The financial services landscape is rapidly changing in the wake of increasing artificial intelligence (AI) innovations. Big banks, hindered by scale and regulations, must find a way to keep up with the pace of AI adoption or risk the consequences of falling behind nimble fintechs.

It’s not about investment. The financial services industry invested an estimated $35bn in AI last year, led by banking’s $21bn of investment, according to a recent McKinsey report.

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It’s about agility. Firms must implement innovations in ways that give them an edge over competitors.

Why banks need change now

Despite their established presence and vast resources, banks are being outpaced by fintechs that are using AI to deliver faster, cheaper, and more user-friendly services.

According to a recent study on the adoption of AI in financial services by Money 20/20 and Acrew Capital, 76% of firms have announced an AI initiative, and most of the survey leaders are fintech companies.

However, there are exceptions within the banking world. JPMorgan, American Express, Mastercard and Mizuho all showed competitive AI strength in the survey.

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But fintechs are challenging the traditional banking model, and consumer expectations have shifted as a result.

A Consumer Affairs report found that 71% of adults now bank online or via their phone, driven by convivence and accessibility. This gives non-traditional financial services a wide opening to upend traditional banking. AI could add fuel to this shift.

Banks must embrace AI quickly to help meet these new demands and avoid falling behind. This means investing in the technology to create a culture of innovation and “AI-first” thinking within their organisations.

Regulatory challenges

One of the biggest hurdles for banks is navigating the complex regulatory environment. Compliance requirements slow down their ability to innovate, unlike fintechs, which often operate with fewer constraints and even fewer restrictions in most regions.

Banks are heavily regulated and subject to strict compliance requirements from government bodies like the Federal Reserve in the US and the European Banking Authority (EBA) in the European Union. The recent EU AI Act, for example, lays the foundations for AI regulation, trying to balance innovation and public safety.

AI also can help banks meet this regulatory challenge. The technology is already being used by many financial services companies to help them understand and navigate regulatory hurdles – which traditionally was a resource and expert-heavy undertaking. By automating compliance processes and monitoring regulatory changes in real time, AI can potentially enable banks to stay ahead of regulatory requirements efficiently. Apart from reducing the burden on compliance teams, it also frees up resources to focus on other, more people-focused tasks.

Strategic implementation

Adopting AI at scale requires a well-thought-out strategic approach that encompasses multiple facets of the banking operation.

This includes creating a clear and detailed strategic roadmap that outlines the steps and milestones needed to successfully integrate AI technologies across various departments. Attracting and retaining talent is also critical, as skilled professionals with expertise in AI and data analytics are essential for developing and maintaining sophisticated AI systems. Examining and updating risk management systems is another important aspect, helping guarantee that the implementation of AI does not introduce new vulnerabilities or exacerbate existing ones. Additionally, developing a comprehensive change management plan is key to helping guide the organisation through the transition, addressing potential resistance and ensuring that all employees are on board with AI and all the new processes that come with it.

These steps are vital for banks aiming to transition into “AI-first” institutions. Banks need to carefully choose an operating model that aligns with their organisational structure and culture. A centralised operating model can help focus resources on key use cases and facilitate rapid progression from experimentation to production, ensuring successful integration of new AI technologies.

Learning from fintechs

Fintechs have successfully used AI to improve fraud detection, customer service, and investment management.

A good example is the online lender Upstart. The firm is already using AI to power its products and investors have responded, pushing up its stock price.

Here are several areas where fintechs have shown that AI can have a significant impact on financial services:

  • Cybersecurity and Fraud Detection: AI algorithms can identify fraudulent behavior in real-time, allowing banks to proactively address threats.
  • Credit Scoring: AI provides more accurate and fair credit assessments by analysing diverse data sources.
  • Customer Service: AI-powered chatbots and virtual assistants offer instant support, enhancing customer experiences.
  • Personalised Services: AI enables banks to tailor financial advice and product recommendations based on individual behaviour.
  • Algorithmic Trading: AI optimises trading strategies and market analyses, reducing reliance on human intuition.
  • Regulatory Compliance: AI streamlines compliance processes, ensuring banks adhere to regulations more efficiently.

Adoption at scale

The benefits of AI, like better data insights, fraud prevention, and improved efficiency, outweigh the risks. Banks are at a pivotal moment. To succeed, they need to embrace these changes as AI presents a transformative opportunity for the banking sector. It has the potential to add between $200bn and $340bn annually to industry revenues through increased productivity.

To fully capitalise on benefits like superior customer service, greater agent productivity, and more efficient software development, traditional banks must embrace AI quickly and strategically. It’s then that they will improve operations and meet evolving consumer demands.

Ryan Cox is Head of AI at Synechron