Post-Loan: The Era of AI Robots

Transformation Overview

Currently, consumer finance companies are using 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% of collections are intelligent

Statistics show that more than half of institutions consider intelligent collection to dominate the entire collection cycle, especially with AI-powered robots capable of independently handling thousands of collection calls.

◎ Diversification of intelligent collection methods

In terms of specific intelligent collection techniques, collection scoring cards, intelligent outbound calls, and robotic collections are the three most widely used tools by institutions.

◎ Clear advantages of intelligent post-loan management

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

◎ Three future development directions

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

Transformation Challenges

With further regulation of personal information usage, the repair of overdue customer data becomes more restricted, 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 of regulatory authorities, which have introduced multiple compliance requirements.

◎ Balancing collection costs and efficiency

Since consumer finance loans are typically small in amount, robotic collection can largely address efficiency issues. However, high standardization and significant upfront R&D costs for intelligent robots remain challenges.

◎ Weaknesses in human-machine interaction

While the application of intelligent collection robots has become more widespread, there are still weak links, such as gaps in strategy configuration compared to manual collection.

◎ Effective transfer of non-performing assets

Beyond collection and write-offs, consumer finance companies also 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 electronic data on the blockchain

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

◎ Continued 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

In addition to methods like robotic collection, consumer finance companies also employ manual collection, SMS and letter notices, outsourcing collection agencies, and legal actions such as court litigation and online arbitration; they also utilize notarization and multi-channel dispute resolution methods like pre-litigation court mediation, arbitration, and people’s mediation.