The State of the AI & ML Market in 2022
Let’s examine the current state of the AI and ML market, including market growth insights and the industries most commonly leveraging the technologies.
According to Fortune Business Insights, the global AI market is projected to reach a value of more than $387 billion (USD) in 2022. By 2029, the market is expected to reach a value of more than $1,394 billion, growing at a compound annual growth rate (CAGR) of 20.1%.
The Fortune Business Insights report further reveals that AI investments in the healthcare industry have been a driving factor in the market’s global growth, accounting for a 150% increase in 2020. The growing use of AI in healthcare throughout 2020 was primarily catalyzed by the Covid-19 pandemic, which pushed many healthcare providers to invest in remote and digital alternatives to in-person care.
Additional key findings in this report include:
- North America has been one of the biggest contributors to AI market growth, generating more than $143 billion in annual market revenue in 2021.
- The Asia-Pacific region is expected to experience the fastest growth of the AI market from 2022 to 2029, with this growth driven forward by 5G deployment in China, Japan, South Korea, India, and Singapore.
- In Europe, the AI market is expected to experience accelerated growth due to increased federal funding.
- AI is used globally for many different business tasks, including marketing and sales, human resources, product/service deployment, risk management, service operation, and supply chain management.
- Several industries make up the market share of AI, including healthcare, IT and telecom, retail, BFSI (banking, financial services, and insurance), advertising and media, automotive, and manufacturing.
How are AI & ML Currently Used in Financial Services?
The BFSI industry is one of the main players in the AI and ML market.
According to market research from March 2022, the market size for AI in the BFSI industry was worth approximately $15 million (USD) in 2021, with an expected CAGR of 35% from 2022 to 2028. By 2028, the AI market in BFSI is projected to reach a value of approximately $140 billion.
As of 2022, the report states that the top use case for AI in the BFSI industry is developing, implementing, and managing improved Customer Relationship Management (CRM) solutions. Through the use of AI, BFSI institutions and businesses can gain a deeper understanding of customers.
In turn, this improved understanding of customers can assist BSFI organizations in product segmentation, marketing campaign management, appropriate targeting, maintaining long-term profitability, and fostering mutually-beneficial relationships with customers.
Let’s take a look at more essential findings from this research report that highlights how AI is used in financial services around the globe:
- Machine learning accounted for 35% of the AI market share in Mexico, largely due to Mexico’s need to develop better algorithms to protect against money laundering.
- The AI market in the U.S. BFSI industry is expected to focus on the financial advisory segment, with a 35% CAGR through 2028 for ML investment algorithms.
- In the UK, the banking segment is employing AI to increase operational efficiency and improve customer experiences, generating more than $400 million (USD) in revenue in 2021.
- The European region accounted for nearly 20% of the AI market share in the BFSI industry in 2021, with key uses of the technology including increased digitalization and the development of novel technologies like robotic process automation.
- The global AI industry is characterized by heavy competition between different players in the BFSI space, with a special emphasis on the development and release of innovative products with enhanced features.
Overall, it is clear that there are many different ways AI and ML can be employed within financial services, making the technology a worthwhile investment for institutions and businesses of all kinds.
From these findings, there are 5 identifiable use cases for AI in the financial services industry:
- Improving anti money laundering (AML) models
- Personalizing financial advisory tools
- Increasing operational efficiency
- Creating better customer experiences
- Developing innovative products and technologies
With this in mind, I’ll now hone in on AI and ML in product development processes to develop and launch new products with enhanced features.
Enhancing Product Development in Financial Services with AI & ML
When it comes to product development, AI and ML technologies offer powerful capabilities.
In The State of AI in 2021 — a McKinsey & Company survey report — product development was identified as one of the top use cases for AI technology. The survey revealed that the top functions AI is employed for in product development include:
- AI-based product enhancements
- Product-feature optimization
- Creation of new AI-based products
The other top use cases highlighted in this report include service operations, marketing and sales, and risk management. Among these use cases, some of the top functions include optimizing service operations, automating contact centers, enabling predictive services, and utilizing advanced data analytics (for customer service, risk modeling, and fraud purposes).
Here are 3 ways AI and ML can enhance product development processes for financial service providers:
Increased Customer Centricity
One of the most essential functions that AI and ML offer financial service providers (FSPs) is the opportunity to identify products and services with high profitability and purchase potential according to specific customer segments.
Using AI to leverage advanced data analytics enables businesses and institutions to better understand customers, including their risk profiles, financial behaviors, and purchasing preferences.
In turn, this data can then be used in product development to create clear customer segments, allowing an FSP to tailor and personalize products through the use of AI and ML algorithms.
Moreover, AI and ML can be leveraged by FSPs to incorporate customer feedback after a product has already been launched. On the back end of product development, AI and ML programs can be used to optimize product testing activities, making it easier to identify problems and reduce human errors.
Improved Investment & Trading Models
Along with offering powerful data capabilities for gaining a better understanding of customers and customer segments, FSPs can also improve investment market predictions using AI and ML algorithms.
Product development for investment and trading platforms allows for automated data collection. As a result, FSPs can both gather much larger amounts of data and analyze this data for valuable market insights that can be used to make more accurate market predictions.
For example, AI and ML can be used to facilitate more accurate decision making by traders through the analysis of real-time data. Additionally, FSPs can employ AI to deal with mundane contract and agreement tasks that have historically been manual activities, freeing up more time for staff members to focus on high-value customer support and new business opportunities.
Continuous Development of Payment Services
Payment services have become one of the largest segments in the global FinTech industry.
In the previous article, I discussed how digital payments account for 94% of the global FinTech investment market share. AI and ML help to facilitate these digital payment services by improving payment processing efficiency, reducing the need for human intervention, and continuously improving the payment service system according to market adjustments and changes.
A great example of this is the growing popularity of alternative payment methods, many of which require AI-powered tools to be integrated into a larger payment system. Not only can AI help integrate these methods, but it also helps FSPs assess how payments are being used around the globe.
As a result, FSPs can enable continuous development of payment platforms based on market and consumer data gathered and assessed by AI solutions.
Final Thoughts: Meeting the Challenge of Compliance with AI & ML
Many financial services companies have already begun their journeys to building robust platforms that utilize artificial intelligence and machine learning.
Unfortunately, a major hurdle that many are encountering is how to properly employ AI and ML in a way that does not dramatically interfere with compliance.
By trying to expand a financial services platform to offer every product and service under the sun, an FSP spreads its resources and staff thin. In turn, AI and ML technologies are deployed quickly and widely for product development without enough consideration into how these products can be made both to be both compliant and highly user-friendly.
While the acceleration of the AI market in financial services is rapid, we need to slow down and move backward to ensure the resulting products are not only compliant but also sensible. Customers and staff members alike need to be able to use these products with a clear understanding of how they work and the product or technology’s role in the company framework.
Overall, the most essential step moving forward is to ensure AI and ML technologies are not broadly used for every possible task. Instead, these technologies should be utilized to create highly tailored and specific solutions to the biggest problems within each financial services institution or business.
The FinTech industry is booming, enabling its assortment of products and services to become one of the fastest-growing markets in the whole world.
Yet, the FinTech industry is not free of downsides, with security and usability being major pain points.
Artificial intelligence and machine learning offer the FinTech industry and financial services providers the opportunity to enhance their business models, optimize product development, and improve the customer experience, all while saving thousands — or even millions — on operational expenses.
This leads us to the question — what is the future of AI and ML in FinTech?
The answer is neither short nor simple.
AI technologies are complex in their own right, requiring the proper professional talent and implementation to be truly effective.
As a result, financial services providers or other businesses in the FinTech space must consider how to best leverage these technologies with their respective resources — and much like how the FinTech industry must focus more heavily on security and protecting consumers, it must also focus on utilizing AI to build, implement, and manage compliant products.