Real-Time Quote Generation Platform Using Multi-Source Data Normalization
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Solution Overview
Problem
The process of obtaining insurance or loan quotes is inefficient due to the need for extensive customer and property information collection, validation, and integration of data from various sources, which often results in inconsistencies and delays.
Innovation Solution
An electronic communications platform that pre-fills property and personal information into insurance and loan applications using data from multiple sources, normalizes the information, and computes quotes in real-time, reducing human error and processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual information collection and validation is performed by loan agents, then data accuracy can be ensured through human review, but the processing time increases to several days or weeks
Solution Approach 1:
The system performs preliminary actions by automatically collecting, validating, and normalizing information from multiple sources before the quote generation process begins. Data is pre-fetched from public and private sources, and validation rules are applied in advance to ensure accuracy without requiring manual review during the actual quoting process.
Solution Approach 2:
The patent replaces the mechanical system of manual information collection and validation by loan agents with an automated computer-based system. The system uses algorithms to fetch data from multiple sources, apply validation rules, resolve conflicts, and generate quotes automatically, eliminating the need for human agents to manually collect and verify information.
2Loss of information
If information from multiple sources is collected to ensure comprehensive data, then data completeness improves, but information inconsistencies and conflicts increase
Solution Approach 1:
The system introduces an intermediary layer consisting of normalization rules and conflict resolution algorithms that mediate between multiple information sources. These intermediaries standardize data formats, reconcile conflicting information using predefined business rules, and prioritize sources based on reliability metrics, allowing comprehensive data collection without compromising consistency.
Solution Approach 2:
The patent changes parameters by transforming data from multiple sources into a standardized format using normalization rules. The system adjusts data parameters to match expected schemas, converts different date formats, currency formats, and data structures into unified representations, and applies transformation rules to reconcile conflicting values from different sources.
3Reliability
If extensive property information (20-30 items) is required for accurate quotes, then quote reliability improves, but the complexity of the application process increases
Solution Approach 1:
The system enables self-service by automatically collecting the required 20-30 property information items without requiring the customer to manually provide each piece of data. The system autonomously fetches information from public records, private data sources, and third-party providers, and performs validation and conflict resolution automatically, reducing the application process to simple customer input while maintaining comprehensive data collection.
4Measurement precision
If follow-up conversations are conducted to collect missing information and correct errors, then data accuracy improves, but the loss of time increases
Solution Approach 1:
The system implements feedback mechanisms that automatically detect missing or erroneous information and trigger targeted data collection actions. Validation rules identify gaps in the data, and the system automatically queries additional sources or requests specific information from the customer only when needed, rather than conducting comprehensive follow-up conversations for all applications.
Data Source
AI summary
A technique for accessing multiple rate data from several sources and applying the rate data to pre-populated data of a survey is described. The techniques include backend processes and architectures that allow for the retrieval, modeling, and population of certain data fields during the customer evaluation process as during the process of requesting a quote for products or services. The process includes accessing static property information of a property from a first plurality of sources, computing a rate for the property based on the static property information, accessing dynamic property information of the property from a second plurality of sources, adjusting the rate for the property based on the dynamic property information, and generating a rate graphical user interface that indicates the adjusted rate for the property.