Cloopen AI's "Large Model Financial Quality Inspection Solution" undergoes a major upgrade: understanding business, covering all scenarios, and achieving high accuracy.
In industries with strict regulations such as finance, quality inspection has always been the most crucial but also the most challenging part of business operations. The current quality inspection model has significant technical bottlenecks and business pain points:
- Compliance pressure and low sampling rate: The regulatory requirement is "full-process penetration supervision", but the manual sampling rate is less than 5%, and 95% of service records are in blind areas.
- "Three lows and one high" in manual quality inspection: Low efficiency (10,000 calls require 1 month), low consistency (judgment consistency rate < 70%), low coverage (unable to handle millions of calls), and high cost (annual manpower > 500,000).
- Shortcomings of traditional NLP: Keywords/rule engines are difficult to identify complex semantics, with a leakage rate as high as 50%; and lack interpretability, making it impossible to trace the basis for violations.
- Insufficient multi-modal adaptation: Unable to handle emerging business data such as voice, text, and video, with over 90% of conversations becoming "silent assets".
Facing the contradiction between the "penetration supervision" requirement and the inefficient quality inspection methods, as well as the technical bottlenecks of complex semantic understanding and multi-modal processing, the Cloopen AI large model financial quality inspection solution has been fully upgraded. It deeply integrates financial business logic and covers four core scenarios: conversations, contracts, marketing, and anti-fraud, shifting risks from "passive remediation" to "active prevention", and redefining the efficiency and accuracy ceiling of intelligent quality inspection.
Highlights of the solution: Accurate, professional, and high value for money
1. Large model semantic quality inspection, achieving full-scale and precise identification
The core pain point of financial quality inspection lies in "low coverage rate of manual inspection" and "high rate of missed detection of hidden violations", while multi-modal data such as voice is difficult to be effectively utilized, and massive data cannot be transformed into compliance insights.
The Cloopen AI large model quality inspection agent has achieved two breakthroughs. Firstly, it possesses deep semantic understanding and cross-context correlation, enabling it to accurately identify complex violation scenarios such as implicit commitments, concept confusion, and logical contradictions. This surpasses the limitations of traditional rule-based quality inspection. For instance, it can precisely distinguish between the process guidance in marketing scripts like "Step 1, Step 2" and the confusing concepts of false promotional terms like "China's First" and "Worldwide First".
Secondly, by combining the semantic processing of the large model with the rule engine of the small model, it can achieve 100% full-volume quality inspection for thousands of phone calls per day. The single call duration is reduced to 10 seconds, which not only meets the requirements of "full-process penetration supervision" but also solves the problems of low efficiency and insufficient coverage of manual quality inspection (less than 5%).
2. Deep Integration of Financial Business Logic
The quality inspection requirements for different scenarios in the financial industry vary significantly. For instance, in the customer service scenario, it is necessary to focus on monitoring emotional conflicts and service processes; in the marketing scenario, it is crucial to detect false promotions; and in the collection scenario, it is essential to prevent excessive pressure.
The Cloopen AI financial quality inspection solution is not a universal template. Its core competitiveness lies in "the accumulation and application of scenario-specific datasets". For each scenario, an exclusive dataset and inspection template are constructed:
- Marketing scenario (such as product recommendation): covers 12 types of violation phrases like "priority of risk disclosure", "exaggerated returns", and "excessive commitment", with 5,000+ samples accumulated, accurately identifying compliance risks.
- Follow-up scenario (such as risk follow-up for existing customers): focuses on "content completeness" and "effectiveness of customer responses", capable of detecting whether the customer responds positively, whether a second reminder is given, as well as "product net value fluctuations" and "market risk warnings" and other mandatory inspection points.
- Customer service scenario: includes core inspection items such as "accurate response to business inquiries" and "communication skills", ensuring service quality and customer experience.
- Complaint scenario: emphasizes "completeness of problem recording" and "effectiveness of emotional soothing", ensuring complete recording of complaint information and effective soothing of customer emotions.
3. Size Model Collaboration, Balancing Effect and Cost Performance
Although pure large models can achieve deep semantic understanding, they are costly in routine tasks; small models, on the other hand, are efficient and cost-effective in standardized tasks such as rule verification and clustering analysis but lack flexibility. Cloopen AI innovatively adopts a size model collaboration model, allowing the two to perform their respective duties and complement each other's strengths, achieving no loss in effect while keeping costs more controllable.
Under this collaborative model, the Cloopen AI large model quality inspection solution not only maintains a high accuracy rate of 96%, but also reduces the overall cost by 40%, truly achieving a "win-win situation of effectiveness and cost-effectiveness".
Full quality inspection scenario coverage in the financial industry
The Cloopen AI quality inspection all-scenario solution, based on the semantic understanding and multi-modal capabilities of the large model, covers the four high-frequency quality inspection scenarios in enterprise operations, helping enterprises truly achieve full, accurate, and traceable intelligent compliance.
1. Customer service conversation review:
Financial institutions generate thousands of customer service, marketing, and follow-up calls every day, which are high-risk areas for compliance.
The Cloopen AI large model quality inspection agent, through the combination of large model semantic understanding and small model rule engine, achieves 100% full-quality inspection and can accurately identify implicit risks such as contradictions, emotional conflicts, and false promotion.
Take a certain securities firm as an example. After introducing the large model quality inspection agent, all conversations are automatically identified, and potential risks such as "unclear customer responses" and "inappropriate agent guidance" are deeply explored. Currently, this scenario has achieved a 96% quality inspection accuracy rate, far exceeding the industry average level.
2. Contract/Document Compliance Review:
The compliance of legal documents such as contracts and agreements directly affects the legal risks of the enterprise. Cloopen AI offers powerful document quality inspection capabilities. Through OCR text extraction and large model semantic analysis, it enables automatic comparison of contract contents and verification of clause consistency.
Compared with manual review where "page-by-page verification is required and it is both time-consuming and labor-intensive", Cloopen AI document quality inspection can complete the review of a complex contract within minutes, with an accuracy rate of 91%, significantly reducing legal risks and labor costs.
3. Compliance review of marketing materials:
All marketing materials of financial institutions (such as H5 pages, WeChat public accounts, posters, advertising copy, etc.) must undergo manual review. The workload is huge and it is prone to missing risk points.
Cloopen AI is based on NLP + OCR image quality inspection capabilities, which can automatically identify prohibited words and extreme words, and verify whether essential information such as "risk warnings" is included. This capability can increase the review efficiency by several times and proactively issue warnings before customer complaints or penalties occur, effectively reducing potential risks.
4. Anti-fraud auxiliary review
In high-risk areas such as remote account opening, online lending, and securities account opening, identity fraud and fraudulent activities occur frequently.
The Cloopen AI large-scale model quality inspection solution builds the "personal quality inspection" capability. Through real-time voiceprint detection and gender/age auxiliary verification, it accurately identifies identity fraud. While achieving seamless verification, it effectively reduces fraud losses.
Conclusion: Cloopen AI Redefines the Value of Financial Quality Control
In the era of strict financial supervision, quality control is no longer a passive task of "meeting inspections", but a core capability for "improving operational efficiency and reducing risk costs". The upgrade of Cloopen AI's comprehensive quality control solution in this round, with the core of large model semantic capabilities, uses the characteristics of "understanding business, covering all scenarios, and high precision" to transform quality control from a "cost center" to a "value center".
Whether it is the quality control for securities account opening follow-up, bank customer service quality control, or insurance marketing quality control, Cloopen AI can provide ready-to-use and precisely adapted solutions.
In the future, with the continuous iteration of large model technology, Cloopen AI will further deepen its understanding of industry scenarios, build a more solid compliance defense line for financial institutions, and help enterprises achieve safe growth under the premise of compliance.