Client onboarding account opening post client review and signature

Industry : BPO and Business Services, Cross-Industry

Function : Operations

Company Profile

This Canadian national financial services bank focuses on physicians and medical professional needs in Canada. The company offers comprehensive retirement estate and planning, wealth management, financial planning, investments, insurance, and banking services, with more than 50 regional offices throughout Canada.

Scope Highlights

  • Account opening upon receiving client signature
  • Microsoft Dynamics 365
  • DocuSign
  • OpenAdvantage account management system
  • Data cleansing rules
  • Robotic Process Automation (RPA)

Challenge

Our client, a large financial and wealth management organization, onboards about 500 customers per week in an account opening process that involves multiple eSignature requests and KYC steps. Because the process varied region to region and involved handoffs to different participants it was prone to data quality issues and delays. The portfolio services team sought to streamline the process with an RPA solution that could speed up processing times, reduce or eliminate data inaccuracies and improve the overall customer experience.

Solution

The automation needed to standardize the complex business rules of client onboarding and allow for a human team member to verify documents at key steps. The solution involved building a human-in-the-loop processing framework that reads and validates the filled data from DocuSign, retrieves uploaded documents and flags for any missing requirements and transforms and standardizes the data according to externalized business rules. The solution then updates the account management system’s client- and account-related data. The solution also generates exception reports for the portfolio services team to allow the team to track which accounts were successfully created and which need manual review.

Results

10,000+ hours saved annually

Faster account opening

Eliminates data errors, improves data quality