AI in Retail Banking: Use Cases, Challenges, and Trends

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 AI critical.

In this article, we’ll highlight key use cases of AI in retail banking and guide you through the process of implementing this technology in your 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 AI is a complex process that requires careful preparation, including upgrading infrastructure, managing data effectively, and strengthening security before developing AI models.
• AI 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.

AI use cases in retail banking: From fraud detection to personalization and beyond

​​Deloitte reports that AI 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 AI for specific use cases before fully transitioning to an AI-first approach. Below, we outline the key ones.

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, they 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.

Fraud detection and prevention

Сybercriminals have learned to weaponize AI 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: 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 AI 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, AI systems 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 AI system can mark this activity as suspicious, enabling banks to respond promptly.

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 AI tools to ensure customers receive quick, tailored, and effective assistance.

Here’s what an AI chatbot can do in 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 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, they create 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 retail banks ensure regulatory compliance while maintaining operational efficiency. 

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 can analyze a customer’s financial profile and use these insights to deliver loan offers that align with individual needs, risk appetite, and creditworthiness. This enables retail banks to provide different interest rates based on varying levels of financial health. One study found that using machine learning models for AI-driven decisions on car loans increased dealerships’ profitability by 34%.

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. 

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. The next question is: how can you start implementing AI in your organization?

Implementing AI in the retail banking sector: Steps, challenges, and best practices 

Adopting AI in retail 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 AI smoothly.

Step#1: Prepare infrastructure

It’s essential to closely examine your 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 your 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.

The preparation stage may be lengthy and require significant investments. However, your AI initiatives may not yield the expected results without a solid foundation.

Step#2: Set up effective data management

AI 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 AI models to access data efficiently.

Yet, the data your 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:

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).

Step #4: Develop and train AI models

Once your 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:

1. Defining use cases. Determine the specific problems the AI system will address for your retail bank, such as fraud detection, credit scoring, or improving customer experience with personalization.
2. 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.
3. Training and testing. Use both gathered and synthetic data to train the AI models, followed by extensive testing to ensure the accuracy of the results.
4. Rinsing and repeating. Continuously refine the AI models based on feedback, addressing any weaknesses or biases that may arise.

Overall, implementing AI is a complex process filled with challenges. However, the potential returns make the effort worthwhile.

Using AI in retail banking: Current 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 gen AI, 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. 

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 own organization, you need to catch up or lose to your 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 AI is essential for companies that want to stay on top of the game. However, many companies lack the internal expertise to implement AI solutions effectively. Therefore, partnering with a reliable development team is essential to capturing the full value of this technology.

Neontri specializes in delivering advanced technology solutions across various domains, including GenAI services, custom software development, and third-party integrations. 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. We also offer ongoing support, continuous improvement of AI implementations, and compliance with regulatory requirements.

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.

However, implementing AI comes with some challenges. It requires considerable time and effort to strengthen your core technology, establish effective data management, and ensure robust security before you can begin developing AI models. Contact us to start your AI journey!

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.