[Series·Part 4] AI Agent-Hosted RTA: The Era of Autonomous Customer Acquisition in Credit Lending

[baoshi.rao]

From strategy generation to execution loops, from data orchestration to feedback optimization, Agents are reshaping the operational logic of financial marketing. As the fourth installment in this series, this article focuses on the implementation path of 'AI Agent-Hosted RTA,' aiming to provide valuable insights.

This is the fourth article in our series The New Era of Credit Marketing: Full-Chain Practices of RTA and AI.

1. Introduction: RTA in the AI Era—No Longer 'Manual Parameter Tuning'

In the credit industry, RTA has become the central system for customer acquisition.

• Phase 1: It addressed the disconnect between risk control and marketing.
• Phase 2: It integrated profit logic into marketing.
• Phase 3: It evolved into an operating system for experiments, budget allocation, and customer operations.

However, this system still faces a bottleneck: heavy reliance on manual intervention.

For example:

• Risk model thresholds require manual setting.
• Bidding coefficient ranges need manual adjustments.
• Cross-channel budget migration rules demand human decisions.

The problem is, human response speeds simply cannot keep up with millisecond-level ad bidding and daily profit fluctuations.

Thus, an inevitable trend emerges: AI Agent-Hosted RTA.

2. The Value of AI Agent Intervention

The core capabilities of AI Agents are global perception, autonomous decision-making, and dynamic execution.

In the RTA context, their value manifests in three key areas:

1) From Risk Control Hosting to Marketing Hosting

• Past: Risk control only blocked users; marketing only set bids.
• Future: AI Agents automatically decide whether to bid, how much to bid, and at what price based on risk scores.

2) From Profit Prediction to Profit Guarantee

• Past: Profit models were analytical tools.
• Future: AI Agents convert profit predictions directly into bidding instructions, ensuring positive per-customer profits.

3) From Multi-Channel Operations to Unified Strategy

• Past: Different channel teams operated independently.
• Future: AI Agents unify budgets and audience strategies across all channels, dynamically reallocating resources.

3. The Ideal Form of Full-Process Hosting

The future AI Agent-Hosted RTA can be envisioned as an automated pipeline:

1. Data Layer: User behavior data, credit data, device data, and ad feedback data.
2. Model Layer: Scorecards (A/B/C models), anti-fraud models, profit prediction models, and Vintage models.
3. Strategy Generation Layer: AI Agents translate model outputs into audience tags, bidding strategies, and budget allocations.
4. Execution Layer: RTA APIs deliver strategies in real time to ad platforms for millisecond-level bidding.
5. Feedback Optimization Layer: Real-time monitoring of ROI, bad debt rates, and profit margins, with automatic strategy adjustments.

In this loop, humans transition from operators to supervisors, primarily setting business goals rather than tweaking parameters.

4. Key Technologies: How AI Agents Achieve 'Hosting'

1) Multi-Objective Optimization

AI Agents simultaneously optimize scale, risk, and profit.

Example: Maximize scale while ensuring profitability.

2) Reinforcement Learning

AI Agents trial, receive feedback, and iterate during marketing, gradually converging on optimal strategies.

3) Causal Inference

Avoid relying solely on correlations to determine whether a strategy (e.g., +5% bidding) truly boosts profits.

4) Automated Monitoring & Risk Alerts

If ROI drops sharply, AI Agents can pause budgets within minutes to prevent losses.

5. Irreplaceable Human Roles

Despite AI Agents' power, humans are still needed for:

1. Setting Business Goals: Deciding whether to prioritize scale or profit.
2. Compliance Boundaries: AI cannot fully interpret regulatory requirements.
3. Expanding Data Sources: Integrating new data (e.g., central bank credit reports, partner data) requires human coordination.
4. Gray-Box Strategy Validation: Major strategy changes still need human oversight and approval.

In short, AI Agent hosting follows the principle: Humans set strategy; machines execute tactics.

6. Case Preview: The Future 'Autonomous Marketing Room'

Imagine a financial marketing scenario in 3 years:

• A mid-sized lending platform operates with a 2–3-person marketing team.
• AI Agents handle all budget allocation, channel selection, audience targeting, and bidding strategies.
• Humans only need to set quarterly goals (e.g., profit margin > 8%).
• Results: Steady growth in customer acquisition, stable profitability, and minimal manual effort.

This is the autonomous era of credit lending—a true fully automated, profit-guaranteed customer acquisition system.

7. Conclusion

Recapping the series:

1. RTA-Risk Control Synergy: Preventing budget waste.
2. RTA-Profit Synergy: Making ad campaigns profitable.
3. RTA Expansion: From tool to operating system.
4. AI Agent Hosting: The ultimate form of full-process automation.

From avoiding losses to generating profits and finally autonomous marketing, this is the evolution of credit RTA.

In the future, those who first implement AI Agent-Hosted RTA will gain a 5-year head start in the credit acquisition game.