Customer Churn Management in Retail Banking

Client acquisition costs form a substantial part of distribution expenses in retail banking, leading many banks to implement systematic client retention management processes. However, these retention interventions often occur at late stages, when clients are on the verge of exiting. The early detection of warning signs across the entire client population could help devise effective preventative measures to boost client retention.

By leveraging responsible AI, banks and insurances can conduct a systematic analysis of clients’ transaction datasets and behaviors to identify triggers and dynamically assess the right moments for active client relationship management to prevent churn. Moreover, calculating an estimate of client lifetime value could further enrich this analysis by providing solid data points on how much should be invested in client retention.

Responsible AI plays a key role here, ensuring that the data is handled ethically and that the models developed do not perpetuate bias or unfair practices. It promotes transparency, interpretability, and fairness, forming an essential part of any data-driven churn management strategy.

Modulos offers a Responsible AI platform, empowering client teams to effectively analyze client datasets and extract key triggers for effective churn prevention. We combine our platform with dedicated AI consulting and data science expertise to deliver a comprehensive, ethically guided solution.

Our platform seamlessly merges advanced analytics with ethical guidelines, driving an ethical AI transformation in churn management. With Modulos, retail banks can confidently navigate the path to improved client retention, knowing that their strategies are not only data-driven but also ethically sound.