Deepening Customer Relationships and Growing Customer Lifetime Value with AI/ML in Retail Banking

Retail banks today face the challenge of enhancing customer relationships while growing customer lifetime value. Traditional methods fall short in the age of digital transformation. However, intelligent AI/ML-enabled platforms offer a revolutionary approach by leveraging data-driven insights in real-time to provide hyper-personalized customer experiences. Here’s how banks can harness these technologies to achieve their goals.??

The Need for Advanced Analytics in Banking?

Many retail banks struggle with extracting actionable insights from core banking data. This data, although rich, often remains underutilized due to the lack of advanced analytics tools. Without these insights, banks miss opportunities to enhance customer experience, anticipate customer needs, and ultimately deepen relationships. In the retail banking sector, vast amounts of data are generated daily from various customer interactions, including transactions, account activities, loan applications, and customer service interactions. However, much of this data remains untapped due to the lack of advanced analytics tools. Here are a few examples illustrating the need for advanced analytics:??

• Transaction Patterns: Banks collect extensive transaction data but often fail to leverage it effectively. Advanced analytics can identify spending patterns, detect anomalies, and predict future behavior. For instance, if a customer regularly spends on travel and dining, the bank can offer targeted rewards or travel-related financial products, enhancing the customer experience.??

• Customer Segmentation: Traditional methods of customer segmentation are often too broad. Advanced analytics can create more granular segments based on detailed customer behavior and preferences. For example, instead of categorizing customers simply by income level, advanced analytics can segment them by spending habits, saving tendencies, and lifestyle choices, enabling more personalized marketing strategies.?

• Service Optimization: Customer service interactions generate valuable data. Advanced analytics can analyze this data to identify common issues and improve service quality. For instance, if analytics reveal that a significant number of customers are calling about online banking issues, the bank can enhance its digital platform and reduce call center volume, improving overall customer satisfaction.?

Reducing Customer Churn with Predictive Analytics?

One of the critical areas where AI/ML can make a substantial impact is in predicting and reducing customer churn. By analyzing patterns and behaviors in transaction data, AI models can identify customers at risk of leaving. These insights enable banks to proactively implement retention strategies, such as personalized offers or improved customer service interactions, thus reducing churn rates significantly. Customer churn is a significant concern for banks, as acquiring new customers is often more expensive than retaining existing ones. Predictive analytics can help banks identify at-risk customers and take proactive measures to retain them. Here are some examples:?

• Behavioral Changes: Predictive analytics can monitor changes in customer behavior that may indicate dissatisfaction, such as reduced transaction frequency or decreased account balances. For instance, if a previously active customer suddenly stops using their credit card, the bank can trigger an intervention, such as a personalized offer or a check-in call to address any potential issues.?

• Feedback Analysis: Analyzing customer feedback from surveys, social media, and customer service interactions can reveal underlying dissatisfaction. For example, if a customer frequently rates their banking experience poorly or leaves negative comments, predictive analytics can flag this customer for targeted retention efforts, such as special incentives or dedicated support.?

• Life Events: Predictive models can also factor in significant life events that may affect customer retention, such as moving to a new city, getting married, or retiring. For instance, a bank could offer a customer moving to a new city a special relocation package, including assistance with mortgage transfers and local branch information, to ensure continued loyalty.?

Enhancing Loan Management??

AI and ML capabilities also play a pivotal role in loan management. A customer data platform (CDP) with AI/ML integration can predict a customer's propensity to pay off a loan early or default. By analyzing various data points, such as spending habits, transaction history, and external economic factors, the platform can provide accurate risk assessments. This allows banks to tailor their loan offerings and management strategies to individual customers, reducing risk and improving repayment rates. Effective loan management is crucial for both customer satisfaction and the bank's financial health. AI and ML capabilities in customer data platforms can significantly enhance loan management by providing better risk assessments and personalized loan offers. Here are some examples:?

• Early Payoff Propensity: By analyzing customer financial behavior and external data, AI models can predict which customers are likely to pay off their loans early. For instance, a customer with a consistent history of making extra payments on their mortgage may be flagged by the model. The bank can then offer this customer new loan products or investment opportunities to keep them engaged.??

• Default Risk Prediction: AI and ML can assess a wide range of factors to predict default risk more accurately than traditional credit scoring methods. For example, a customer with erratic spending patterns, frequent overdrafts, and recent job loss might be identified as high risk. The bank can then adjust its loan terms or offer financial counseling to mitigate the risk of default.??

• Personalized Loan Offers: By understanding individual customer needs and financial behaviors, banks can create personalized loan offers that are more likely to be accepted. For instance, a customer with a solid income but high student loan debt might benefit from a debt consolidation loan with lower interest rates. By providing tailored solutions, banks can increase loan uptake and customer satisfaction.?

?The Role of TCS Customer Intelligence & Insights??

TCS Customer Intelligence & Insights? for banking exemplifies how an AI/ML-enabled platform can transform customer relationships. Here’s how it helps:?

• Real-Time Insights: TCS Customer Intelligence & Insights? leverages real-time data to provide banks with up-to-date insights into customer behaviors and preferences. This allows banks to act swiftly and appropriately to meet customer needs.?

• Hyper-Personalization: The platform uses AI to segment customers and deliver personalized experiences. From targeted marketing campaigns to customized financial products, hyper-personalization fosters stronger customer loyalty.?

• Churn Prediction and Prevention: By employing sophisticated ML algorithms, TCS Customer Intelligence & Insights? can predict which customers are likely to churn. Banks can then deploy targeted retention strategies to keep these customers engaged.?

• Risk Assessment: The AI capabilities within TCS Customer Intelligence & Insights? can assess a customer's credit risk more accurately than traditional methods. This enables banks to manage their loan portfolios more effectively, minimizing defaults and optimizing profits.?

• Enhanced Customer Engagement: With deeper insights into customer preferences and behaviors, banks can engage customers more meaningfully. Personalized communications, timely offers, and responsive customer service all contribute to a more satisfying banking experience.?

?In Conclusion?

In a competitive financial landscape, retail banks must leverage AI and ML to transform data into actionable insights. This transformation facilitates a deeper understanding of customer needs and behaviors, allowing banks to enhance customer experiences, reduce churn, and improve loan management. Platforms like TCS Customer Intelligence & Insights? for banking enable banks to provide hyper-personalized experiences, reduce churn, and manage risk more effectively. By doing so, banks can deepen customer relationships, enhance customer satisfaction, and ultimately grow customer lifetime value. Embracing these technologies is not just a strategic advantage but a necessity for futureproofing in the digital age.?

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