Post-Loan: The Era of AI Robots

Transformation Overview

Currently, consumer finance companies use methods such as collection scoring cards, intelligent outbound calls, and robotic collections in post-loan recovery, gradually shifting from passive responses to proactive services.

◎ Over 80% or even 90% Use Intelligent Collections

Statistics show that more than half of institutions consider intelligent collections to be the dominant approach throughout the entire collection cycle, especially as AI-powered robots can independently handle thousands of collection calls.

◎ Diversification of Intelligent Collection Methods

The most commonly used tools for intelligent collections include collection scoring cards, intelligent outbound calls, and robotic collections.

◎ Clear Advantages in Post-Loan Management with AI

AI robots can be configured with different personas and voices, enabling quick responses tailored to various user communication needs and scenarios.

◎ Three Future Development Directions

Technological advancements promote deep integration and adaptation across scenario development, customer service, and business processes.

Transformation Challenges

With stricter regulations on personal data use, the repair of overdue customer information becomes more limited, leading to increased customer contact loss; additionally, there are emerging issues related to alleged “agency rights protection.”

◎ Maintaining Compliance in Post-Loan Collections

Violent collection practices that infringe on consumer rights have worsened in recent years, becoming a key focus for regulators, resulting in multiple compliance requirements being introduced.

◎ Balancing Cost and Efficiency in Collections

Consumer finance loans are typically small in amount per borrower. While intelligent robots can largely address these issues, their high standardization and significant initial R&D costs pose challenges.

◎ Weaknesses in Human-Machine Interaction

Although the use of intelligent collection robots is becoming more widespread, there are still gaps in human-machine interaction, such as strategy configuration differences compared to manual collections.

◎ Effective Transfer of Non-Performing Assets

Beyond collection and write-offs, consumer finance companies face the challenge of how to effectively transfer non-performing assets, which are characterized by low average amounts and lack of collateral.

Transformation Breakthroughs

Emerging technologies like IoT, cloud computing, big data, artificial intelligence, and blockchain are key to digital transformation in finance, enhancing the role of expert human collection teams.

◎ Chain All Credit Data Throughout the Entire Process

Some institutions are experimenting with blockchain and cloud computing. In overdue loan litigation, companies use blockchain evidence storage to record the entire credit process, turning electronic data into admissible evidence, and establishing a comprehensive risk prevention and dispute resolution mechanism that includes information retention, evidence fixation, and verification.

◎ Continued Investment in Technology Resources

With the widespread adoption of digital financial products and services, many consumer finance companies plan to increase investment in technology, providing high-quality financial services through intelligent capabilities externally, and leveraging big data, AI, and cloud computing internally.

◎ Traditional Post-Loan Management Cannot Be Abandoned

Besides using intelligent robots for collection, consumer finance companies also employ manual collection, SMS and letter reminders, outsourcing collection agencies, and legal actions such as court litigation and online arbitration; they also utilize notarization and multiple dispute resolution channels like pre-litigation mediation, arbitration, and people’s mediation.