The banking industry is projected to be more affected by artificial intelligence (AI) than other sectors. Retail banking, one of its largest segments, is no exception.
The use of AI in retail banking has been growing due to the unique challenges that have emerged in recent years. Operating costs have risen, while profit margins have shrunk as central banks have lowered interest rates. To remain competitive and sustain revenue growth in these conditions, retail banks must focus on strengthening customer relationships through building more profound and meaningful connections. This makes the adoption of innovations like artificial intelligence critical.
In this article, we’ll highlight key AI and Gen AI use cases in retail banking and guide you through the process of implementing this technology in the organization based on proven practices developed by Neontri experts.
Key takeaways:
- Retail banks can use AI technology in many ways, such as improving credit scoring, fraud detection, customer service, personalization, and onboarding for new customers.
- Implementing artificial intelligence is a complex process that requires careful preparation, including upgrading infrastructure, managing data effectively, and strengthening security before developing AI models.
- Artificial intelligence is already making an impact in retail banking, and trends suggest its role will continue to grow, especially with the automation possibilities offered by generative AI.
Strategic importance of AI for retail banking competitiveness
AI has become the defining factor separating leaders from laggards in retail banking. Financial institutions that see AI as just another technology tool miss the bigger picture. AI has become a strategic imperative that drives operational efficiency and shapes long-term market position. Banks effectively applying AI report profitability gains of up to 34% in key product lines, while slower adopters risk losing customers to more personalized and efficient digital-first competitors.
Beyond streamlining operations, AI underpins the next generation of banking services. Predictive analytics anticipate customer needs and personalization engines build loyalty by making switching less attractive.
Market leaders like JPMorgan and Bank of America are already reshaping business models around AI, creating sustainable advantages. For others, the message is clear: invest in AI now or risk losing relevance as AI-native challengers and tech giants capture market share.
A critical component of this investment involves transitioning from manual reviews to automated KYC software for banks, which reduces operational friction while strengthening regulatory compliance.
AI use cases in retail banking: From fraud detection to personalization and beyond
Deloitte reports that artificial intelligence is no longer just a tool within the strategy of leading innovators in the banking and financial services industry. It has become a “determinant of strategy,” with market leaders shaping their long-term goals around AI technology.
However, even if your retail bank isn’t at that stage yet, you can still start leveraging it for specific use cases before fully transitioning to an AI-first approach. Below, we outline the key ones.
AI predictive analytics for retail banking
Predictive analytics driven by artificial intelligence helps finance teams anticipate customer needs, spot trends, and manage risks before they become problems. In contrast to traditional tools that mainly rely on historical data, predictive models analyze patterns to forecast future events.
Banks use this technology to spot early signs that a customer might leave, such as fewer transactions, lower balances or changes in spending habits. It also helps predict cash needs at different branches, adjust staffing levels based on expected traffic, and identify which customers may be at risk of missing loan payments or overdrawing their accounts.
Wells Fargo uses AI-powered predictive analytics in its mobile app to provide personalized financial insights and alerts. It analyzes a customer’s spending, income, and account activity to help them stay on top of their finances.
For example, if the system notices that rent is due soon but the account balance is low, it can suggest transferring funds or using overdraft protection. It also forecasts upcoming bills, flags unusual activity, and predicts future balances
Credit scoring and loan approvals
Traditional credit scoring systems rely on a limited set of financial variables, such as credit history, loan types, and outstanding balances, to evaluate a borrower’s creditworthiness. This approach overlooks a wealth of customer data that could provide deeper insights into their financial health. These systems also fall short when it comes to credit scoring of people with non-traditional financial backgrounds, such as gig economy workers.
AI-powered scoring tools, in contrast, can analyze vast amounts of data from multiple sources. For instance, institutions can examine customers’ spending patterns, employment records, payment history for rent or utilities, and more. Based on this comprehensive analysis, AI models can predict the likelihood of loan defaults and suggest more reliable credit scores, enhancing risk management for retail banks.
JPMorgan uses AI to improve how it assesses credit risk. Instead of relying only on income and credit history, its machine learning models also analyze spending habits, cash flow, and, where allowed, social media activity to get a fuller picture of each borrower.
This helps the bank predict risk more accurately, speed up loan decisions, and cut costs. The system also learns from new data over time, so its predictions keep improving as conditions change.
Fraud detection and prevention
Сybercriminals have learned to weaponize artificial intelligence technology to commit financial fraud. According to Deloitte, generative AI could enable bad actors to cause losses of up to $40 billion in the US alone by 2027.
Some cases have already occurred. In one, fraudsters stole as much as $25 million from a Hong Kong-based firm by using a deepfake of the company’s CFO authorizing the transfer. What can retail banks do to protect themselves and their customers?
The solution is obvious: financial institutions must be even quicker with implementing AI to strengthen security and fraud prevention. And the Biotch survey shows that 73% of financial institutions already use such technologies to fight fraud.
AI systems can process vast datasets at lightning speed, analyzing and detecting anomalies in real time. By quickly identifying and flagging customer behaviors that deviate from normal patterns, they allow banks to take proactive measures against fraud and prevent financial losses. For example, if a customer’s account suddenly shows multiple purchases from various locations, the system can mark this activity as suspicious, enabling banks to respond promptly.
Mastercard is using generative AI to spot fraud faster and more accurately, especially when cards are compromised. The system scans data from billions of cards and millions of merchants to detect unusual patterns and predict which cards might be at risk. This helps banks act quickly and block fraud before it spreads.
As a result, Mastercard doubled its detection rate for compromised cards and improved overall fraud detection by up to 300%. It also cut false positives by over 85%, so real customers aren’t mistakenly blocked.
While these improvements are vital, implementing a robust agentic AI strategy for risk management is becoming essential to defend against the increasingly complex tactics of modern attackers.
Customer service
AI-powered banking chatbots go beyond just answering frequently asked questions; they are capable of handling complex customer service inquiries by considering contextual nuances and providing human-like responses. Machine learning (ML) algorithms, which underpin these AI virtual assistants, can analyze transaction patterns and histories, communication logs, demographics, and online behaviors to extract valuable customer data. As a result, retail banks can leverage artificial intelligence tools to ensure customers receive quick, tailored, and effective assistance.
Here’s what an AI chatbot can do in retail banking:
- Explain the terms of financial services.
- Help customers initiate transactions.
- Offer tailored loan options or investment opportunities.
- Calculate monthly loan payments.
- Assist in day-to-day account management.
One of the most well-known AI-powered assistants in the retail banking sector is Erica, a chatbot developed by Bank of America. Erica provides personalized financial guidance and assistance, such as sending bill reminders, notifying them of changes in recurring charges, and offering weekly updates on monthly spending. Since its launch in 2018, Bank of America reports that Erica has assisted 42 million clients.
However, AI-powered chatbots benefit not only customers but also retail banks. By increasing customer engagement, AI in consumer banking creates numerous upselling and cross-selling opportunities. Virtual assistants also enable banks to reduce labor costs while enhancing customer satisfaction.
Customer onboarding and KYC
Onboarding is pivotal in shaping customer experiences in the retail banking sector. Slow onboarding processes can lead to decreased customer acquisition and retention rates, negatively impacting the bank’s bottom line. Implementing AI is one effective way for banks to expedite this process and enhance the customer experience from the very first touchpoint.
Here are just a few examples of how using AI in retail banking can enhance customer onboarding:
- Guiding customers in real time as they fill out forms.
- Reducing manual data entry by automating data capture from documents.
- Speeding up KYC checks and document verification through rapid analysis of submitted information.
- Offering personalized service recommendations.
Moreover, AI financial systems are effective in the re-KYC compliance process. By performing continuous transaction monitoring, document processing, and risk assessments, they make sure that customer information and status remain up-to-date. This helps consumer and retail banks ensure regulatory compliance while maintaining operational efficiency. AI also enables automated solutions for adverse media checks, helping retail banks identify potential reputational risks and comply with AML regulations more efficiently.
HSBC has used AI to modernize its customer onboarding and KYC process, turning what was once a slow, paper-heavy task into a quick digital experience.
New customers can now upload ID documents like passports or licenses through an app or online portal. AI checks these instantly for authenticity, pulls out key data, and flags any issues. At the same time, biometric tools like facial recognition verify identity in real time by comparing a selfie to the document photo. This replaces manual reviews and speeds up account setup.
By automating these steps, HSBC has made onboarding easier and safer, for both customers and staff.
Personalization
According to eMarketer, 74% of customers want their banks to provide more personalized experiences, and 66% are open to banks using their data to achieve this. How can implementing AI in retail banking enhance personalization?
One clear application is offering tailored loans. By leveraging big data, AI tools analyze a customer’s financial profile and use these insights to deliver loan offers that align with individual needs, risk appetite, and creditworthiness.
Retail banks can also use AI-driven insights to receive a deeper understanding of customer context and needs and, thus, upsell and cross-sell relevant products and services. For instance, a customer’s spending habits and payment discipline may reveal that they are ready to upgrade from a basic credit card to a premium one.
Ally Bank offers an AI-powered Smart Savings Tool that provides personalized financial advice by analyzing customers’ spending habits and financial goals. It automates money movement and savings recommendations, helping customers optimize their finances in a highly individualized way.
In summary, retail banks, both large and small, must integrate AI technology into their digital strategies to meet customer expectations and gain a competitive edge, ensuring profitability and growth.
Implementing AI in the retail banking sector: Steps and best practices
Adopting AI in consumer banking requires expertise and precision due to the industry’s complexity and stringent regulatory requirements. Below, we outline the critical steps, challenges, and best practices you need to consider to implement it smoothly.
Step#1: Prepare infrastructure
It’s essential to closely examine a bank’s existing infrastructure before implementing AI. Start by assessing core financial systems to identify any limitations that may hinder AI adoption. For instance, many retail banks rely on legacy software and monolithic applications, which often lack the scalability, flexibility, and processing power necessary for effective AI integration.
Identifying these limitations will help institutions create a preparation roadmap. Depending on the current state of infrastructure, banks may have the following options:
- Migrating to the cloud or transitioning to a hybrid cloud infrastructure that supports large-scale data processing and machine learning models.
- Modernizing IT infrastructure by transitioning from monolithic to microservices architectures.
- Replacing outdated systems with modern alternatives—either off-the-shelf solutions or custom-built options.
Additionally, effective AI implementation requires a strong infrastructure that supports real-time data access across all business units. This enables AI systems to make timely, informed decisions based on the most up-to-date information.
The preparation stage may be lengthy and require significant investments. However, AI initiatives may not yield the expected results without a solid foundation.
Step#2: Set up effective data management
Artificial intelligence models need large volumes of quality data to provide accurate, unbiased insights. The challenge is that retail banks often collect structured data (e.g., transaction records and customer profiles) and unstructured data (e.g., emails and social media customer interactions) from multiple sources. This data is frequently fragmented, siloed, unclean, and inaccessible. So, to move forward with AI implementation, it’s crucial to organize it effectively.
Here are the necessary steps:
- Implementing a unified data ingestion pipeline to streamline the collection of data from diverse data sources.
- Consolidating data from different sources into a data lake or data warehouse to form a centralized source of information.
- Setting up data cleansing and normalization processes to ensure consistent data quality.
- Developing a data governance framework that defines clear policies for data ownership, access control, and data retention.
- Optimizing data availability and accessibility by setting up storage solutions and APIs that allow artificial intelligence models to access data efficiently.
Yet, the data the bank gathers may not be sufficient for AI training. In this case, you’ll likely need to incorporate synthetic data—artificially generated data that supports AI models in scenarios where real-time data is scarce (e.g., fraud detection). Using synthetic data also helps you meet compliance requirements.
Step# 3: Ensure cybersecurity and data privacy
Implementing artificial intelligence in retail banking also requires securing data at rest and in transit, as AI systems can be vulnerable to cyberattacks. Here are key measures to consider:
| Security measure | Description |
| End-to-end encryption | Ensures that data is encoded from the point it’s created to when it reaches its endpoint, protecting it from being accessed by unauthorized users even if intercepted. |
| Multi-factor authentication (MFA) | Requires at least two different types of verification (one-time passwords, biometric authentication, push notifications), reducing the risk of unauthorized access. |
| Access control and role-based access | Restrict access to AI systems and data based on user roles to ensure only authorized employees can access protected information. |
| Network security monitoring | Involves continuous surveillance of network traffic for suspicious actions to identify and respond to threats in real time. |
| Intrusion detection and prevention systems (IDPS) | Detect and, in the case of prevention systems, actively block potential intrusions by identifying suspicious patterns and behaviors. |
| Endpoint protection | Involves securing all devices (endpoints) connected to the retail bank’s network, such as computers, mobile devices, and ATMs, with measures like antivirus, anti-malware, and firewalls. |
Another critical aspect is ensuring your intelligent banking software complies with regulatory requirements. Key legislation to consider includes the General Data Protection Regulation (EU), the California Consumer Privacy Act (US), and the Personal Information Protection and Electronic Documents Act (Canada). Strict compliance with these regulations is essential for successful AI adoption, as it helps prevent legal issues, builds customer trust, and supports the long-term success of AI initiatives.
Expert tip: Build an AI risk management culture. AI introduces new types of risks not present in traditional banks, so every banker must understand their role in managing them. Employees should be trained to spot potential issues, such as biased AI algorithms, poor data quality, or questionable system recommendations, and feel confident raising concerns.
Step #4: Develop and train AI models
Once infrastructure, data, and security measures are in place, the next critical step is designing and training AI models. Here’s a simplified overview of this process:
- Defining use cases. Determine the specific problems the artificial intelligence software will address for the retail bank, such as fraud detection, credit scoring, or improving customer experience with personalization.
- Selecting an algorithm. Choose the appropriate machine learning or natural language processing algorithms based on the defined use cases. Your development team will handle this, so it’s essential to engage experienced AI experts in the project.
- Training and testing. Use both gathered and synthetic data to train the models, followed by extensive testing to ensure the accuracy of the results.
- Rinsing and repeating. Continuously refine the models based on feedback, addressing any weaknesses or biases that may arise.
Expert tip: Don’t forget to train your workforce. Invest in comprehensive education programs to make sure the teams can confidently work with AI systems. Many banking professionals may feel unsure about using AI, and without the right training, employees might resist new tools or use them the wrong way. Every employee essentially becomes an AI risk manager who needs to understand how these systems work and what might go wrong. So, focus on basic AI knowledge for everyone, targeted training for different roles, and ongoing learning to keep pace with technological advances in AI.
Overall, implementing artificial intelligence in fintech is a complex process filled with challenges. However, the AI’s potential returns make the effort worthwhile.
AI in consumer banking: Key challenges to watch
While there are clear benefits of AI in consumer banking; its adoption also introduces challenges that banks must carefully manage. From legal to technical, understanding these is key to safe and successful implementation.
Legal and regulatory uncertainty
Banks often use customer and third-party data to train AI models, but it’s not always clear who owns this information and how it can be used. With regulations still changing, navigating issues like liability, data consent, and fairness standards can be challenging.
Limited control over AI accuracy
There’s no guarantee that AI models produce results fully understanding data and context. This makes it difficult to explain outcomes, especially in sensitive situations like loan approvals or fraud detection, raising concerns about reliability and accountability.
Risk of bias in AI decisions
AI can pick up on biases in past data, which can lead to unfair results, particularly in lending. If nothing is done about it, this could be unethical and even break fair lending rules.
Data security and system integration issues
Many banks still have to deal with data that is fragmented, inconsistent, or outdated. This affects the performance of AI systems and makes integration with existing infrastructure more complex. At the same time, growing system complexity increases the risk of data breaches, raising serious concerns about security and compliance.
Talent gaps in both AI and banking
There’s a shortage of professionals who understand both AI technologies and the unique needs of the banking sector. This makes it difficult to build reliable systems that align with business goals and regulatory requirements.
High costs and unclear ROI
Building and maintaining AI solutions requires large upfront investments for infrastructure, talent and tools. As a result, predicting returns can be difficult due to added costs such as compliance, model retraining, and system upgrades.
Key challenges of AI implementation in retail banking
Successfully adopting AI requires navigating complex technical, regulatory, and organizational hurdles. Retail banks must prepare for challenges like data privacy concerns, legacy system integration, and talent shortages, among others. Understanding these and applying strategic solutions is vital to realizing the full benefits of AI-driven transformation.
| Challenge | Description | How to overcome |
| Legacy system integration | Outdated core banking systems often lack APIs and flexibility needed for AI integration, creating technical bottlenecks. | Adopt microservices architecture, migrate to API-first platforms, and implement hybrid/multi-cloud solutions for flexibility and scalability. Banks that implement such modernization report up to 35% faster data processing and significantly enhanced customer personalization. |
| Data quality and fragmentation | Customer data exists in silos across departments with inconsistent formats, incomplete records, and quality issues. | Establish unified data governance frameworks, implement data lakes or data meshes, apply robust data cleansing, and create centralized customer 360 platforms. |
| Regulatory compliance complexity | Navigating evolving AI regulations while maintaining compliance with existing banking laws creates legal uncertainty. | Partner with RegTech specialists, adopt explainable AI models, and establish dedicated compliance teams for AI governance. These measures can reduce compliance risks and audit findings by 25–30%. |
| Talent shortage | Scarcity of professionals who understand both AI technologies and banking domain expertise. | Invest in employee upskilling, partner with specialized AI firms, and build cross-functional teams blending banking and tech expertise. This can accelerate AI project delivery by 20% or more. |
| Security and privacy risks | AI systems create new attack vectors and require handling sensitive customer data at unprecedented scales. | Enforce end-to-end encryption, deploy AI-specific anomaly and threat detection systems, adopt zero-trust security architectures, and continuous monitoring. |
| Change management resistance | Employees may resist AI adoption due to job security concerns or lack of understanding about the technology. | Develop training programs, communicate AI benefits clearly, and involve staff early in implementation, which can raise AI acceptance and improve service efficiency by up to 70%. |
| ROI measurement difficulties | Quantifying AI’s impact on customer satisfaction, risk reduction, and operational efficiency proves challenging. | Establish clear KPIs before implementation, use A/B testing for AI features, and implement robust analytics to track both quantitative and qualitative benefits. |
Using AI in retail banking: Future trends
Retail banks are rapidly adopting AI to stay competitive and keep pace with technological advancements, creating a dynamic market. Here are some of the key trends to watch:
- Trend #1: Full-scale automation with generative AI. Almost three-quarters of work in banking can be automated or augmented with generative AI, making the industry most likely to be impacted by this technology. Unsurprisingly, banking was one of the sectors in 2023 with the most significant investment in GenAI, amounting to $20.6 billion.
- Trend #2: More opportunities, more challenges. Generative AI is expected to bring $200-340 billion of value to the global banking sector annually. Banks are racing to implement the technology but are facing challenges, such as inaccurate data and security concerns.
- Trend #3: Focus on operations. The business area in the financial services industry with the highest AI adoption rate is operations (48%), showing that the primary concern in the sector is efficiency.
- Trend #4: Leading banks are setting the pace. Of all the large banks in the Americas and Europe, Capital One has come out as the most prepared to embrace artificial intelligence, receiving a readiness index score of 90.91. This was based on several criteria, including dealmaking activity, product launches, and key people.
- Trend #5: Banks will highlight their use of AI to stay competitive. More banks are starting to talk openly about how they embrace AI. It’s becoming a new way to build trust, show innovation, and stand out from competitors. Sharing this information helps attract both customers and top talent.
- Trend #6: AI will reshape how banks work and stay profitable, marking a new era of digital transformation. It helps banks streamline operations by automating repetitive tasks, improving decision-making, and reducing costs. Institutions that use AI well will be more efficient and are likely to stay ahead in a changing market.
Big players are getting ahead of the race to adopt AI. Of all the large banks in the Americas and Europe, Capital One has come out as the most prepared to embrace artificial intelligence, receiving a readiness index score of 90.91. This was based on several criteria, including dealmaking activity, product launches, and key people.
But no matter the size of your organization, you need to catch up or lose to the competitors. Working with the right development team will help you get past the challenges while embracing the growth opportunities.
Build your AI capabilities with Neontri
As financial services continue to evolve, embracing artificial intelligence is essential for companies that want to stay on top of the game. However, many companies lack the internal expertise to implement such solutions effectively. Therefore, partnering with a reliable development team is essential to capturing the full value of this technology.
Neontri specializes in delivering advanced custom software development services and solutions across various domains, including GenAI development services, custom software development, and third-party integration services.
Our team brings a wealth of experience in creating and implementing AI solutions specifically tailored for the banking industry. By working with Neontri, companies get access to cutting-edge technologies, 10+ years of expertise, and industry best practices that allow them to integrate AI capabilities with existing systems seamlessly.
Final thoughts
Leveraging AI can significantly enhance how retail banks approach fraud prevention, credit scoring, customer service, onboarding, and personalization. With many industry players already embracing this technology, failing to adopt it may result in losing ground to competitors.
FAQ
What is the impact of AI on customer trust in banks?
AI enables banks to revolutionize various touchpoints in the customer journey, making the processes much more user-friendly and customized to individual needs and preferences. The positive experience makes the customer much more inclined to trust the bank and stay loyal to it.
