‘AI-powered’ — the buzzworthy phrase throughout the tech world for 2024.

Every new company or new product last year showcased a feature or program enhanced by artificial intelligence.

What does AI-powered mean? And more importantly, is its impact ‘real’ for the financial services industry (providers and customers)?

For those familiar with financial technology (FinTech) innovation, 2025 is expected to usher in a dynamic pace of innovation enhanced by AI.

No longer speculative hype, artificial intelligence forms a foundational layer for the next wave of industry growth — impacting the way the financial institutions and banking tech providers manage customer relationships, facilitate communications, and scale performance to new heights.

We’ve covered AI’s growing influence in finance since early 2018. In this article (first of a 4-part series), let’s focus on AI in Financial Services for 2025 (TL;DR):

  • What’s driving AI adoption forward across all tech communities;

  • Key benefits of artificial intelligence in financial services;

  • Specific applications for AI in FinTech;

  • Challenges in applying AI;

[Links to the 3 other parts will be added at the end of this article as soon as they’re published in the next week!]

The Forces Fueling AI Adoption

The adoption of artificial intelligence across tech sectors is no a longer a pipe dream — it’s a ‘work-in-progress’ based on a mashup of innovation and market forces.

We slowly observe this daily in email, social media, and workplace apps. There are options to summarize information from documents, draft responses to emails, and research — embedded into existing user experiences. The demand for speed, convenience, and insights from volumes of data is higher than ever.

In 2025, the most dynamic forces fueling AI integration globally are:

  • Changing Consumer Expectations: Today’s customers expect fast, seamless, and personalized services.

    • For financial services, we see this expectation in mobile banking experiences and recommendations from robo-advisors; through AI, financial institutions & fintechs can anticipate client needs, provide real-time advice, and build hyper-personalized offers that deepen customer loyalty.

  • Operational Efficiency: Automation of tasks and optimizing human capacity is a core value from AI, which is a huge painpoint for financial services (and other process-intensive industries).

    • Within banking, ops improvements minimize inefficiencies and costly overhead, while updating legacy workflows. Activities such as due diligence verification, fraud monitoring, and risk scoring are now automated in minutes.

  • Competitive Pressure: New technology often leads to a race within market players of who will create & deliver value-add solutions the fastest.

    • For financial institutions, implementing tech and change management efforts are typically slow and lengthy. There’s pressure coming from enterprise fintechs & large tech firms featuring banking enhanced by AI.

  • Regulatory Compliance: The future holds more regulation for companies of all sizes — for global firms, the bar is higher to ensure adherence in multiple countries/regions at the same time.

    • New AI solutions can monitor activity for existing laws & regulatory guidance, AND make adjustments in real-time. This ensures proper reporting for regulators and less risk of penalties.

The collective pressure from these market forces makes it extremely challenging for organizations to sit on the sidelines (regardless of vertical, company size, or location).

On the plus side, the impact from artificial intelligence brings multiple benefits — let’s cover what this looks like for the financial services industry.

Benefits of Implementing AI

Industries are being pressured to adopt artificial intelligence in the next 12 months, but what are the benefits in doing so?

We spoke broadly in the context of market forces above — here’s a specific list that aligns with the financial services industry:

  • Increased accuracy improves decision-making: Automation from AI reduces the reliance on human review, thus lowering the chance for errors. Decisioning is based on large volumes of data (instead of bias or intuition), which creates consistent evaluations.

  • Cost savings from personnel reduction: By reducing manual reviews, staffing can now focus on improving models & processes. It’s estimated that 1 of 5 hires can be reduced through proper integration of AI solutions at financial institutions.

  • Better cybersecurity: For banks and fintechs, staying ahead of new threats from bad actors is a never-ending battle. Data can be fed into AI-powered models in real-time to help identify and prevent fraudsters employing sophisticated attacks (e.g. mixed threat of deep fakes, malware, smishing, and phishing).

  • Improved scalability: Organizations (especially those handling large volumes of users and/or transactions) need solutions that can quickly scale without deteriorating performance. Artificial intelligence provides premium capacity management by managing greater workloads with minimal add-on costs.

At a high-level, AI makes complete sense within financial services — especially as real-time transacting, money movement, and banking requests are in massive demand.

Let’s dive deeper into these benefits to identify strong applications of AI taking place today.

Top Areas to Apply AI in Financial Services

The market forces and benefits in adopting artificial intelligence make sense, but how does this actually apply to innovating the financial services industry?

Let’s think of this in the context of ‘capability buckets’ in which AI is transforming an existing approach to a core function within banking & finance.

Fraud Detection and Prevention

Easily the #1 opportunity in financial services from a cost reduction perspective — annual losses from fraud amount to billions across the industry!

Automating review of large data sets, identifying patterns of fraud in real-time, and flagging bad actors prior to transaction authorizations is possible through an AI-powered risk & compliance system.

Multi-variable analysis on user location, spend habits, and money movement patterns can block a fraud attempt in real-time.

This minimizes the need for customers to dispute fraudulent transactions and banks to issue provisional credit.

Risk Management

To broaden the previous point, AI can power assorted risk management activities beyond fraud prevention.

Risk assessments, creating & filing suspicious activity reports, and responding to regulatory inquiries are being enhanced daily by AI.

Incorporating industry news, the latest regulatory guidance, and recent market trends into a comprehensive model helps predict potential risks quickly & accurately — while avoiding missteps with adopting regulation.

Automation of Back-Office Operations

Similar to risk management, back-office management is benefiting from the automation of data capture, ledgering of account activity, and verification of documents.

The ability to automate reduces the likelihood of human error, while approving overall efficiency at financial institutions and fintechs.

Customer Experience Enhancement

Hyper-personalized experiences are possible with artificial intelligence powered applications.

From chatbots to virtual assistants, customer inquiries and service requests can be quickly addressed in a tailored manner. No need to go through a call center and wait for an available (human) agent.

This next level includes customized financial insights and advice (on a 1:1 client basis). Financial institutions are asking customers about their short-term & long-term goals, then putting together financial plans. These plans now come with custom bank, loan, and investing products.

Despite these compelling applications building a solid case for adoption in 2025, it’s not that simple for firms to integrate AI (especially financial institutions).

Friction in Choosing AI Adoption

The benefits and applications from artificial intelligence are clear across multiple industries that work with technology.

For the banking & finance industry, financial institutions and fintechs hit a wall of challenges when it comes to proper implementation of AI:

1. Increased Concern with Data Privacy, Security

Preventing data breaches and fraudulent access is a major responsibility for all companies serving individuals & businesses.

Companies must manage critical transaction, account, and financial info — or risk non-compliance with data protection laws (that vary by jurisdictions, regions).

The large size of data sets with AI expand this concern to a whole new level.

2. Ethical and Fair Consideration for All

AI programs run on models & algorithms that were designed by humans, which comes with a level of inherent bias.

Not knowing the inputs used to build models places fintechs & financial institutions at-risk of evaluating clients unfairly.

This is crucial in lending programs in which underwriting processes are automated by AI.

Lack of fair consideration, practices that may be unintentionally deceptive, and gaps in proper disclosures to users can lead to regulatory infractions.

3. High Staffing Costs for Individuals with AI Expertise

The high demand + low supply of qualified AI talent is a global concern.

This reality drives up the hiring and salary expense of AI experts with the necessary skill & experience to implement best-in-class products.

Financial institutions face a larger tech gap, which drives them to outsource this need to talent development & resource staffing agencies.

4. Cumbersome Integrations with Legacy Tech Stacks

The banking industry has a historical challenge of working with legacy IT systems due to native technology stacks built before the 90s.

Rather than completely revamp this infrastructure, newer tech has been added in a patchwork manner over the last 30 years.

As a result, an AI integration (for a bank) and/or upgrade is much more expensive than in other industries.

5. Regulators Need to Weigh In

New rules are expected to come from regulatory agencies — what this will look like, and when announcements take place is unknown.

The concern for all industries is being able to evolve an existing AI integration with the latest rules (or changes) in order to avoid penalties.

The Road Ahead: AI’s Future in Financial Services

By the end of 2025, AI will no longer be a ‘nice-to-have’ — it’ll be an expected way of doing business.

Leaders will emerge as companies who become ‘super-users’ of AI.

Beyond knowing its benefits and applications, these firms are rigorously deploying the latest AI developments into daily operations (across multiple business divisions).

Industry giants are already diving deep into the AI pool: (i) JPMORGAN has COiN for analyzing legal contracts such as documents, (ii) Capital One has a customer assistant called Eno to manage spending and monitor activity, and (iii) PayPal leverages machine learning (ML) algorithms for identifying unusual transactions.

For fintechs and financial institutions, the pressure to utilize AI in the near-term is at an all-time high.

The next generation of AI features is coming soon, which will extend the learning curve even further for non-tech savvy enterprises & banks — leaving slow-movers behind.

Stay connected with this series on AI as next up we explore: “The Ethics of AI in Financial Services.”


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