Let’s acknowledge a crucial aspect from the outset – managing the Customer Lifecycle Management (CLM) journey has become an increasingly complex, costly, and challenging endeavor. This is a shared challenge across the financial sector, exacerbated by an era marked by continuous change.
According to FullCircl, financial institutions face a plethora of evolving challenges: shifting economic and geopolitical landscapes, mounting regulatory and operational complexities, intensifying competition, rapid technological advancements, escalating financial crime and fraud risks, and a fluctuating talent market. Notably, there has been a significant shift in customer expectations, necessitating a transformative approach in how FIs interact with their clientele throughout the lifecycle.
The existing operational models are proving unsustainable, evidenced by the hefty costs associated with onboarding new commercial customers—ranging from $20,000 to $30,000, with an additional potential loss of $25,000 due to onboarding delays. Moreover, FIs are grappling with a global financial crime compliance cost of $206.1bn and a regulatory compliance expenditure of £38.4bn.
The imperative is clear: FIs need to integrate intelligence comprehensively to herald a new era in Client Lifecycle Management. What exactly does CLM entail, and why is it critical for financial institutions? CLM involves managing the entire client relationship spectrum—from acquisition and onboarding through continuous interactions and due diligence. Effective CLM not only facilitates compliance but also enhances client retention, trust, loyalty, and profitability.
Given the multifaceted challenges today, a revamped approach to CLM is necessary. Many FIs struggle with outdated technologies, fragmented processes, human errors, siloed data, and lack of unified client views, all of which complicate data valorization and operational efficiency.
The future beckons a reimagined CLM strategy pivoting on automation, AI-driven analytics, and data integration, enabling FIs to meet their core challenges, unlock substantial value, and enhance operational effectiveness while freeing up human resources for strategic roles.
Data, often termed as the new gold, plays a pivotal role in transforming CLM. Utilizing deep data reserves and AI facilitates smarter, quicker decision-making processes, improves regulatory compliance, proactively manages risks, and delivers personalized client experiences—all while reducing costs and enhancing competitive agility.
The journey of CLM redefinition involves several stages, from client acquisition to retention. Initially, FIs can leverage market intelligence and automated insights for targeted engagement. The onboarding process can be streamlined through automated workflows and comprehensive KYC, KYB, and AML screenings, balancing superb customer experience with stringent compliance.
The subsequent phases involve automating application processes, enhancing credit decisioning, and utilizing real-time insights for risk management. Ultimately, the goal is to foster deeper client relationships through personalized, proactive engagement strategies.
Proof of the efficacy of a data-driven, automated approach is seen in the experiences of major banks. For instance, Santander and Metro Bank have significantly reduced their onboarding times and efforts through digitalization, demonstrating improvements in efficiency and customer satisfaction. Similarly, Tide and ThinCats have leveraged automation to boost their processing capabilities and operational efficiency, leading to increased revenue and customer engagement.
In conclusion, 2025 stands as a landmark year where FIs must embrace intelligence-driven CLM to navigate the myriad challenges effectively. As highlighted in a recent McKinsey report, strategic investments in technology are crucial for maximizing returns, particularly in enhancing tech capabilities.
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