A McKinsey report on Generative AI predicts that the technology will lead to $200–340B in annual value, largely due to increases in productivity.
But we’ve all been hearing these promises for a while. The question that everyone wants to know the answer to is how exactly can AI help increase productivity in the banking sector right now.
That’s why we’ve decided to take a look at the top 10 Generative AIs that are helping retail banks in 2025 to drive efficiency, trust, and improve customer experiences.
What is Generative AI in banking?
In banking, Generative AI involves leveraging advanced artificial intelligence to streamline operations, strengthen fraud detection, deliver tailored financial guidance, and boost both efficiency and security across services.
For retail banking contexts, one of the most compelling uses of Generative AI is when it comes to customer experience. By drawing on AI-enhanced tools, agents or advisors are able to get quick, accurate access to information, improve the speed and efficiency of solutions, and interact at a level that was previously impossible in a remote environment.
Why Generative AI Is a must for financial services
We all know about the market trend potential of AI. The above statistic, to put it into perspective, accounts for 2.8 to 4.7 percent of total industry revenues – a staggeringly enormous percentage if you stop to think about it.
But capital market trends aside, there are other solid reasons to lean into the technology in the banking sector. Generally speaking, it comes down to the fact that there’s no sitting on the fence with AI.
If you don’t embrace Generative AI, you won’t be replaced by a fully automated alternative like a robo advisor or a mobile banking app that is 100% AI. Instead, you will be replaced by a company that uses Generative AI better than you do, whether for customer interactions, customer engagement, or with virtual assistants.
This is exactly what happened in the banking industry with the rise of fintech companies which caught incumbent banks off guard, long before Generative AI was even released. Adopting next-gen AI now will not only drive better customer outcomes but also increase operational resilience.
Given this context, what Generative AI tools should retail banks incorporate in 2025?
1. Navigating cybersecurity risks
One of the key capabilities for improving customer trust is an improvement in fraud detection and prevention thanks to Generative AI.
EY groups these functionalities into three areas:
Advanced fraud detection
Real-time data analysis helps uncover patterns and anomalies that indicate risk factors like cyber threats, enabling proactive detection and prevention.
Automated incident response
Streamlines the incident response process, allowing human experts to focus on more complex threats.
Continuous learning and adaptation
Generative AI’s dynamic learning capabilities ensure that security measures evolve with the changing threat landscape in financial services, enhancing their long-term effectiveness.
This shows the growing strategic potential of AI in fraud detection, going beyond popular tools like Feedzai and Kount – which are used by Paypal and American Express respectively for fraud risks during payments.
2. Streamlining onboarding
Generative AI is setting a new standard for retail banking and financial services onboarding in 2025, offering advanced features like intelligent form pre‑filling and automated ID verification to dramatically shorten sign‑up times.
For instance, Generative AI can analyze scanned identity documents and customer-provided details to auto-populate application fields, minimizing manual entry errors and speeding up the process. Once a user submits their ID and a selfie, the system can instantly verify the document match using biometrics, completing KYC checks in minutes rather than days.
This application can be seen in digital banking solutions from Revolut to Amazon, where the latter’s Amazon Bedrock-powered AI assistant guides users through uploading their ID, running automated document and selfie verification, and pre‑filling forms to create a bank account in minutes.
According to Capgemini’s 2024 World Retail Banking Report, banks still spend over 50% of onboarding time on documentation, even though only 4% of new applicants receive same‑day approval – a number that can certainly be improved.
Next-level client servicing and customer service
Features like automated chat summaries and sentiment analysis are emerging as productivity-boosters for agents. During a Live Chat, the AI Co‑Pilot can provide suggested responses, instantly summarize conversation history, and analyze customer sentiments over previous interactions.
This information allows agents to pick up on frustrated or satisfied tone and tailor their follow-up dynamically. This not only accelerates resolution but allows agents to focus more on personalized advisory rather than routine monitoring. Tools like these contribute to the overall increase in agent efficiency, with one bank handling over 20% more support volume and delivering nearly 60 % first‑contact resolution.
4. Portfolio rebalancing software
Generative AI is enabling AI-suggested asset allocation and dynamic portfolio rebalancing for retail or investment banking by adjusting client portfolios based on real-time market insights.
Using techniques like reinforcement learning and adaptive risk budgeting, these systems assess changing correlations, volatility, and unstructured data (such as breaking news) to proactively rebalance assets when risk thresholds are crossed or opportunities emerge.
The US-based private equity firm Quantum Capital integrated a GenAI platform that simulates thousands of market scenarios using deep learning and NLP-powered news sentiment analysis. The system then recommends asset allocations tailored to client risk profiles, automatically executing adjustments. The reports suggest that performance improved by 35% above benchmarks and drawdowns were reduced by around 20% during downturns.
5. Better campaign management
Campaign management in retail banking or commercial banking is seeing a dramatic shift by enabling predictive targeting and churn risk analysis. This, in turn, is empowering banks to deliver personalized, timely campaigns and improved customer service while proactively retaining high-risk customers.
The AI-driven models analyze behavioral or financial data, transaction histories, and sentiment cues to forecast which customers are most likely to respond or churn.
Personalized targeting of customers
One example analyzed by Cornell University follows a “large UK lender” that used CLV-based propensity models, showing the top 10% of customers were 3.2 times more likely to adopt investment products than randomly chosen clients. This allows them to create highly targeted offers with much higher ROI.
Likewise, dynamic micro-segmentation (which is updated in real-time) has improved campaign response by up to 40%. Applications for this for financial institutions include nudging customers abandoning mortgage applications with personalized rate offers.
Improving retention rates
On the customer retention side, AI systems can flag churn risk early by detecting patterns like declining logins, negative sentiment, or product inactivity – and trigger automated retention actions. Current examples of this include platforms like Wizr AI, churn prediction powered by generative models allows banks to deploy tailored retention strategies and offers just in time.
One financial services firm deploying such AI reduced churn up to 20% by enriching CRM data and triggering proactive outreach.
What’s next for Generative AI in banking?
In 2025, retail banks are at a pivotal moment. Generative AI is no longer a future trend, but a present-day necessity.
And the future is not going to be any different.
As the McKinsey report highlights, the potential for $200–340B in annual value isn’t just about long-term projections, it’s rooted in real-time productivity gains, improved customer experience, and smarter digital transformation. Financial institutions that harness AI-driven solutions are set to enhance customer engagement, and improve regulatory compliance.
These tools streamline operations and elevate the quality of human interactions. GenAI applications, built on advanced large language models and natural language processing, empowers advisors to offer more relevant financial advice, improves risk management, and strengthens customer trust.
The choice is clear: banks that integrate these AI models, alongside customer-facing chatbots and personalized product recommendations, into their workflows will lead in customer satisfaction, operational resilience, and competitive edge.
The digital banking era rewards those who act fast. Now is the time for the banking sector to embrace Generative Artificial Intelligence – not just to keep pace with fintech startups, but to redefine what exceptional financial services look like.
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