Top 5 AI Use Cases for Retail Banking

The utilisation of artificial intelligence (AI) has been progressively gaining importance in various businesses, including retail banking. AI offers a multitude of benefits in this industry, such as enhancing customer service and detecting fraudulent activities. Given the intensifying competition in the field, it is crucial for executives to recognise the potential of AI and incorporate it into their business strategies to maintain their competitive edge. There are five primary AI use cases for retail banking that could play a crucial role going forward.

Discover Hidden Small and Medium Enterprises

With the help of AI-based systems, banks can analyse vast amounts of structured and unstructured data, interpret patterns, and pinpoint potential business prospects. This enables banks to identify promising small and medium-sized enterprises (SMEs) that would have gone unnoticed in the past. By monitoring multiple data sources, including social media, online transactions, and web traffic, AI can forecast the demand and growth potential of these businesses. As a result, banks can offer tailored loan products, risk assessments, and financial services, ultimately leading to increased revenue.

Moreover, incorporating AI technology simplifies payment processing, strengthens fraud detection, and boosts operational efficiency. This enables banks to concentrate on developing robust relationships with their SME clients, leading to heightened revenues and customer loyalty.

Credit Scoring and Underwriting

Assessing creditworthiness and making informed lending decisions are pivotal aspects of retail banking, specifically credit scoring and underwriting. The accuracy of credit scores is directly proportional to the amount of data taken into account during the process. By analysing extensive data and recognising patterns that indicate creditworthiness, AI can aid banks in enhancing credit scoring and underwriting. For instance, AI can scrutinise information like credit history and spending patterns, as well as consider alternate sources such as social media activity and geolocation data for a comprehensive evaluation.

Aside from ensuring accurate lending decisions, AI-powered techniques provide a more equitable approach to lending by mitigating the influence of biases. By swiftly processing large amounts of data, AI enables banks to make more informed lending decisions, while simultaneously establishing the credibility of the decision-making process.

Optimise Card-On-File Experience

AI facilitates improved fraud detection and prevention measures by examining user behaviour and spending habits in real-time to detect anomalies and potential security risks, safeguarding cardholder information. Additionally, AI-powered systems enable banks to provide personalised services and tailored offers based on customers' buying history and preferences. This approach results in greater customer engagement and satisfaction, ultimately leading to increased transactions and higher customer retention rates.

Moreover, AI can boost the precision and effectiveness of billing procedures by implementing automated data validation and management. These systems can also enhance risk evaluation by forecasting the possibility of declined transactions or delayed payments, and identifying risky clients or merchants, which ultimately reduces losses.

Personalised Customer Services

A significant advantage of AI in the banking industry is its ability to provide personalised customer service. AI-driven chatbots can effectively handle a diverse range of customer inquiries, providing prompt and accurate responses. This efficiency enables banks to direct their valuable human resources to more pressing tasks while simultaneously delivering exceptional customer service.

In addition, machine learning algorithms can analyse customer behaviour, transaction history, and preferences, enabling banks to gain a profound understanding of individual needs. This understanding empowers banks to offer personalised product recommendations, such as tailored credit, savings, or investment options, ultimately elevating customer satisfaction and promoting long-term loyalty.

Fraud Detection 

Retail banks are frequently targeted by fraudsters attempting to exploit vulnerabilities in financial systems. AI can assist in detecting fraudulent activities by scrutinising large quantities of data to identify patterns and irregularities that may suggest questionable behaviour. Additionally, AI can monitor banking transactions in real-time, enabling it to rapidly detect any suspicious activities and alert bank personnel for further investigation.

Moreover, AI-enabled fraud detection systems can learn and adjust to novel fraudulent activities, assisting banks in remaining ahead of progressively sophisticated fraud schemes. By leveraging AI in this manner, banks can enhance the safeguarding of their customers from financial losses caused by fraud, while reducing the danger of reputational harm to their brands.

The bottom line

As digital banking services continue to emerge, and the desire to enhance customer experiences grows, the adoption of AI in the banking sector is becoming progressively crucial. The industry is confronting a novel phase of customer expectations that call for personalised, efficient, and secure banking services. Banks are grappling to deliver authentic omnichannel experiences, leading customers to select competitors with more personalised options, as emphasised in the World Retail Banking Report 2022 by Capgemini and Efma.

To meet the expectations of modern customers, banks must incorporate innovative technologies that can enhance customer experience and streamline operations. Artificial intelligence is a vital tool in achieving these objectives. By utilising advanced algorithms and machine learning techniques, AI can analyse large amounts of data in real-time, detect patterns and trends, and make accurate predictions about customer behaviour.