AI in Banking: Revolutionizing Financial Services Through Intelligent Automation

The past few years have been highly successful for retail banks, with a strong recovery from the pandemic. However, the future looks challenging as banks face slower deposit growth, tougher interest rate conditions, and rising operating costs.

AI in banking provides a promising solution to counter sluggish growth and navigate the challenging environment. Early adopters of AI stand to gain a competitive edge, with some experts predicting up to a 15% improvement in cost-income ratios over the next few years.

How can banks shift from digital to AI-first strategies and maximize the potential of GenAI? In this article, we’ll explore the top applications of AI in banking, its transformative benefits, and what the future holds for the industry.

What is AI in banking

AI in banking powers both internal and customer-facing operations. It helps financial institutions automate routine tasks, analyze large datasets, tailor offers to individual clients, and more.

AI has become increasingly important in the banking sector, with 75% of organizations already using it across their operations. For now, AI is mainly used in transforming data into analytical insights, anti-money laundering (AML), fraud detection, and cybersecurity.

Market forecasts also indicate that AI will become the next digital revolution in banking. By 2030 AI spending in banking is projected to surge to 84.99 billion U.S. dollars, with 55.55% CAGR.

In a recent interview about AI's role in transformation of banking operations, Nitin Rakesh, CEO & Managing Director at Mphasis, said the following:

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"There will probably be a reprioritization of spend towards high priority, high ROI areas, which also means there will be a focus on using AI operations in taking out cost in areas where there’s still too many people involved.”

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With this in mind, we can predict that AI in banking will greatly help in operations such as customer onboarding, load processing, back-office operations, and document management.

Without further ado, let’s explore how AI is being adopted in banking today.

The evolution of AI in banking

According to the research by Polaris Market Research, AI in banking market size was valued at $19.84 billion in 2023, and is expected to reach $26.10 billion by the end of 2024.

GenAI has gained traction in the banking sector since the launch of ChatGPT in late 2022. Since then, the technology enabled financial institutions to enhance customer service, improve operational efficiency, and innovate business models by leveraging vast amounts of data across various formats.

If we compare AI adoption rates from 2022 and expected rates in 2025, we can see that AI becomes increasingly critical for banks, jumping from 8% in 2022 to 43% in 2025.

Besides, banking companies are actively hiring data engineers, risk managers, and data scientists to support AI-related activities. With information we could find across open sources, top UK banks now have 73 data-related job openings, and 63 AI-related (having AI-related keywords in job descriptions). This signals a rising demand for talent that can build an infrastructure where AI can be applied effectively, deploy AI models, and mitigate risks that come with AI.

The current state of AI adoption in banking

AI in banking has implications for both the client-facing side of banking and the back end, where data analytics and software development are vital. In customer interactions, generative AI in banking, like chatbots and automated credit scoring systems, creates faster, more service-oriented experiences.

On the operational side, AI drives efficiency in capital management, risk modelling, and fraud detection while supporting better trading and portfolio management decision-making.

The following table outlines the four main categories where AI solutions for banks have the most impact:

A recent survey by Digital Banking Report identified how banks are implementing AI in their processes today:

For example, Citizens Bank is already rolling out some of these use cases across the organization. Don McCree, Senior Vice Chair & Head of Commercial Banking at Citizens Bank shared how AI is being implemented in the organisation:

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“We’ve got a few use cases that we’re rolling through the company. The most significant one is the call centre. We also use AI to help our coders build new technologies and APIs. We see applications in how we write annual reviews, how we actually advise our customers, how we spot trends more efficiently and enable our bankers. It’s actually quite exciting and we’re spending a lot of time investing in it.”

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Considering all of the above, we can see that AI in banking is in its early days and organizations make a larger bet on internal use cases, while researching on how to automate human-dominated areas of banking sectors, like customer support call centers.

Deloitte’s research highlights that AI in banking doesn't primarily save costs by replacing the workforce but by augmenting it. AI enables employees to handle higher processing volumes and perform tasks more efficiently, significantly scaling up productivity and capacity.

Key Drivers for AI Implementation

The future of AI in banking will be lucrative. Analysts estimate that AI in the banking sector could add a record $1 trillion of value.

In the next few years AI will reshape the banking world and determine winners and losers. Successful AI pioneers can achieve a 5-15% improvement in cost-income ratio. Early adopters of AI could see a 5-15% improvement in cost-income ratios—a significant advantage, especially as the industry only regained pre-2020 income levels in 2023 after years of decline.

Let’s take a closer look at some of the most significant drivers for AI implementation in banking.

Revenue generation

AI offers banks new revenue-generating capabilities by driving growth across multiple business lines.

One area is insight-driven pricing, where AI enables real-time customization of offers, such as preferential lending rates, by accurately assessing credit risk. This precision helps attract and retain high-value customers.

Another key area is hyper-personalized marketing, which uses AI to analyze customer data and identify individual needs, significantly improving conversion rates through tailored communication.

Finally, AI-powered trading algorithms can process a vast range of real-time market insights and automate trading decisions, boosting trading income. These capabilities allow banks to optimize revenue streams, gain a competitive edge, and enhance overall profitability.

Cost savings

AI enables cost savings in banks by automating repetitive tasks like data entry, query management, and document drafting, freeing up staff for higher-value activities.

GenAI enhances productivity by summarizing large documents and supporting data governance, remediation, and analytics. This "marginal gains" approach boosts efficiency across the workforce, similar to how proficiency in spreadsheets and typing transformed productivity back at the beginning of the digital era.

In IT, AI-generated code accelerates software development, reduces oversight needs, and improves quality, making maintenance less cost- and resource-intensive.
Additionally, AI strengthens loss avoidance functions, such as credit risk management and fraud prevention, by analyzing broader datasets to reduce loan impairments and financial losses. These combined efficiencies lower operational costs while enhancing performance.

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Note: Learn how to automate routine software development processes and speed up CI/CD pipeline by 55% with our guide.

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Applications of AI in banking

From automating routine tasks to enabling real-time fraud detection, AI transforms banking into a more dynamic, secure, and customer-centric industry. Below are some of the key applications where AI is driving innovation and value in the banking sector.

AI-powered chatbots and virtual assistants

Customer engagement is one area where the future of AI in banking seems assured. Banks can analyze customer behavior and preferences using advanced algorithms to deliver tailored financial products and services, making their customers feel attended to personally.

AI-powered chatbots transform repetitive processes in customer services, such as answering questions and guiding customers through transactions, with 24/7, nearly human-like support.

AI in action: Erica is Bank of America’s virtual financial assistant. It’s designed to help customers spend, save and plan their finances smarter. In 2024, Erica surpassed 2 billion interactions with over 42 million clients, doubling its engagement from 1 billion interactions in just 18 months. Clients speak with Erica approximately 2 million times daily for tasks such as monitoring subscriptions, understanding spending habits, and managing deposits and refunds.

Personalized financial solutions

After the COVID-19 pandemic, banking customers have significantly higher expectations. During the initial months of the pandemic, online and mobile banking usage surged by 20% to 50% globally, and this elevated engagement is expected to persist well into the post-pandemic era. Additionally, 15% and 45% of consumers anticipate reducing their visits to physical branches in critical global markets even as restrictions lift.

With AI, banks can process wide arrays of customer information and deliver finely tuned, contextually relevant services through the most appropriate channels at precisely the right moment.

AI in action: A universal bank in the UK has achieved a fivefold increase in click-through rates for its personal lending offers by using personalized content and refining its target audience selection.

Streamlining operational efficiency through intelligent automation

Banks increasingly turn to intelligent automation to enhance operational efficiency, which has applications in dozens of other industries. By automating repetitive tasks such as data entry, compliance monitoring, and transaction processing, banks can reallocate resources to more strategic initiatives.

Intelligent automation tools, like AI-powered systems for document analysis, not only speed up processes but also minimize human error. For example, advanced algorithms can process vast datasets, identify patterns, and generate actionable insights, enabling faster decision-making. Moreover, this technology enhances scalability, allowing banks to handle growing volumes of transactions or regulatory requirements with ease.

Data in action: Deutsche Bank’s Alpha-Dig program exemplifies this trend. Alpha-Dig analyzes vast geopolitical data from sources such as news articles and social media, creating comprehensive risk profiles for countries. This intelligent automation allows banks to respond swiftly to potential geopolitical threats, thus streamlining decision-making processes and minimizing operational risks.

Process automation and Robotic Process Automation (RPA)

Process automation through AI-driven RPA enables banks to manage repetitive tasks like customer onboarding and loan applications with enhanced speed and fewer errors.

AI in action: JPMorgan Chase’s Contract Intelligence (COiN) platform. COiN uses machine learning to analyze and extract vital information from legal documents. What would previously take up to 360,000 hours annually for human employees to complete is now achieved almost instantly with COiN, saving both time and resources while minimizing human error.

AI in risk management and fraud detection

With the increasing sophistication of financial crimes, banks are turning to AI to strengthen their risk management and fraud detection capabilities. AI allows banks to identify suspicious activities and abnormal transaction patterns more accurately and efficiently than ever before.

AI in action: HSBC, in collaboration with Google, developed the Dynamic Risk Assessment platform, which screens billions of daily transactions for potential fraud and money laundering. This technology has enabled the bank to uncover previously undetected financial crimes with greater speed and precision than human investigators. Similarly, Deutsche Bank’s Alpha-Dig program uses AI to assess trends, risks, and context in global financial activities, helping banks respond proactively to market threats and illegal activity.

Compliance and regulatory reporting

AI is becoming a critical tool for banks to navigate complex regulatory requirements and combat financial crime. By automating the analysis, reporting, and documentation of large datasets, AI helps banks dismantle criminal networks and assists governments in tracking and seizing laundered assets.

AI in action: Several European banks, such as Danske Bank and Swedbank, turned to AI-powered compliance solutions after being fined for inadvertently laundering billions of euros. These banks now use machine learning algorithms to monitor insider trading, detect financial misconduct, and identify subtle indicators of non-compliance. By improving accuracy and reducing the need for manual oversight, AI enables banks to stay ahead of regulatory changes and operate more efficiently.

Challenges in implementing AI solutions in banking

As miraculous as the use of AI in banking sounds, it is not always easy to put into action. As has been the case in other industries, a bank’s leadership and organizational culture are more likely to hinder than help in implementing AI in the banking sector. There are also the technical aspects, with many analysts pointing to an AI skills gap. Going even further, the impossible conundrum of having to speed everything up (analytics, customer service, reporting) while maintaining the same, if not better, standard of service and compliance would be challenging for any industry.

To achieve this optimization level while maintaining a high-performance standard, banks must first address several layers of a development stack to ensure AI is deployed equally without upsetting the balance between security and agility. The following table showcases four distinct levels that AI can penetrate to improve service delivery and what actions banks must take to effectively introduce AI into their legacy systems.

Another potential problem is the ever-changing regulatory framework that will likely govern the role of AI in banking. The problem is that lawmakers are struggling to keep up with the nimble movement of AI advancements, which occur almost daily, just like when Italy banned ChatGPT for weeks over data privacy concerns. The Italian government was forced to put the brakes on in response to a threat they could not fully understand, as it took weeks for the service to come back online after the intervention of ChatGPT creators OpenAI and Microsoft.

A strategic framework for AI excellence in banking

The role of AI in banking is set to deepen, and soon, become implacable. For financial institutions to remain competitive, embracing AI’s transformative power must be done with a structured approach that prioritizes comprehensive, enterprise-wide innovation over isolated experiments. Banks that adopt this strategic mindset will reap the benefits of AI in banking, creating enduring advantages and forging stronger connections with their clients, even in an increasingly challenging economic climate.

Establish a cohesive, top-down AI strategy

The future of AI in banking is unfolding rapidly, with an urgent call for banks to focus on a unified AI approach. However, the enthusiasm among shareholders, employees, and boards to adopt AI solutions for banks can lead to a scattered approach, as financial institutions face hundreds of pending AI applications.

To maximize the impact of artificial intelligence in banking, executive leadership needs a clear, top-down AI strategy. This involves establishing how AI in banking and finance can be systematically leveraged to secure long-term competitive advantages and differentiate in the market.

Instead of deploying AI projects in isolation, a strategic vision can ensure that the role of AI in banking drives continuous value and fosters innovation aligned with the bank’s objectives.

Transform entire processes, not just single applications

For AI in the banking industry to generate real impact, banks must shift from experimenting with one-off applications to rethinking entire processes.

Many banks started their AI journey with individual tools or prototypes; however, the true benefits of AI in banking come from reimagining processes end-to-end. For example, in AI investment banking, generative AI can streamline workflows across the deal lifecycle—from initial client outreach to due diligence and valuation, optimizing every step of the process. By embedding AI-driven solutions across full workflows, banks can more effectively track performance improvements against strategic business goals, providing a more consistent and valuable experience for customers and employees alike.

This approach avoids the pitfalls of piecemeal adoption and enables a cohesive, efficient use of AI in banking that scales across the organization.

Decide when to buy and when to build for a competitive edge

With rapid advancements in AI banking technology and an influx of new tools, banks must strategically decide between buying ready-made AI solutions and building custom models in-house.

Purchasing AI technology in banking can be a cost-effective solution for applications where standardization meets the bank's needs, especially when these tools are mature and ready for immediate deployment.

On the other hand, creating tailored AI banking technology solutions may offer a significant edge in areas unique to the bank, such as regulatory compliance or risk management. For example, custom-built AI models, such as Dynamic Risk Assessment and COiN, that enhance anti-fraud systems can give banks an advantage by enabling better compliance and customer protection.

Additionally, banks must invest in data governance frameworks, especially for unstructured data, to fully leverage the future of artificial intelligence in banking and maintain control over data privacy and security. These investments pave the way for sustainable, scalable AI banking solutions that deliver high-value results.

Scale AI capabilities from Proofs of Concept to full-scale products

While a proof of concept (POC) can quickly showcase AI’s potential, the impact of artificial intelligence in the banking sector is only fully realized when AI solutions can scale to enterprise-grade products. Building robust AI in banking industry applications requires thoughtful engineering, rigorous testing, and adaptation to reach high accuracy and reliability standards.

For example, while an initial AI-driven tool for loan processing might achieve moderate success in a demo, significant engineering work—such as fine-tuning and implementing security measures—may be needed to make it viable for high-stakes financial decision-making.

Banks must prioritize high-value projects, deploy reusable components, and consider establishing AI Centers of Excellence to maintain consistency and accelerate delivery across their AI portfolio. This approach can significantly reduce the time and resources needed to bring impactful AI banking technology to full scale, ensuring that generative AI contributes to the bank’s bottom line.

Vodworks: your fast lane to becoming an AI-first bank

As the banking industry delves deeper into the future of AI, one thing is clear for every organization: in the journey toward AI transformation, data is the foundation.

Data governance, faultproof data pipelines, and compliance with data privacy regulations is a top priority for financial institutions planning to adopt AI. Experience shows that successful AI integration often requires collaboration with expert technology partners.

Vodworks is a global software development company and a trusted partner specializing in data engineering and AI enablement, helping businesses prepare and optimize their data for AI implementation.

With 150+ experts across 4 R&D centers, Vodworks supports companies in Europe, Asia Pacific, and the Middle East, ensuring their data infrastructure is ready for efficient AI integration. Serving industries like banking, telecom, healthcare, and media, Vodworks team has extensive experience with industry-specific data.

Vodworks’ key services include:

• Data pipelines: Building systems for real-time data flow and centralized storage.
• Data warehouse: Setting up a single source of truth for accurate AI data processing.
• Data cleaning and normalisation: Using AI to normalize and prepare data for analysis.
• AI enablement: Deploying tailored AI models to meet specific business needs.

Leveraging their expertise in data engineering, Vodworks’ team helped True Digital optimize 5 trillion data points and reduce data infrastructure costs by 50%.

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Reach out today to discover how Vodworks can help your fintech company implement AI.

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