In the competitive, high-stakes asset management sector, customer experience is not just a differentiator for asset management firms; it’s a determiner of success or failure. With so many players in the market, customers evaluate and compare, looking for the best expertise, investment track record, and service experience. One key to a superior experience is the customer onboarding journey. Customers want to be onboarded easily, quickly, and securely—without any errors, delays, or risks.
As finance organizations look for new ways to drive strategic value from artificial intelligence (AI), a global asset management firm turned to CGI to raise its customer onboarding to a new level. CGI delivered an AI-driven anomaly detection solution that improved the quality and efficiency of the firm’s customer onboarding journey, saving it millions of dollars per year, reducing customer churn, and more.
The challenges of rules-based anomaly detection
The firm’s former customer onboarding process was completely rules-based. Pre-defined rules determined which types of anomalies could be detected and the point at which quality control kicked in (e.g., when customer accounts valued at $50,000 or more are opened). Anomalies beyond the perimeters of the rules were missed. Further, anomalies that typically have a very low failure rate would still trigger quality control, costing time and money.
In addition, even as human behaviors, forms, processes, etc. in onboarding changed, the rules stayed the same. As a result, new anomalies resulting from any changes went undetected.
Undetected errors (e.g., wrong type of account opened) resulting from this rules-based approach led to customer calls, delays, manual rework, and customer churn. Further, it posed financial risks. If an error, for example, resulted in the wrong stock being purchased for a customer, the asset management firm would be liable for any financial repercussions.
Shifting to AI-driven anomaly detection
CGI implemented a machine-learning solution for the firm that gathers and analyzes metadata across the entire onboarding process; for example, historical and recent data related to how, when, and where new accounts are opened. Through this data and the use of multiple AI analysis models at different points along the onboarding life cycle, the solution can predict potential account opening failures, despite the tremendous volume of data and the fact that most account openings are error free.
Because it’s AI-driven and not limited by rules, the solution also can detect any type of error in onboarding—existing or new. It can even detect errors that have never been contemplated. For example, in its analysis of onboarding metadata, the solution uncovered the fact that more onboarding errors are made by personnel near the time of lunch or at the end of the workday—a human behavior issue the firm could now address.
Saving millions and satisfying customers through improved onboarding
Through AI-driven anomaly detection, the firm now detects more onboarding errors and better manages quality control triggers, resulting in a smoother, faster customer onboarding journey. The outcome of this transformation is saving millions of dollars per year and ensuring fewer customer calls and churn. Another benefit is improved data quality and reliability using AI.
In terms of delivery, the CGI team delivered on time and on budget. Further, we provided AI consulting and supported the firm in moving AI anomaly detection to the cloud.
Through our work, the firm is delivering an exceptional customer onboarding experience that’s driving customer attraction and satisfaction while significantly improving the bottom line and increasing competitive advantage.
