AI is rapidly becoming the cornerstone of banking innovation, with Generative AI (GenAI) in finance software standing out as a transformative force. Citi Bank estimates that embracing AI technologies could boost the banking sector’s profits by an extraordinary $170 billion, translating to a 9% growth by 2028.
Whether you’re a fintech entrepreneur, a banking leader, or new to the financial sector, keeping up with AI trends in banking is essential. These technologies open doors to numerous possibilities, including uncovering data-driven insights, elevating brand engagement, enhancing customer experiences, and fortifying risk management strategies.
There are many industry leaders, like JPMorgan Chase and Capital One, that have already implemented AI in their businesses, and 43% of BFSI companies consider it essential to their success by 2025.
Don’t let your business fall behind by adopting these latest AI trends and actionable guidance provided by KMS Solutions’ experts to help you stay competitive in this dynamic industry.
The Current AI Market in Banking
According to the Precedence research, the AI market in banking worldwide is forecasted to reach $315.50 billion in 2033, growing at a robust annual rate of 31.83% from 2024. The AI trend has continuously grown as more banks and financial institutions embrace digital transformation and modernization.
Here’s how artificial intelligence has already affected the banking industry:
- Automated Transaction: In Australia, several banks now automate the loan approval process, enhancing efficiency and reducing waiting times.
- Fraud Detection Success: NatWest has cut fraud by 6% across the UK and reduced new account fraud by 90% since 2019. These advancements have lowered costs and increased loan interest, with AI-driven personalization achieving 5x more clicks on offers.
- Boosting Productivity: AI is anticipated to increase productivity in investment banks by 27% and improve front-office operations by 27%–35% by 2026. A McKinsey report highlights that generative AI could add $200 billion to $340 billion annually to the global banking industry, primarily through efficiency gains.
AI is expected to revolutionize decision-making processes further, with applications in loan origination, credit assessments, and client relationship management. We have identified the top AI trends with the potential to influence the BFSI industry in 2025. Let’s dive deeper into these trends in the next section!
Top 8 AI Trends in Banking to Prepare For in 2025
1. Centralized Operating Models for GenAI
Banks are increasingly moving from decentralized data practices to a centralized operating model. A recent survey from McKinsey & Company reveals that over half of companies, including major technology firms with approximately $26 trillion in assets, have adopted centralized structures for generative AI (GenAI).
This shift happens as GenAI introduces specific challenges that are more effectively addressed through a centralized approach, such as:
- Generating inaccurate or nonsensical information.
- Risking intellectual property violations.
- Lack of transparency in system operations.
- Potential biases and unfair outcomes.
- Security vulnerabilities.
Centralized oversight ensures that pilot initiatives form a strong foundation for scalable deployment. It also enables better resource allocation, leveraging limited GenAI expertise. This model fosters collaboration, enhances teamwork, and attracts top talent.
A thoughtfully designed centralized model can drive successful GenAI implementation and position banks for an AI-powered future. When you adopt this approach, you’ll need to establish a core team responsible for overseeing your company’s entire AI solution. This team ensures rigorous quality assurance, standardized processes, continuous monitoring for bias, and robust security protocols.
2. Cloud-First Architecture
Scaling remains a significant challenge for banks in the fintech space, and adopting a cloud-first architecture is one of the most effective solutions. This strategy is also essential for integrating AI into banking applications.
AI workloads demand extensive computing power and data resources, which can be difficult—or even impractical—to manage with on-premises solutions. Consequently, cloud computing is set to become a cornerstone strategy for the future. Embracing a cloud-first mindset while ensuring seamless integration between cloud and on-premises systems to streamline the migration process.
Read more: Banking Cloud Migration In Australia: Best Practices for a Smooth Transition
We’ve collaborated with leading platforms like AWS and Azure to build scalable applications for fintech and various other industries. Share your project with us, and our team will assist you in driving it forward. Contact us now!
3. Advanced Security & Anomaly Detection
In 2022, Revolut experienced a database breach after cyber attackers infiltrated their system, a breach they unfortunately discovered too late. Financial companies often find it difficult to detect unusual activities such as fraud, account takeovers, abnormal transactions, network hacks, and money laundering. Moreover, fraudsters constantly change, and traditional security measures often struggle to keep pace with new tricks like synthetic identity fraud.
A recent survey by Gartner reveals that 34% of organizations are either using or in the process of implementing AI-driven security tools to tackle risks related to generative AI. These tools not only analyze historical data but also learn and evolve over time, improving their ability to identify emerging fraudulent activities.
To stay ahead, banks are increasingly adopting GenAI and robotic process automation (RPA). These technologies can help BFSI businesses:
- Detect irregular patterns in network activity that may indicate unauthorized access or cyber-attacks
- Identify and flag behaviors or digital signatures associated with known cyber threats
- Provide real-time alerts for suspicious activities and automate responses, such as blocking malicious IP addresses or isolating compromised systems.
4. Large Language Models (LLMs) Adoption in Banking
The banking sector is undergoing a major digital transformation, driven by the power of Large Language Models (LLMs). These advanced AI technologies are set to redefine how banks operate and interact with their customers, offering more than just technological upgrades.
LLMs enable banks to deliver highly personalized customer experiences by analyzing large volumes of data in real time. This allows them to provide intelligent, conversational responses to customer inquiries. As more consumers turn to digital banking, this level of personalization has become essential.
By 2022, 78% of U.S. adults preferred using mobile apps or websites for banking, signaling the growing shift toward digital banking. A survey of 390 financial institutions across 28 states reveals that AI-powered solutions are becoming a key focus for the future.
With the U.S. banking industry recovering from the bank failures in early 2023, most banks are projected to perform well and build capital in 2024. This sets the stage for LLMs to play a transformative role in the future of banking.
5. Chatbots and Virtual Assistants
PwC’s research revealed that 73% of consumers prioritize customer experience (CX) when deciding whether to make a purchase from a company. A powerful way to enhance CX is by leveraging AI-driven chatbots and virtual agents.
AI chatbots enable users to quickly check balances, process transactions, and transfer funds simply through conversation. Virtual agents, on the other hand, assist users with tasks like setting up new accounts and routing more complex issues to the appropriate department. Best of all, these solutions are available around the clock.
Businesses can start small with integrating AI chatbots and virtual assistants—such as using ChatGPT in finance apps to provide instant responses to customer inquiries and tasks. A notable example is Bank of America’s Erica, which helps customers with tasks ranging from accessing account information to making payments and offering personalized financing advice.
Several other banks have embraced AI-powered assistants. Capital One offers Eno, a chatbot to assist with financial management, while TD Canada Trust provides Clari to help users manage their finances. Citi also made strides with the Citi Bot SG on Facebook Messenger, allowing customers easy access to support via social media platforms.
6. Algorithmic Trading
Algorithmic trading streamlines the buying and selling of securities by using algorithms to divide large stock orders into smaller, more manageable “child orders.” These orders are executed based on predefined criteria such as price, volume, or timing.
Over the past two decades, financial customers have come to represent about 50% of global FX trading, largely due to the introduction of advanced trading platforms. Notably, algorithmic trading now accounts for nearly 75% of spot FX transactions.
BNP Paribas exemplifies a company offering tailored algorithmic trading solutions as part of their services. Leveraging AI, they deliver numerous advantages, including:
- Integrating multiple data sources to provide a comprehensive market view.
- Automating trading decisions through predefined rules and machine learning models.
- Adapting and optimizing strategies in response to shifting market conditions.
- Offering real-time transaction monitoring and reporting.
For trading app developers, incorporating algorithmic trading capabilities can be transformative. Algorithms such as decision trees, neural networks, and support vector machines analyze extensive datasets, learning from historical market trends to identify patterns often overlooked by humans. This functionality can significantly enhance trading decisions for both you and your users.
7. AI in Regulatory Compliance
AI is increasingly integral to regulatory compliance in the banking sector, offering tools to navigate complex legal frameworks efficiently. By automating and enhancing compliance processes, AI helps financial institutions adhere to various regulations, thereby reducing the risk of non-compliance and associated penalties.
Several regulations shape the integration of AI in banking compliance:
- Basel III: An international regulatory framework that sets standards for bank capital adequacy, stress testing, and market liquidity risk. AI assists banks in meeting these standards by providing advanced risk assessment tools.
- General Data Protection Regulation (GDPR): A European Union regulation that governs data protection and privacy. AI applications in banking must comply with GDPR to ensure the ethical use of customer data.
- Anti-Money Laundering (AML) and Know Your Customer (KYC) Regulations: AI systems used in AML and KYC processes must comply with regulations designed to prevent financial crimes. This includes accurately verifying customer identities and monitoring transactions for suspicious activities, ensuring that AI applications do not inadvertently facilitate money laundering or terrorist financing.
This agile approach allows Capital One to continuously improve their mobile app based on real-time customer feedback while ensuring minimal downtime and faster time-to-market.
8. AI in Pricing
Optimizing pricing has long been crucial for banks, directly influencing their profitability. In 2024, there’s a notable transition from simple optimization to personalized pricing strategies. Banks are now integrating human expertise with generative AI and comprehensive data sets to enhance scenario planning and tailor pricing to individual customers.
This evolution is driven by a data-centric approach to pricing decisions, supported by several key factors:
- Significant AI Investment: The financial sector’s expenditure on artificial intelligence is projected to grow substantially, increasing from approximately $35 billion in 2023 to an estimated $97 billion by 2027, reflecting a compound annual growth rate (CAGR) of 29%.
- Expansion of AI in Fintech: The market size of AI within the fintech industry expanded from $42.83 billion in 2023 to $44.08 billion in 2024, indicating a rising investment trend in this domain.
AI is transforming how banks determine pricing. By utilizing AI and data analytics, banks can move away from static pricing models, instead offering personalized pricing tailored to each customer. This shift has the potential to unlock significant revenue and profitability gains, establishing AI as a pivotal element in the future of bank pricing strategies.
Key Strategies for Banks to Successfully Implement AI
Before diving into these trends, it is essential to analyze if they truly align with your business goals. While AI offers immense potential, it’s crucial to ensure it meets your specific needs.
Then, we’ll explore practical strategies for integrating AI into your business, focusing on a seamless and secure implementation tailored to your requirements.
Choose the Right AI Trend to Adopt
Before diving into these trends, it is essential to analyze if they truly align with your business goals. While AI offers immense potential, it’s crucial to ensure it meets your specific needs.
Then, we’ll explore practical strategies for integrating AI into your business, focusing on a seamless and secure implementation tailored to your requirements.
Seek Guidance from AI Specialists
Integrating AI into your app goes beyond implementation—you also need to consider factors like cloud storage, data management, and system integration. Collaborating with AI experts who understand the banking industry can make this process smoother and more efficient.
At KMS Solutions, we’ve supported numerous banking platforms, including investment portfolio management and card systems. Our expertise enables us to help clients select the most suitable technologies, AI models (OpenAI GPT, Google Gemini, etc.), and deployment strategies. Additionally, we develop tailored AI roadmaps that align with business goals and comply with regulatory standards.
Leverage Outsourced AI/ML Expertise
When you’re ready to move forward with your AI project, partnering with a trusted AI/ML outsourcing provider can be a smart choice. Outsourcing is often more cost-effective than building an in-house team while delivering comparable results. By outsourcing, you can:
- Expand your AI capabilities while maintaining quality and security standards.
- Achieve faster outcomes with expert-driven, high-quality solutions.
- Allow your internal team to focus on other critical business priorities.
Our KMS experts specialize in fintech software development, offering services such as custom platform development, integration, modernization, software testing, and ongoing support. Our skilled AI team delivers tailored solutions designed to meet your unique business objectives.
Case Study: Empowering Optima with GenAI
The KMS’ development team has collaborated with Optima’s internal team to integrate AI models, including OpenAI GPT and Google Gemini, to automatically extract large volumes of invoices within minutes.
Prioritize Security and Regulatory Compliance
Focusing on data security and adhering to regulations is essential when implementing AI in the financial sector. Given the sensitivity of financial data, banks must comply with industry standards such as GDPR, the EU AI Act, and local financial regulations. Collaborate with your security and compliance teams to design robust AI-driven fintech systems that are transparent, ethical, and equipped to identify and mitigate risks like fraud and bias.
A good starting point is establishing a strong data management framework. This involves cleaning, organizing, and collecting high-quality data from various sources. A well-structured data system not only enhances security but also supports the development of more accurate predictive models.
Sum Up
Our analysis of the top 8 AI trends in banking for 2025 highlights a wide range of applications, from enhancing operating models to streamlining AI-powered customer onboarding.
These advancements point to a shared trajectory: AI is set to revolutionize the banking sector by simplifying operations, cutting costs, and unlocking new opportunities for value creation.
In short, AI isn’t just changing banking—it’s redefining it. Looking ahead, one thing is clear: Generative AI will remain a key driver of innovation in the industry. The trends we’ve explored represent just the beginning, with even more groundbreaking developments expected in the coming years.
