Post-Loan: The Era of AI Robots

Transformation Overview

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

◎ Over 80% or even 90% of collections are intelligent

Statistics show that more than half of institutions consider intelligent collection to dominate the entire collection cycle, especially as AI robots can independently handle thousands of collection calls.

◎ Diversification of intelligent collection methods

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

◎ Clear advantages in intelligent post-loan management

AI robots can be programmed 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 in scenario development, customer service, and business processes.

Transformation Challenges

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

◎ Maintaining compliance boundaries in post-loan collection

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

◎ Balancing collection costs and efficiency

Consumer finance loans are typically small, and while intelligent robots can largely address collection issues, their high standardization and initial development costs remain challenges.

◎ Weaknesses in human-machine interaction

Although the use of intelligent collection robots is more widespread, there are still gaps in areas like strategy configuration compared to manual collection.

◎ How to effectively transfer non-performing assets

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

Transformation Breakthroughs

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

◎ On-chain credit data for the entire process

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

◎ Increasing investment in technological 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 methods like AI robot collection, consumer finance companies also use manual collection, SMS and letter notices, outsourcing collection, as well as legal actions such as court litigation and online arbitration; they also employ notarization and multi-channel dispute resolution methods like pre-litigation court mediation, arbitration, and people’s mediation.