Cloopen Ai Large Model Quality Inspection Practical Application: Enabling Value Growth for City Commercial Banks in Four Business Scenarios

The Cloopen Ai Large Model Quality Inspection System is not merely a compliance tool; rather, it utilizes deep semantic understanding and data mining to transform "quality inspection capabilities" into "business growth impetus" in the four core business scenarios of telephone sales, customer service, follow-up visits, and marketing. This helps a city commercial bank achieve multiple improvements in regulation, efficiency, and customer value.

Scenario 1: Telephone Sales - Precise Diagnosis of Issues, Enhancing Conversion Efficiency
Traditional quality inspection relies on sampling and cannot identify the core reasons for low agent conversion rates from the full set of calls, resulting in ambiguous optimization directions. The Cloopen Ai system uses full-call semantic analysis to deeply explore the key factors affecting conversion (such as speech adaptation, demand matching, response to customer concerns, etc.), providing precise basis for optimization.

Typical case
In a certain call marketing team of this line, one of the agents consistently had a conversion rate lower than the average of the team. Manual inspections did not reveal any violations in the language usage or loopholes in the process. After a full-scale analysis of their call records by the system, it was found that this agent always rigidly followed the SOP language, regardless of whether the customer mentioned "funds liquidity needs" (such as "This money may be needed at any time") or "low risk preference" (such as "Don't want to bear losses"), they mechanically introduced the product features without responding specifically to the customer's needs.
The operation team then conducted special coaching (such as teaching them to adjust the focus of the language based on the customer's needs), and within one month, the conversion rate of this agent increased by 19.5%, verifying the practical value of the system in sales optimization.

Scenario 2: Customer Service - Quantify "Service Temperature" to Improve Customer Satisfaction
Traditional quality inspection can only detect violations of key words (such as insulting the customer), but it cannot capture "cold tone, lack of empathy" and other hidden service issues, resulting in "service quality" remaining at the subjective perception level. The Cloopen Ai system combines "voice emotion analysis + semantic understanding" to convert hidden service issues into quantifiable indicators.
The system can determine "negative service" and mark low scores by analyzing the smoothness of the customer service tone (such as mechanical responses without fluctuations) and the degree of evasiveness of the semantics (such as "Just wait" or "I'm not sure"). At the same time, it conducts standardized detection of "inappropriate opening phrases, omission of reminder at the end, excessive emotional fluctuations" and forms a "service quality scoring system".

Typical case
When the customer service replied "Just wait", the traditional quality inspection did not mark any violation keywords, but the system, through tone and semantic analysis, judged that the reply was "negatively service-oriented" and deducted points. The bank's customer service department then screened out similar negative conversations and carried out a special rectification of "empathetic communication". After the rectification, customer satisfaction increased by 23%, and related complaints decreased by 18%, truly implementing "service warmth".

Scene 3: Customer Follow-up - Capturing "Edge Cases" Risks, Strengthening the Compliance Defense Line
In the follow-up process of wealth management products and loan businesses, there are often "inconsistent responses by the customer before and after, and logical contradictions" (such as "first confirm knowing the rules, then deny understanding") in terms of implicit compliance issues (e.g., "first confirm knowing the rules, then deny understanding"). Traditional quality inspection relies on keywords, which is prone to missing such risks and leaving a compliance.
Cloopen Ai large model， through context semantic logical analysis， accurately captures the risk points in the full-scale follow-up： The system can compare the customer's multiple responses to the same question (such as "whether you know that the account cannot be involved in money laundering" first answering "yes" and then answering "no")， automatically mark "replies contradicting each other" and trigger manual re-inspection to ensure the follow-up is compliant without loopholes.

Typical case
The agent asked the customer during the compliance follow-up, "Do you know that your account and transactions cannot involve any illegal activities such as money laundering?" The customer initially replied "Yes", but a few seconds later changed their answer to "No". Such contradictory responses are easily overlooked in traditional random checks, but the system accurately identifies this risk through context correlation and marking it as a high-risk level. This prompts manual review and confirmation, effectively preventing subsequent compliance disputes caused by insufficient follow-up.

1. 1Precise Customer Profiles: The system extracts customer characteristics from the calls (such as age, gender, region), and key demands (such as loan inquiry customers focusing on "credit limit, interest rate, and loan processing speed"), forming multi-dimensional customer profiles. For example, after analyzing thousands of loan inquiry conversations, it was found that male customers aged 30-40 account for over 60% of the inquiry customers, and their top three concerns are "credit limit ceiling", "annualized interest rate", and "loan processing time", providing a basis for banks to precisely target the target customer group and design product selling points;
2.  Gold Scripting Library: The system automatically compares the response strategies of high-conversion agents with those of ordinary agents, summarizes efficient communication logic (such as "respond to the customer's concerns first, then introduce the product advantages"), and forms a "Gold Scripting Library". New employees can directly learn these communication methods that have been verified in the market, significantly shortening the growth cycle and rapidly improving marketing capabilities.