Top 5 AI Use Cases for Retail Banking

Artificial intelligence (AI) is becoming an increasingly important tool for businesses across industries, and the retail banking sector is no exception. From improving customer services to fraud detection, AI has a wide range of use cases in this industry. As the competition in the industry continues to intensify, it is crucial for executives to understand the potential of AI and integrate it into their business strategies to remain competitive in the market. In this article, we will explore the top five AI use cases for retail banking and why its adoption by the industry is important.

Discover Hidden Small and Medium Enterprises


AI-powered systems are capable of analysing large volumes of structured and unstructured data, deciphering patterns, and identifying potential business opportunities, enabling banks to identify high-potential SMEs that were previously unnoticed. By monitoring various data sources such as social media, web traffic, and online transactions, AI can predict the demand and growth potential of these businesses, ultimately enabling banks to increase their revenue by offering personalised loan products, risk assessments, and financial services.

In addition, AI implementation streamlines payment processing, enhances fraud detection, and improves operational efficiency, allowing banks to focus on building stronger relationships with SME clients, which in turn results in increased revenues and customer loyalty.

Credit Scoring and Underwriting

Credit scoring and underwriting are two critical areas in retail banking. Banks need to evaluate creditworthiness accurately and make informed lending decisions. In developing credit scores for potential borrowers, the more data that is considered, the more precise credit scores will be. AI can help banks to improve credit scoring and underwriting by analysing vast amounts of data and identifying patterns that are indicative of creditworthiness. For example, AI can assess data like credit history and spending habits. It can also consider alternative sources such as social media activity and geolocation information for a comprehensive evaluation.

In addition to making correct lending decisions, artificial intelligence-based approaches are a better way to ensure that lending decisions are not impacted by biases. By considering large amounts of data efficiently, AI allows banks to make better lending decisions, while also establishing the credibility of decision-making processes.

Optimise Card-On-File Experience


By enabling better fraud detection and prevention mechanisms, AI can analyse user behaviour and spending patterns to identify inconsistencies and potential security threats in real-time, ensuring protection for cardholder information. AI-driven systems can also help banks to offer personalised services and targeted offers based on customers’ purchase history and preferences. This process leads to enhancing customer engagement and satisfaction, resulting in increased transactions and higher customer retention rates.

Additionally, AI can improve the accuracy and efficiency of billing processes through automated data validation and management. Such systems can also optimise risk assessment by predicting the likelihood of declined transactions or late payments and identifying risky clients or merchants, ultimately minimising losses.

Personalised Customer Services

One of the most notable benefits of AI in the banking sector lies in its capacity to deliver personalised customer services. AI-powered chatbots can efficiently manage a broad spectrum of customer queries, offering rapid and precise responses. This efficiency enables banks to allocate valuable human resources to more critical tasks while providing unparalleled customer service.

Furthermore, machine learning algorithms can examine customers’ behaviour, transaction history, and preferences, allowing banks to gain a deeper understanding of individual needs. This insight empowers banks to provide personalised product recommendations, including bespoke credit, savings, or investment options, ultimately enhancing customer satisfaction and fostering long-term loyalty.

Fraud Detection


Retail banks are often the target of fraudsters seeking to exploit vulnerabilities in financial systems. AI can help detect fraudulent activity by analysing vast amounts of data to identify patterns and anomalies that may indicate suspicious behaviour. AI can also continuously monitor banking transactions in real time, allowing it to quickly detect any suspicious activity and flag it for further investigation by bank staff.

Additionally, AI-powered fraud detection systems can learn and adapt to new types of fraudulent activities, helping banks stay ahead of increasingly sophisticated fraud schemes. By using AI in this way, banks can better protect their customers from financial losses due to fraud while minimising the risk of reputational damage to their brands.

Why Implementing AI is Important

The implementation of AI within the banking sector is becoming increasingly important due to emerging digital banking services and the desire to improve customer experiences. The industry is facing a new era of customer expectations that demand personalised, efficient, and secure banking services. Banks struggle to provide true omnichannel experiences, causing customers to choose competitors with more personalised offerings, as highlighted in the World Retail Banking Report 2022 by Capgemini and Efma.

In order to meet these expectations, banks need to adopt new technologies that can streamline their operations and improve their customer experiences. AI can play a crucial role in achieving these goals by leveraging advanced algorithms and machine learning techniques to analyse vast amounts of data in real time, identify patterns and trends, and make predictions about customer behaviour.

evoML for Retail Banking

evoML is a machine learning (ML) model generation and optimisation platform that can benefit the banking sector in several ways:

1. Faster and more resource-efficient model development: The conventional ML model-building process can be extremely resource intensive. This includes financial resources as well as computational and human resources. evoML’s automated machine learning capabilities can help banks to build accurate and reliable predictive models quickly and efficiently.

2. Model code acceleration for quicker decision-making: Processing large amounts of data quickly and accurately is critical for many financial applications. By improving the running speed and efficiency of the ML models, banks can process large amounts of data and reduce prediction time, enabling them to make decisions more quickly and helping them stay competitive in a rapidly changing market.

3. Model code ownership for customisability and flexibility: Owning model code enables banks to customise their models according to their specific needs and requirements. They can make changes to different components of the model to improve its accuracy and relevance to their business. Owning the model code also allows banks to continually improve their models. They can retrain the model as new data becomes available, update it with new features, and make other changes to ensure that it remains accurate and relevant over time.

4. Increased explainability for better regulatory compliance: evoML’s explainable AI features can help banks understand how AI models are making decisions, and ensure that models are free of algorithmic bias. This also enables banks to communicate the credibility of their algorithms to regulatory bodies.

5. Scaling AI cost-efficiently: Banks need to ensure that their AI solutions can scale according to changing business needs. evoML’s cloud-based architecture ensures scalability and cost-efficiency, enabling retail banks to implement AI-powered solutions quickly and efficiently.

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