One of the region’s largest Non-Banking Financial Companies (NBFCs) manages a diverse loan portfolio across multiple retail lending segments. As the business expanded, the organisation faced growing challenges in monitoring collection performance across internal teams and outsourced field agents, while holding portfolio quality and operational efficiency. To address rising bounce rates, increasing Non-Performing Loans (NPLs), and escalating collection costs, the client partnered with AIQU to build an AI-powered debt collection platform that reshaped collection operations through automation and advanced analytics.
One of the region’s largest non-banking financial companies ran collections across multiple teams and channels, spanning a large borrower base. Leadership had limited visibility into how those teams performed day to day, which made it hard to spot risk early or improve recovery.
Key challenges included:
AIQU built a fully automated debt collection operating model on an AI and machine learning platform, bringing customer, payment, and economic data into one place so the client could run collections from a single, data-driven view. The platform drew on several data sources, including:
It automated routine workflows and used the data to prioritise accounts, flag risk patterns early, and recommend the next best action for each borrower.
The new operating model delivered measurable improvements within the first six months.
