AI in Banking: 2025 Trends

Estimated reading time: 19 minutes

Approximately 70% of financial services executives believe AI will directly contribute to revenue growth in the coming years. (Gen)AI drives a significant transformation in the banking sector. The adoption goes fast: AI technologies are no longer considered nascent but are increasingly being integrated into core business areas. Why? Because they offer the potential to reshape operations, enhance customer experiences, and drive new revenue streams.

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Applications of AI in Banking and Finance

The adoption of Generative AI in the banking sector has seen a remarkable increase in a short period. According to Gartner, in 2024, 75% of banking leaders reported having already deployed or being in the process of deploying GenAI. This was a significant shift from early 2023. Back then, (Gen)AI was treated with greater caution, with many financial institutions even prohibiting its use within their branches. This rapid integration underscores GenAI’s perceived value and potential across various key business areas within banks.  

In 2025, financial institutions are actively communicating the tangible benefits of AI and GenAI. They emphasise enhanced customer experiences, significant gains in operational efficiency and productivity, and strengthened security and fraud detection capabilities. The IDC Financial Insights Survey further underscores the potential, which reveals a diverse range of use cases.

The most promising use cases in finance

• Knowledge worker augmentation (cited by 55% of respondents),
• Real-time conversation monitoring (52%),
• Synthetic data generation (46%),
• Compliance automation (36%).

Several key use cases are currently driving the adoption of AI and Gen AI in banking:

1. Enhancing Customer Experience

AI-powered chatbots and virtual assistants transform customer service by providing 24/7 support and personalised interactions. Why? These AI chatbots handle a large volume of customer requests across multiple channels. They reduce the workload for human staff while maintaining service quality. For instance, a GenAI chatbot can answer customer questions meaningfully. However, the bank’s backend systems must equally efficiently address any underlying issues the chatbot identifies. Beyond basic queries, AI can offer personalised financial advice and recommendations, based on an analysis of customer data, behaviour, and transaction history. This personalised approach enhances customer satisfaction and fosters loyalty. It gives users the product and service recommendations specific to their individual needs.  

Ready for tailored experiences with AI? Read more in our guide on AI hyper personalisation.

2. Improving Operational Efficiency

AI and Gen AI are instrumental in automating routine tasks, such as data entry, reconciliation, and compliance checks. By automating these time-consuming processes, banks can significantly reduce operational costs and enable their employees to focus on more strategic responsibilities. Furthermore, Gen AI can simplify backend operations by automating code generation and converting legacy code. This capability helps banks modernise their IT infrastructure, accelerate the time-to-market for new products and services, reduce development costs, and ensure smoother transitions during system upgrades.  

3. Strengthening Risk Management and Fraud Detection

One of the most popular use cases for GenAI in banking is fraud detection. Advanced AI algorithms can analyse vast datasets to identify suspicious patterns and predict potential threats, strengthening financial transaction security. These systems can adapt in real time to evolving threats, providing a crucial layer of protection against fraud. Additionally, Gen AI can generate realistic, secure, and anonymised synthetic credit data, allowing banks to simulate customer behaviours and test credit risk models without risking sensitive customer information. This approach aids in modelling and testing while adhering to strict data privacy regulations and security requirements.  

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4. Personalised Marketing and Content Creation

Generative AI empowers banks to craft highly targeted marketing campaigns tailored to individual customer preferences. AI can analyse customer information, including demographics, preferences, and needs, to gain valuable insights into individual consumer profiles. Based on this analysis, AI algorithms can then automatically generate personalised email campaigns, targeted advertisements, and relevant product offers. This ultimately leads to improved customer engagement and increased revenue streams for businesses. AI’s capability allows for a shift from generalised pricing models to hyper-personalised approaches in marketing, improving profitability and strengthening customer loyalty.  

5. Ensuring Regulatory Compliance

AI-driven data analysis and reporting are enhancing banks’ regulatory compliance. Artificial intelligence offers the capability to automatically analyse extensive datasets, thereby streamlining the process of ensuring compliance with regulatory requirements. This automation significantly boosts the accuracy of reporting, a critical aspect for financial institutions operating within the heavily regulated banking sector.

6. Empowering Employees with AI Copilots

Frontline AI Copilots are transforming how bank employees interact with customers by delivering faster, more personalised, and highly relevant experiences. These Gen AI-powered tools act as real-time assistants for banking staff, offering insights and recommendations regarding the next best steps and actions. Additionally, AI Copilots can automate routine tasks for employees, such as updating data, filling out forms, and preparing follow-up messages, thereby improving efficiency and allowing staff to focus on more complex customer needs.  

The rapid shift from initial caution to widespread deployment of Gen AI in banking indicates a significant change in how financial institutions perceive and value this technology. The initial reluctance has given way to a strong belief in its potential to drive substantial improvements across various business functions. The diverse range of use cases, spanning both customer-facing and internal operations, underscores the versatility and broad applicability of Gen AI within the banking sector. From enhancing customer interactions and streamlining internal processes to bolstering security and ensuring compliance, Gen AI is proving to be a multifaceted tool with the potential to revolutionise how banks operate. Furthermore, the ability to generate synthetic data represents a notable advancement, allowing banks to innovate and test new solutions while adhering to stringent data privacy regulations, a critical consideration in the financial industry.

Other use cases of AI in banking

Beyond the primary applications, AI transforms banking through diverse use cases that enhance operational efficiency and customer experience. These include : 

• AI-powered document extraction, which automates data processing and reduces manual errors
• Advanced credit risk assessment algorithms that provide more accurate lending decisions
• Process automation across banking workflows
• Predictive maintenance for ATMs, critical equipment to minimise downtime
• AI-driven investment advice that offers personalised financial recommendations tailored to individual investor profiles.

Each of these applications demonstrates AI’s potential to revolutionise traditional banking processes by introducing greater speed, accuracy, and intelligent decision-making capabilities.

Challenges and Disadvantages of AI in Banking

The integration of AI and Gen AI into the banking sector presents both significant challenges and compelling opportunities:

Potential Challenges

Several challenges consistently emerge:

• Regulatory Hurdles and Compliance Requirements: The financial sector is highly regulated. This requires careful navigation of complex regulatory landscapes when implementing AI and Gen AI solutions.  
• Need for Skilled Talent and Expertise: A shortage of professionals with specialised skills in AI and Gen AI development, deployment, and management poses a significant challenge to widespread adoption.  
• Ethical Considerations and Bias in AI Algorithms: Ensuring fairness, transparency, and accountability in AI-driven decisions is crucial. It maintains customer trust and avoids unintended discriminatory outcomes.  
• Risk of Inaccurate or Biased Content Generation: Gen AI models can sometimes produce inaccurate or biased outputs. They could damage a bank’s reputation if not properly validated and controlled.  
• Implementation Costs and ROI Uncertainty: Quantifying the ROI for AI projects and managing the often substantial implementation costs can be challenging for financial institutions.  
• Integrating AI with Legacy Systems: Many banks rely on complex and often outdated legacy IT systems. Therefore, it can create significant hurdles when integrating new AI technologies.  

Data Security and Privacy Concerns

Financial institutions face multi-faceted data security challenges when implementing AI technologies:

• Increased Risk of Breaches: AI algorithms often process vast amounts of sensitive customer data, including Personally Identifiable Information (PII) and financial records. These can heighten the risk of data breaches if not adequately protected.  
• Internal Threats: Employees may inadvertently or deliberately expose PII while using AI tools, creating significant organisational risk. This danger is heightened in environments lacking clear AI-specific acceptable use policies.
• Difficulty in Data Retraction: There are concerns about the ability to permanently retract or delete data within AI or Machine Learning (ML) systems, potentially leading to unintended data exposures.  
• Evolving Cyber Threats: Threat actors are increasingly using AI to craft more sophisticated attacks, including phishing schemes and ransomware, making it a cybersecurity arms race where financial institutions need to leverage AI for defense. 
• Data Quality and Governance: The effectiveness of AI models heavily relies on the quality and quantity of data used for training. Poor data quality can lead to inaccurate or biased outcomes, and ensuring proper data governance is crucial.  
• Third-Party Risks: Financial institutions’ increasing reliance on third-party providers introduces new security and privacy risks across the supply chain.  
• Lack of Transparency: The “black box” nature of some AI applications can make it difficult to understand how decisions are made. This raises concerns about data usage and potential misuse.  
• Regulatory Compliance: Banks must adhere to stringent data privacy regulations like GDPR when using AI, requiring appropriate consent for data collection and ensuring data anonymisation where possible .  
• Customer Trust: Customers have reservations about data privacy and security when AI is involved in managing their finances and want to know exactly how their data is being used and what safeguards are in place.  
• Ownership of Data and Insights: Emerging AI solutions can challenge traditional expectations regarding financial institutions’ ownership of data, models, and insights.

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Significant Opportunities

Despite these challenges, the opportunities presented by AI and Gen AI in banking are substantial:

• Enhanced Customer Engagement and Personalisation: AI enables banks to deliver hyper-personalised experiences, tailored product recommendations, and proactive services, increasing customer satisfaction and loyalty.  
• Improved Operational Efficiency and Cost Reduction: Automating routine tasks and optimising processes through AI can significantly reduce operational costs. Also, this can free up human resources for more strategic initiatives.
• Strengthened Risk Management and Fraud Prevention: AI algorithms can analyse vast datasets more effectively than traditional methods to detect and prevent fraudulent activities, safeguarding the bank and its customers.
• Faster Time-to-Market for New Products and Services: Gen AI can accelerate the development and deployment of innovative banking solutions, allowing banks to respond more quickly to changing market demands and customer needs.  
• New Revenue Streams and Business Models: AI and Gen AI can create opportunities for banks to offer new AI-powered services, personalised advice, and other innovative offerings that can generate new revenue streams.  
• Improved Employee Productivity and Satisfaction: AI-powered tools and copilots can assist employees with routine tasks and provide valuable insights, enhancing their productivity and overall job satisfaction.  
• Better Regulatory Compliance and Reporting: AI can streamline compliance processes, improve the accuracy of regulatory reporting, and help banks meet evolving regulatory requirements.  

Challenges and opportunities of AI in banking: summary

Data security, regulation, and talent acquisition are consistently seen as major challenges. This highlights the need for a strategic approach to AI adoption in banking. This strategy must cover technology, governance, compliance, and workforce development. Success requires addressing all these critical elements.

On the other hand, AI and Gen AI offer significant opportunities. These include better customer engagement, improved efficiency, and stronger risk management. These benefits give banks strong reasons to invest and overcome the challenges.

Both innovation potential and misuse risks are important. This emphasises the need for responsible AI frameworks in banking. Such frameworks are crucial for development and deployment to build trust and ensure ethical practices.

Anticipated Developments and Trends for 2025 for AI in Banking

Outlook for 2025

Most sources converge on the expectation of increased adoption of AI and Gen AI in banking in 2025. They expect a significant focus on realising tangible business value, including revenue growth. Forrester specifically emphasises the continued evolution of digital banking experiences towards greater personalisation and a more human-like feel. Gartner and Deloitte highlight the growing importance of domain-specific AI models for more targeted applications. Also, the increasing need to address regulation and risk management barriers. PwC’s reports underscore the transformative potential of Gen AI and the necessity for robust governance frameworks and talent development to capitalise on its benefits fully. A potential area of slight divergence might be the pace and extent of adoption for emerging technologies like AI agents.

The strong agreement across various reports on key use cases like customer service, fraud detection, and operational efficiency indicates a broad industry understanding of the most immediate and valuable applications of AI and Gen AI in banking. This consensus suggests that banks will continue prioritising these areas in their AI strategies. The increasing attention given to AI agents points towards a potential next phase of AI adoption in the banking sector, characterised by more autonomous and proactive systems. This evolution could significantly impact further streamlining operations and enhancing service delivery. Furthermore, the widespread expectation of a shift towards revenue generation in 2025 signifies a maturing of AI adoption in banking. The initial focus on experimentation and efficiency gains is now complemented by a strong emphasis on demonstrating a clear ROI and leveraging AI to drive financial growth.

Increased Focus on Revenue Generation

The initial wave of AI adoption in financial services primarily focused on driving efficiency. Today, the industry’s attention is increasingly shifting towards leveraging these technologies for revenue growth. Approximately 70% of financial services executives believe AI will directly contribute to revenue growth in the coming years by:

• Enhancing customer experiences,
• Making products and services more personalised and relevant,
• Enabling innovative new offerings,
• Empowering cross- and up-selling,
• Improving security against potential threats.

^{Rise of More Personalised Digital Banking Experiences}

Forrester predicts that digital banking experiences in 2025 will become increasingly humanlike, connected, and empowering. This evolution will involve banks focusing on delivering exceptional user experiences by minimising friction, reducing cognitive load, and simplifying tasks. Conversational banking, powered by AI interfaces, is anticipated to take off in 2025, making digital interactions more natural and engaging. Furthermore, there is an expected push towards autonomous finance. In this area, banks leverage AI and data to anticipate customers’ unique financial needs and offer proactive, relevant services.

^{Potential Advancements in Autonomous Finance and Embedded Banking Services}

Forrester also anticipates that embedded finance will gain broader appeal in 2025. This trend involves seamlessly integrating banking products and services into non-banking platforms and applications. Thus, allowing customers to access financial services at their point of need. This development, coupled with the move towards more autonomous financial experiences, suggests a future where banking becomes more integrated into customers’ daily lives.

As banking customers gravitate to digital touchpoints, banks understand the critical need to deliver an exceptional user experience. They aim to minimise friction, reduce cognitive load, and simplify tasks. For example, getting a business loan offers direct inside accounting software based on real-time financial data.

^{Increasing Importance of Domain-Specific AI Models}

Gartner predicts a significant increase in enterprises’ use of domain-specific Gen AI models in the coming years. By 2027, over 50% of the Gen AI models should be specific to a particular industry or business function. While this prediction extends beyond 2025, the trend towards more specialised AI models will likely gain momentum in 2025 as banks seek to leverage AI for specific and nuanced applications within the financial domain. These domain-specific models can be smaller, less computationally intensive. Potentially, they reduce the risk of inaccurate outputs compared to general-purpose models.  

The anticipated shift towards revenue generation through AI signifies a maturation of the technology within the banking sector. Banks increasingly view AI as a tool for cost reduction and a direct means to drive business growth and enhance their competitive positioning. The predicted evolution of digital banking experiences towards being more humanlike, connected, and empowering indicates a holistic approach to improving customer engagement, aiming to create more intuitive, seamless, and supportive interactions. Furthermore, the increasing focus on AI governance and risk management reflects a growing awareness of the potential downsides of AI deployment, prompting a more cautious and responsible approach to its implementation within the highly regulated financial industry.

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Growing Emphasis on AI Governance, Ethics, and Risk Management

As the adoption of Gen AI accelerates, organisations are increasingly focused on managing the associated risks. McKinsey reports that companies are more actively mitigating risks related to inaccuracy, cybersecurity, and intellectual property infringement. Deloitte identifies regulation and risk management as significant barriers to deploying Gen AI initiatives. This highlights the critical need for robust governance models. This growing emphasis underscores the importance of responsible AI development and deployment within the banking sector to ensure trust and mitigate potential negative consequences.

AI regulation challenges

AI policy and regulation landscape is rapidly evolving, with countries and regions working to establish comprehensive frameworks. Notable examples include Singapore’s AI methodologies, the European Union’s AI Act, and US President Biden’s AI Executive Order. Despite these efforts, three key challenges persist in developing effective AI policies for financial services.

The first challenge is the breakneck pace of AI innovation, which outstrips regulators’ ability to comprehend emerging implications and threats. Second, the proliferation of AI models makes it difficult to determine which aspects require scrutiny. Third, there is ongoing debate about the appropriate level of oversight. Should it be managed by industry, state, federal, or regional entities? Interestingly, a survey of OECD members suggests that existing regulations in financial services may already provide a robust foundation for governing most AI applications. Particularly in areas like consumer protection, model risk management, cybersecurity, and fairness. However, the inconsistent global approach to AI regulation creates uncertainty for businesses navigating this complex landscape.

Devoteam’s Strategy for AI-first banking

Devoteam envisions a transformative approach to AI-first banking that goes beyond incremental technological improvements. The strategy focuses on fundamentally reimagining banking through artificial intelligence, presenting three primary pathways for financial institutions:

1. Create a New AI-First Bank in Parallel. This approach involves developing an entirely new banking model alongside existing infrastructure. It allows for radical innovation without disrupting current operations. It provides a sandbox for experimenting with cutting-edge AI technologies and designing a completely reimagined customer experience.
   
2. Transform Existing Bank to an “AI-First” Design. A more comprehensive transformation strategy that systematically redesigns the entire banking ecosystem around AI capabilities. This approach requires deep organisational commitment, significant investment in technology and talent, and a willingness to fundamentally restructure existing processes.
   
3. Incremental AI Enhancement (AI-Second). A more conservative approach that focuses on gradually integrating AI capabilities into existing banking functions. This approach minimises risk while still driving technological improvement.
   

The AI-first banking vision centers on five key transformative dimensions:

• Enhanced Customer Experience: AI enables hyper-personalised banking through intelligent chatbots, virtual assistants. Contextually aware financial guidance adapts to individual customer needs and preferences.
• Operational Efficiency: Comprehensive automation of routine tasks, from transaction processing to compliance checks, dramatically reduces operational costs and increases organisational agility.
• Advanced Risk Management: Sophisticated AI algorithms analyse complex data ecosystems to identify potential risks, detect fraudulent activities, and develop more nuanced credit assessment models.
• Product Innovation: AI acts as a catalyst for developing dynamic financial products. For instance, predictive investment strategies and personalised loan offerings that respond in real-time to individual customer profiles.
• Data-Driven Insights: Leveraging AI transforms raw data into actionable intelligence, enabling more informed decision-making and predictive financial strategies.

This vision represents more than a technological upgrade. It fundamentally reimagines banking’s potential, where AI becomes the core driver of innovation, efficiency, and customer-centricity.

Conclusion

The banking sector is undergoing a transformative phase, with Artificial Intelligence and Generative AI poised to revolutionise its landscape. As highlighted in the analysis, there is a growing trend towards increased AI adoption, with a strategic focus on revenue generation and developing more sophisticated digital banking experiences. Banks recognise the potential to enhance customer engagement, improve operational efficiency, and strengthen risk management through these advanced technologies.

Financial institutions must adopt a comprehensive and strategic approach to capitalise on this technological shift fully. This involves developing a clear AI strategy aligned with business objectives, investing in talent development, establishing robust governance frameworks, and prioritising data security and privacy. Banks should also modernise their IT infrastructure, foster collaborations with technology providers, maintain customer trust through transparency, and implement AI solutions through a phased, carefully monitored approach. By embracing these technologies responsibly and innovatively, banks can position themselves at the forefront of digital transformation, creating superior customer experiences and driving sustainable growth in an increasingly competitive financial landscape.