corporation, and Personal Capital Corporation.
As the financial sector becomes a consumer of the capabilities of AI service providers, jobs will be created by financial institutions but recreated by service providers, changing the very patterns of behavior. Due to the active integration of processes based on machine learning, new roles will require knowledge in the field of artificial intelligence.
The level of transformation of human activity with the advent of AI is commensurate with the level of transformation that arose after the advent of the Internet and personal computers.
AI, being introduced into all spheres of human activity, changes its architecture. Those who are incapable of such a transformation will be uncompetitive. That is why it is necessary not only to be aware of the advanced opportunities provided by AI but also to implement processes based on it.
The use of AI in the financial industry is constantly expanding, this is an extremely fast-growing area. AI helps make digital transformation simple and secure. By adopting AI technologies, financial institutions can reduce costs through increased productivity, AI algorithms instantly detect anomalies and fraudulent information, improved customer experience, and more.
It is anticipated that processing power, data accessibility, and AI capabilities will all rise quickly in the years to come. Financial institutions need to be prepared not only for the potential risks of AI adoption but also for how it could affect their digitalization strategies as part of their future business models.