AI Persuasive Reference for Independent Insurance Agents
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Solution Overview
Problem
The insurance industry faces a high turnover rate and shortage of skilled insurance sales agents due to low income levels for entry-level positions, and existing software applications are not effectively tailored to support independent agents in their sales processes.
Innovation Solution
An AI-based robotic process automation system, known as IISA Bot, uses a deep neural network and decision tree classifiers to generate personalized persuasive references for insurance products and services, dynamically updated based on customer feedback and behavior analysis, to assist independent insurance sales agents in maximizing their revenues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If insurance software applications are developed mainly with insurance companies in mind, then the software meets company needs, but independent agents lack effective support tools
Solution Approach 1:
The patent introduces an intermediary layer between insurance company systems and independent agents. This intermediary software acts as a mediator that translates company data into agent-friendly formats, providing personalized support tools without requiring changes to core company systems. The intermediary processes and presents information in ways that specifically benefit independent agents' workflows.
Solution Approach 2:
The software provides localized, agent-specific functionality rather than a one-size-fits-all approach. It tailors the user interface, data presentation, and analytical tools to match the specific needs and workflows of independent agents, allowing different agents to access customized views and capabilities based on their individual requirements.
2Productivity
If entry-level sales agents receive low income, then company costs are controlled, but agent turnover rate increases
Solution Approach 1:
The software provides preliminary, automated preparation of sales materials, prospect analysis, and campaign strategies before agents engage with customers. This preliminary action enables even entry-level agents to access sophisticated sales support without requiring extensive experience, thereby improving productivity from day one and making the low-income position more sustainable.
Solution Approach 2:
The system implements continuous feedback loops that track agent performance, provide real-time guidance, and adjust strategies based on results. This feedback mechanism helps entry-level agents improve their skills and productivity over time, making the position more rewarding and reducing turnover despite lower compensation.
3Ease of operation
If manual sales processes are used by independent agents, then flexibility is maintained, but time consumption and inefficiency increase
Solution Approach 1:
The software enables agents to serve themselves with automated generation of sales materials, automatic prospect follow-up scheduling, and self-updating of customer databases. This self-service capability reduces the time agents spend on administrative tasks while maintaining the flexibility of manual processes, as agents retain control over their workflows without manual bottlenecks.
Solution Approach 2:
The patent replaces manual, mechanical sales processes with automated digital systems. Instead of manually creating proposals, tracking prospects, and analyzing performance, the system uses automated algorithms and digital tools to perform these functions, dramatically reducing time consumption while preserving agent flexibility and control.
Data Source
AI summary
Methods and systems are provided for AI-based robotic automation for persuasive references. In one novel aspect, a robotic persuasive reference is generated based on a prospect product-service (P_PS) matrix, which is generated based on predictive analysis using DNN model and dynamically obtained feedbacks. In one embodiment, the DNN model is trained with customer personal profiles against associated PS revenues for each customer data set. In one embodiment, the predictive analysis uses a decision tree classifier. In one embodiment, the computer system detects one or more predefined triggering events comprising feedback information for the robotic persuasive reference and one or more predefined lifetime events, updates the P_PS matrix based and the robotic persuasive reference accordingly. In one embodiment, the feedback information is a sentiment analysis on responses from the prospect. In another embodiment, a recency, frequency, and page browsing analysis is performed based on the one or more detected lifetime events.