Reshaping Banking and Financial Services with Generative AI

Last updated: June 2026

Generative AI has moved from novelty to infrastructure in financial services… teams now build on the model that fits the job — Claude, GPT, Gemini, or an open model — and increasingly combine large language models with autonomous agents and retrieval-augmented generation (RAG) grounded in a bank's own data.

Financial institutions now have the tools to enhance their operations, streamline processes, harness data insights, and cater to clients with personalized experiences, thus driving customer satisfaction, building trust, and fostering loyalty.

In today's competitive market, offering personalized services tailored to individual customer needs and preferences is essential for success, and technologies like generative AI empower financial institutions to deliver these crucial experiences, leading to higher customer retention rates and a stronger competitive position within the industry.

But whenever we adopt new technology, it matters to understand not only the opportunities but also the risks and the guardrails. That is doubly true in a regulated industry. At Wolfpack Digital, we build AI features as an AI-native product partner, which means every model output is reviewed and signed off by a senior team before it reaches production — non-negotiable when the output affects someone's money or creditworthiness. In the sections that follow, we look at where generative AI transforms finance, and the controls each use case demands.

What is generative AI?

Generative AI is a form of artificial intelligence that can create new content by learning from existing examples, mimicking human creativity. It finds applications in industries like media, e-commerce, healthcare, gaming, design, and more, enabling personalized recommendations, creative content generation, and enhanced virtual assistants. In banking specifically, generative models are increasingly paired with agents that take multi-step actions and with RAG systems that answer using a bank's own documents and policies, without exposing that data. Its ability to innovate and automate processes makes it a valuable asset across various sectors.

Now, let’s look at some key areas in which generative AI is impacting the finance industry and a few aspects that advise caution:

Improved Customer Onboarding

Customer Onboarding is the process of enhancing and streamlining the initial experience of new customers, facilitating a smooth and efficient transition to a product or service, resulting in higher satisfaction and engagement.

Generative AI can streamline and enhance the digital customer onboarding process by automating document verification. AI-powered systems are able to extract relevant information from documents like identification cards, passports, driver's licenses, and utility bills, making it easier, faster and more efficient to verify customer identities. This leads to quicker and smoother onboarding experiences for customers, reducing the need for manual document checks and paperwork.

In addition to its document verification capabilities, generative AI can also significantly enhance the customer's digital onboarding process through biometric verification methods. These methods, including facial recognition and fingerprint scanning, offer a robust and secure way to validate customer identities and add an extra layer of security that effectively mitigates the risks of identity theft and fraud.

Upsides:

🚀 A faster and more efficient onboarding process, minimizing waiting times for customers.

✅ Enhanced accuracy in document verification, decreasing the risk of identity fraud.

💰 Lower operational costs for banks due to automated verification and lesser manual intervention.

Things to look out for:

🔒 Concerns about data privacy and security when using AI to process sensitive customer information.

🤖 Potential biases in AI algorithms could lead to unfair decisions during the onboarding process.

Enhanced Personalization

Personalization refers to tailoring products, services, or content to meet individual preferences, needs, and characteristics, enhancing the user's experience.

Generative AI can analyze vast customer data and transaction histories to gain insights into individual preferences and behaviours. Banks, financial institutions and fintech startups can capitalize on this information to better understand their customers. This understanding allows them to personalize their services, offers, and product recommendations, resulting in higher levels of customer satisfaction and loyalty.

For instance, banks can use AI models to analyze a customer's browsing behaviour, spending patterns, investment preferences, and financial goals to suggest suitable products and services, such as credit cards, loans, or investment opportunities.

But there are other options, too, from personalized user interfaces that tailor the layout, content and offers based on individual preferences all the way to predictive analytics that anticipate customer needs and provide proactive solutions, such as reminding customers of upcoming bill payments or suggesting suitable financial products.

Upsides:

🤝 Improved customer satisfaction and loyalty due to personalized offerings.

📈 Higher conversion rates as customers receive offers tailored to their needs and preferences.

📊 Increased revenue for banks through cross-selling and up-selling opportunities.

Things to look out for:

🔐 Risks of privacy infringement if customer data is not adequately protected.

🤖 Overreliance on AI recommendations may lead to the neglect of human expertise and personalized customer service.

Natural Language Processing (NLP) in Customer Service

Natural Language Processing (NLP) in Customer Service is the application of AI and language understanding technologies to enable automated, efficient, and personalized interactions between customers and support systems.

Banks are using generative NLP models to enhance their customer service capabilities through chatbots and virtual assistants, which reduce the workload on customer service agents and allow banks to provide round-the-clock support and interact with customers in a conversational manner.

NLP enables chatbots to understand user intent and respond appropriately to customer queries and requests, provide account information, and assist with basic banking tasks without the need for human intervention.

Another use of NLP is to help banks gauge customer satisfaction and sentiment expressed in various channels, aiding in service improvement through sentiment analysis.

Upsides:

🌐 Improved accessibility and responsiveness due to the 24/7 availability of customer support

💸 Cost-effective customer service solution, reducing the need for a large support team.

⚡ Faster response times for routine queries, leading to better customer experiences.

Things to look out for:

🤖 Limitations in chatbot capabilities for complex queries or emotional support.

😓 Risk of frustrating customers if chatbots fail to understand or provide adequate responses.

Fraud Detection and Prevention

Fraud detection and prevention refer to the process of identifying and stopping unauthorized or deceptive activities to safeguard against financial losses and protecting individuals and organizations from fraudulent behaviour.

Fraud detection in the financial industry relies heavily on manual efforts, where analysts have to manually review transactions using rule-based systems. This results in slower response times, limited data analysis capabilities, and higher false positive rates, leading to challenges in identifying complex fraud patterns and staying ahead of evolving threats.

Generative AI can be employed to detect and prevent fraudulent activities in real time. By continuously analyzing transaction patterns and user behaviour, AI models can identify unusual or suspicious activities, enabling banks and institutions to take immediate action to safeguard their customers and assets.

For example, if a transaction deviates significantly from a customer's usual spending behaviour, the system can trigger an alert for further investigation or even block the transaction if it appears fraudulent.

Upsides:

⏱️ Real-time fraud detection, leading to prompt action and mitigation of losses.

👮 Improved security for customers and their assets, enhancing trust in the offered services.

💰 Reduced financial losses and reputational damage due to timely fraud prevention.

Things to look out for:

⚠️ False positives in fraud detection could inconvenience legitimate customers.

🕵️‍♂️ Advanced fraudsters may find ways to bypass AI-based detection systems, requiring ongoing improvement and adaptation.

Risk Assessment and Credit Scoring

Risk assessment is the process of evaluating the potential risks associated with lending money to a borrower to determine the likelihood of default, while credit scoring is the use of statistical models to quantify a borrower's creditworthiness based on historical credit data.

Risk assessment and credit scoring are largely manual and rely on conventional statistical models, historical data, and fixed criteria to evaluate creditworthiness, leading to limited data analysis, uniform evaluations, slower decision-making, and higher potential for human biases in the process.

Generative AI can improve risk assessment and credit scoring processes. By examining historical data and customer behaviour, banks can more accurately predict creditworthiness and enable real-time, personalized, and unbiased evaluations based on comprehensive data analysis, ensuring more accurate predictions and inclusive lending practices, reducing the risk of default.

Upsides:

🎯 More accurate credit scoring, leading to better-informed lending decisions.

🛡️ Reduced risk of defaults and loan delinquencies, benefiting the bank's financial health.

🌟 Improved access to credit for customers with limited credit history.

📋 Credit scoring and creditworthiness assessment are classified as high-risk uses under the EU AI Act.

Things to look out for:

⚖️ Potential biases in AI algorithms that could result in discriminatory lending practices.

🕶️ The lack of transparency in AI-driven credit scoring models makes it challenging to explain decisions to customers.

Portfolio Management

Portfolio management refers to the professional management and diversification of clients' investments to achieve their financial objectives and optimize returns while considering their risk tolerance.

Traditional portfolio management lacks personalization, struggles with data analysis and scalability, cannot react quickly to market changes, and faces challenges in efficiently managing risks, among other limitations.

Generative AI can assist banks in managing investment portfolios and developing investment strategies more effectively. By evaluating market trends, economic indicators, and historical data, AI models can provide insights into asset allocation and portfolio performance, helping banks and financial institutions come up with more informed investment decisions.

Upsides:

💹 Data-driven investment decisions, leading to potentially higher returns for customers.

🔄 Improved diversification and risk management in investment portfolios.

💡 Access to more sophisticated investment strategies for a wider range of customers.

Things to look out for:

📉 AI models may struggle to account for unforeseen market events or black swan events.

👁️‍🗨️ Overreliance on AI-generated insights without human oversight may lead to suboptimal decisions.

Regulatory Compliance

Regulatory compliance in the context of banks refers to adherence to the laws, rules, and guidelines set by regulatory authorities so the bank operates ethically, securely, and within the legal framework.

Compliance is often plagued by manual processes, high costs, reactive approaches, and challenges in handling big data, leading to inefficiencies, potential errors, and increased risk of non-compliance. Generative AI can help: it can automate reporting tasks, enhance data processing, provide real-time transaction monitoring, and strengthen the AML/KYC checks that sit at the heart of financial compliance.

What matters in 2026 is that the regulatory landscape AI has to operate inside is now concrete, and a financial product touches several frameworks at once:

• EU AI Act — in force, with a risk-based structure. AI used for credit scoring and creditworthiness assessment is classified high-risk, triggering obligations for data governance, transparency, human oversight, and record-keeping.
• DORA (Digital Operational Resilience Act) — applies to EU financial entities and their critical ICT providers, setting requirements for operational resilience, incident reporting, and third-party risk.
• PSD2 and open banking — govern secure access to payment data and strong customer authentication.
• GDPR — governs how customer data is collected, stored, and used, which directly constrains how AI models are trained and deployed.

Generative AI can aid banks in ensuring compliance with various regulations and reporting requirements. Incorporating AI can automate tasks, enhance data processing, provide real-time monitoring, and improve risk assessment, making compliance practices more effective and efficient.

Upsides:

🔐 Enhanced security and reduced risk of unauthorized access to accounts.

🎉 Convenient and user-friendly authentication methods for customers.

🛡️ Difficult for fraudsters to replicate biometric features, making it more secure than traditional passwords.

Things to look out for:

🚨 Biometric data breaches could have severe consequences for customers.

🌐 Dependence on voice or biometric authentication could create accessibility issues for customers with certain disabilities.

Conclusions

Generative AI has brought a real shift to financial services, giving institutions stronger ways to serve customers, streamline operations, and build trust — more efficient and personalized services, lower operational costs, better security, and a smoother overall experience. With a mindful approach to the ethical and regulatory considerations above — transparency, bias, data privacy, and the EU AI Act, DORA, PSD2, and GDPR — banks and fintechs can use these technologies responsibly and fairly.

At Wolfpack Digital, this is the work we do. We've built fintech products including the Swiss financial app Everon, a mobile banking app for Banca Transilvania, and the Extra Karte banking app — always with security and compliance built in, and with a senior team reviewing every AI output before it ships.

Should you have a fintech product idea or an existing project you’d like to spice up with AI, we’d gladly take a look. Our portfolio is bursting with relevant projects, from wealth management apps to investment platforms and financial management tools.