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Pt. 1: Artificial Intelligence in FinTech: How AI & Machine Learning Can Solve Key Challenges

Two computers focus on stocks.

FinTech is broken — and it has been for a while.

While many FinTech companies tout next-level security, the reality is very different.

For instance, in the U.S. — one of the biggest adopters of FinTech — the average data breach cost is $9.44 million. This is nearly twice the global average for a data breach, which sits at $5.09 million.

The U.S. is not alone in its data breach woes, however.

Though the U.S. does lead the pack in terms of costly data breaches, the following countries and regions experience similarly severe breaches:

  • The Middle East: Highest average cost of a data breach is $7.46 million in 2022
  • Canada: Highest average cost of a data breach is $5.64 million in 2022
  • The UK: Highest average cost of a data breach is $5.05 million in 2022
  • Germany: Highest average cost of a data breach is $4.85 million in 2022

These figures show that one of the main problems that many FinTech solutions aim to address — that being cybersecurity — remains one of the biggest digital challenges.

This is in part due to a lack of widespread adoption of FinTech services and solutions within the financial industry. Many traditional institutions feel hesitant toward FinTech partnerships, as they often perceive the companies as competition rather than potential collaborators.

As a result, more and more companies are moving away from the “FinTech” branding, as it carries with it a tumultuous reputation in the larger financial industry.

Keeping all of this in mind, let’s dive into the mystery and madness of FinTech, including what’s going on in the current FinTech industry, particularly with product development.

In this article, I discuss the increasingly vital role played by artificial intelligence (AI) and machine learning (ML) in the FinTech product development space.

What’s Going on in FinTech?

The skyrocketing growth of FinTech over the past few years has been undeniable.

According to the Global FinTech Marketplace Report Analysis 2022, the global FinTech market is expected to reach a value of $225.1 billion by 2027.

The Americas lead the way in FinTech, accounting for 76% of the global FinTech investment market.

Regarding which FinTech products and services are experiencing the most growth, digital payments are the most widely popular solution, accounting for 94% of the global market. Meanwhile, the software segment of the FinTech market currently represents 47% of the total market share.

Yet, despite the overwhelmingly positive market numbers, major challenges are afoot in the industry.

As mentioned earlier, maintaining effective cybersecurity (fraud protection, anti-money laundering, PCI compliance, etc.) is among the top hurdles to overcome. Additional hurdles include a lack of available tech talent (especially regarding mobile applications) and a need for improved user experiences.

With these challenges in mind, we can break down the problems in FinTech into 3 main categories:

  • Human Experience: FinTech and financial service providers have made waves in digitizing the global banking and finance industry. Along the way, however, some technologies and solutions have lost the human touch, interfering with usability and inhibiting a great user experience. FinTech product developers must place a greater emphasis on technologies that help update and improve user interfaces to offer more intuitive experiences.
  • Security Automation: Security challenges are only becoming more complex as new financial technologies are developed every day. To meet this challenge, financial service providers must focus on creating automated security models, programs, and products that can identify and address specific risks autonomously.
  • Staff Support: Finding available talent — especially when it comes to tech professionals — can be extraordinarily difficult for both businesses and financial service providers alike. This means digital solutions must be developed that can help support staff members and clients when using different products, platforms, and other key technologies. 

Is AI the Solution? Key Use Cases for AI & ML in Financial Services 

Artificial intelligence (AI) and machine learning (ML) play a vital role in FinTech. This role spans across entire businesses, from fortifying security systems to assisting in product development and launches.

For example, one use case for AI, machine learning, and automation technologies is to serve as key security components for organizations that can more efficiently contain breaches.

IBM’s Cost of a Data Breach 2022 report states that organizations with fully-developed AI and automation programs could identify and contain data breaches an average of 28 days faster than organizations without such programs.

The result? More than $3.05 million (USD) is saved by these programs.

Additionally, organizations with hybrid cloud models experienced lower average data breach costs overall at $3.8 million. Comparatively, all organizations operating within the cloud, including public and private cloud models, averaged $4.24 in data breach costs in 2021.

A second major use case for AI and ML is the ability to create AI-powered algorithms that can process and analyze massive amounts of data, drawing out meaningful insights much faster than manual data analysis and pattern recognition ever could.

According to McKinsey & Company’s 2021 report Building the AI Bank of the Future, banks should have four distinct technology layers — an engagement layer, an AI-powered decisioning layer, a core technology and data layer, and an operating model layer.

When these layers are optimized to work independently in unison, banks can achieve:

  • Distinctive omnichannel experiences
  • At-scale personalizations
  • Rapid innovation cycles

So far, I have described how AI and ML can be employed to solve two of the three key challenges discussed earlier: security automation and improved human experiences.

But what about the staff support problem?

Working with AI and ML technologies requires a fair amount of tech talent, at least initially.

This is where collaborations between banks and FinTechs become so critical — banks can provide the necessary funding and monetary resources while the financial service provider carries out the development, implementation, and — in some cases — management of the technology.

Moreover, AI-powered customer service models can also help support business teams on the client-facing side. AI programs like chatbots and virtual personal assistants can help direct customers to the products or services they need, as well as answer specific questions and route customers to the right departments for questions in need of a human answer.

One area of FinTech that can particularly benefit from the deployment of AI is product development. Read part 2 to dive into FinTech product development in the financial services marketplace.

Saba Dovlatabadi

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