A specialized credit card issuer was burdened with a legacy, multi‐step process that involved handling invoices from over 10,000 vendors. Each vendor’s invoice came in a unique format and was processed using old, manually driven applications. This resulted in disparate data storage, human‐induced discrepancies, slow processing times, and an inability to quickly adapt to ongoing enhancements required by the business. The existing system was not scalable, and manual interventions were causing significant delays and increased error rates.
Eclantiqx engineered a comprehensive transformation using Pega Infinity. The solution was designed to fully automate the accounts payable process by accepting new invoices from vendors, vetting each submission, verifying vendor details, and then subjecting the invoice to a systematic review. A key part of the solution involved calibrating the invoiced amount with any previously issued credit memos and then automatically routing the invoice for approval and disbursement. Integrated with the client’s CRM system and empowered by a robust business rules engine, the platform provided real-time dashboards and reporting, ensuring that any adjustments could be made on the fly without manual intervention.
The organization suffered from a poor employee experience due to outdated, legacy applications that required employees to navigate multiple systems manually. The existing system not only slowed down the selling process but also failed to offer features that were relevant to individual customer needs. Furthermore, there was no efficient mechanism in place to maintain the relationship between a single customer and the multiple accounts they held, leading to a high churn rate and missed opportunities to upsell or cross-sell products
Eclantiqx implemented a transformative solution that leveraged intelligent next-best–offer technology. In this approach, call center executives (CCEs) received automated suggestions generated from a sophisticated application that analyzed customer profiles, geographical data, and nature of expenses. The solution was built on Pega 8.6 and integrated with a Python-based data processing system and Tableau for business intelligence, enabling the backend marketing team and data scientists to continuously refine the offer engine. This multi-channel approach also supported call center, email, and web interactions so that every customer inquiry could be met with the most relevant and personalized offer.
