Agentic AI, Edge Computing and the Future of Digital Transformation in Financial Services

Digital transformation in financial services is changing how banks and other financial institutions operate, presenting both opportunities and challenges. With new trends and technologies appearing all the time, customers are getting more tech-savvy and expect more than just standard services. They seek an experience that’s secure, compliant, and highly personalized to their needs, which is forcing the traditional financial institutions to adapt. Small and medium-sized (SME) organizations face even greater difficulties. According to Deloitte’s 2025 banking industry outlook, size remains a key advantage, as larger companies have stronger brand presence that could provide them with a buffer to remain attractive and competitive. This makes it important for SMEs to adopt financial technology trends by investing in future-proof innovations that add real value to customers and long-term resilience. 

Hence, in this article, we highlight the latest developments that shape the digital transformation in financial services and will help organizations to stay sharp, optimize operations, and meet clients' expectations.

Why is digital transformation a game-changer in financial services

Before diving into the current technological advancements, let’s take a step back to understand how digital transformation is reshaping financial services. Traditional institutions have been shifting toward tech-driven solutions for years, but with fintech shaking up the industry, the pressure to evolve has intensified. The biggest advantage of tech-powered companies lies in making financial services more accessible and convenient, giving customers more control over their money with intuitive and smooth experiences. Although traditional financial institutions remain trusted in security and stability, they must enhance digital experiences with innovations such as AI-driven automation, customer analytics and personalized financial solutions to stay competitive and meet customers' expectations head-on.

5 tech-driven trends powering digital transformation in financial services

As we mentioned, emerging financial technology trends are creating decision-making challenges for financial institutions as they determine which innovations are worth investing in. Additionally, the long-term benefits are not always immediately clear, leading to uncertainty in adoption. AI in financial services, for example, is widely discussed, but not every innovation will align with the organization’s needs. Here’s why we selected five digital advancements that not only drive long-term value and resilience for the sector but also makes it more competitive and innovative.

Predictive AI driving innovation and resilience in financial services

The industry relies on processing vast amounts of data, making predictive AI an essential part of overall digital transformation in financial services. Predictive AI models prove valuable by performing automated analysis, improving accuracy, and reducing manual effort and errors. For financial institutions, financial data security is critical, as mistakes can be costly - not just when it comes to money but also in terms of trust and security. According to an IBM research, the average cost of a data breach involving over 50 million records reached $375 million in 2024, with financial services ranking second in breach-related costs after healthcare. The same research also claims that nearly half (46%) of breaches involved customer data, while 51% were due to malicious attacks, 25% to IT failures, and 24% to human error. Despite these risks, only 28% of banks extensively use AI-driven security and automation - technologies that play a key role for strengthening cybersecurity, ensuring compliance, and maintaining operational stability.

For those already leveraging AI in financial services, the advantages are clear. One such example from our experience is an AI model that analyzes affordability and fraud data from external credit bureaus. Using real-time machine learning, it accurately flags fraudulent applications and integrates into the bank’s automated credit decisions. Additionally, customer analytics enabled the solution to reduce fraud risk and improve regulatory compliance by ensuring more accurate, data-driven decisions.

Transforming fraud with AI detection – case study

However, predictive AI models are not limited to cybersecurity purposes. They play a critical role in various areas of financial decision-making, including credit risk assessment, market trend predictions, and anticipating user behavior. These AI tools are also used for risk management, personalized customer service, and optimizing operational efficiency, allowing financial institutions to make more informed decisions and offer tailored services to customers.

One useful application is predicting customer churn rate. By analyzing transaction history, account balances, customer service interactions, and demographic data, banks can identify when a client is likely to leave. To address this, they can enhance engagement, personalize marketing, and offer proactive solutions - ultimately, delivering tailored experience and driving customer satisfaction.

Virtual assistants and chatbots redefining customer experience

Speaking of personalization, in 2025 AI-powered virtual assistants and chatbots are set to go far beyond simple question-answering. According to Allied Market Research , the conversational AI market is projected to reach $32.6 billion by 2030, reflecting growing enthusiasm for AI-driven customer interactions. Considering exceptional customer service is a key differentiator in financial industry, the evolution of conversational AI is more relevant than ever.

A major driver behind AI-powered virtual assistants is customer analytics in finance, which enables institutions to understand user preferences, transaction behaviors, and service interactions at a deeper level. By leveraging these insights alongside machine learning, virtual assistants and chatbots can deliver personalized experiences, tailoring responses based on users’ past interactions, preferences, and behavioral patterns. For instance, Accedia built a Natural Language Processing (NLP)-powered virtual assistant for a Central European credit union. The assistant automated balance checks, loan applications, and other common inquiries, providing instant, personalized, 24/7 support. Integrated into the credit union’s mobile app and website, it made managing finances easier for customers and therefore boosted customer satisfaction.

Nevertheless, as financial technology trends continue to arise, it is of no surprise the next frontier for conversational AI goes even further - emotional intelligence. Future AI systems are expected to become more human-like, improving their ability to detect and appropriately respond to human emotions, making interactions more empathetic, engaging, and natural.

From reactive to proactive: the rise of Agentic AI

AI in financial services goes one step further with agentic AI being recently on the rise. Agentic AI is a new generation of artificial intelligence designed to operate with autonomy, proactively making decisions and taking actions to achieve specific goals. Unlike traditional AI, which follows predefined instructions or reacts to user inputs, agentic AI analyzes situations, strategizes the next steps, and adapts based on past interaction - all while working toward objectives set by humans.

In the context of digital transformation in financial services, agentic AI is boosting customer engagement by building open banking and embedded finance to deliver personalized financial management. These AI-driven systems leverage customer analytics in finance to analyze user behavior, optimize spending, and proactively suggest financial strategies, giving consumers more control over their finances. Rather than waiting for user input, they anticipate needs, act, and ensure compliance, making interactions more seamless and intuitive.

For financial institutions, now is the time to explore agentic AI to automate complex decision-making and enhance efficiency. Early adopters will gain a competitive advantage, offering smarter, more proactive services that revamp the customer’s experience in finance.

Edge computing reshaping real-time data processing

Edge computing is at the core of the financial technology trends by processing data closer to its source instead of relying on distant cloud servers or data centers. Gartner even predicts that in 2025, 75% of all data will be generated outside traditional data centers and cloud platforms.

For the financial industry specifically, that means that devices like ATMs, payment terminals, and mobile banking apps can analyze and act on data instantly, reducing latency, improving performance, and enabling real-time fraud detection through behavioral analysis and facial recognition. Additionally, it can support customer analytics in finance by accelerating access to behavioral insights, helping banks personalize services and optimize risk assessments more efficiently.

On business and operational level, keeping data locally comes with benefits such as cutting infrastructure costs and strengthening cybersecurity. As Valerie Clark, IBM Edge Computing Global Services Leader, explains: "The ability to bring analytics right to the edge, where data is created, and enable decision-making in milliseconds can be a real differentiator on the market."

Explainable AI bringing transparency to intelligent decisions

As AI in financial services becomes more sophisticated, its decision-making processes get increasingly complex and opaque often referred to as the “black-box” problem. This happens when an AI model makes a decision, like approving or denying a loan, without explaining why - making it difficult for users to trust, interpret, or challenge the outcomes. When it comes to digital transformation in financial services, trust and compliance are essential, and this lack of transparency can hinder adoption of AI while raising concerns about fairness and accountability.

To bridge this gap, Explainable AI (XAI) makes AI models more transparent and interpretable without sacrificing performance. For instance, unlike traditional AI, which simply approves or denies a loan, XAI provides clear reasoning, showing how factors like credit score, income, and spending habits influenced the decision. As regulators push for greater AI accountability, this level of insight is becoming increasingly essential. In response, companies working with AI must find the right balance between efficiency and interpretability, ensuring explainability becomes a core element of responsible AI implementation.

Final thoughts

Digital transformation in financial services is no longer optional - it’s essential for staying competitive. With the right strategy in mind, SMEs can identify the most relevant financial technology trends and adopt AI, automation, and data-driven insights to strengthen security, enhance customer experiences, and drive sustainable growth.

If you’re ready to take the next step, Accedia is here to guide you through the process and implement solutions that deliver real impact. Let’s discuss how we can support your digital transformation journey.