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EntityIdentification: Real-Time Matching & Fraud Prevention - MCP Implementation

EntityIdentification: Real-Time Matching & Fraud Prevention

EntityIdentification: MCP Server delivering industry-leading accuracy in real-time entity matching, empowering seamless data verification and fraud prevention across enterprise systems.

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About EntityIdentification

What is EntityIdentification: Real-Time Matching & Fraud Prevention?

EntityIdentification is a Model Context Protocol (MCP) powered solution designed to determine if two datasets originate from the same entity. By combining text normalization, semantic analysis, and AI-driven evaluation, this tool ensures accurate real-time comparisons while mitigating fraud risks in data validation workflows.

How to Use EntityIdentification: Real-Time Matching & Fraud Prevention?

Implementing the tool involves three core steps: normalize text inputs, perform structured JSON comparisons using semantic evaluation, and leverage generative AI models to finalize entity matching decisions. The process is accelerated through pre-built functions like compare_json(), which automates key-value analysis across nested data structures.

EntityIdentification Features

Key Features of EntityIdentification: Real-Time Matching & Fraud Prevention

  • Context-Aware Normalization: Auto-corrects text case, punctuation, and whitespace inconsistencies
  • Order-Insensitive Comparisons: Handles unordered list comparisons for flexible semantic evaluation
  • Deep JSON Analysis: Recursively evaluates all nested keys using standardized comparison logic
  • Cognitive Validation: Integrates Gemini-2.0 models to assess contextual similarity beyond literal matches

Use Cases of EntityIdentification: Real-Time Matching & Fraud Prevention

Common applications include:

  • Financial institutions verifying customer account consistency
  • Logistics systems matching shipment records across databases
  • E-commerce platforms detecting duplicate seller accounts
  • Government agencies cross-referencing identity documents

EntityIdentification FAQ

FAQ from EntityIdentification: Real-Time Matching & Fraud Prevention

Q: How does semantic comparison differ from exact matches?

A: While exact matches require identical strings, semantic analysis identifies meaning equivalence (e.g., "Main St" vs "Main Street") using contextual understanding.

Q: Can this handle non-English data?

A: Yes, the normalization engine supports UTF-8 encoding and multilingual processing pipelines.

Q: What's the deployment process?

A: Install via package manager, initialize with require('entity-id'), then configure model parameters for your use case.

Content

EntityIdentification

Identify whether two sets of data are from the same entity. 识别两组数据是否来自同一主体

This is a MCP (Model Context Protocol) server. 这是一个支持MCP协议的服务器。

Data Comparison Tool

This tool provides a comprehensive way to compare two sets of data, evaluating both exact and semantic equality of their values. It leverages text normalization and a language model to determine if the data originates from the same entity.

Features

  • Text Normalization : Converts text to lowercase, removes punctuation, and normalizes whitespace.
  • Value Comparison : Compares values directly and semantically (ignoring order for lists).
  • JSON Traversal : Iterates through each key in the JSON objects and compares corresponding values.
  • Language Model Integration : Uses a generative language model to assess semantic similarity and provide a final judgment on whether the data comes from the same entity.

Installation

To use this tool, ensure you have the necessary dependencies installed. You can install them using pip:

pip install genai

Usage

Functions

  1. normalize_text(text) :
* Normalizes the input text by converting it to lowercase, removing punctuation, and normalizing whitespace.
  1. compare_values(val1, val2) :
* Compares two values both exactly and semantically.
* If the values are lists, it ignores the order of elements for semantic comparison.
  1. compare_json(json1, json2) :
* Compares two JSON objects key by key.
* Uses `compare_values` to evaluate each key's values.
* Integrates a language model to assess semantic similarity and provides a final judgment.

Example

import json
import genai
import re

# Define your JSON objects
json1 = {
    "name": "John Doe",
    "address": "123 Main St, Anytown, USA",
    "hobbies": ["reading", "hiking", "coding"]
}

json2 = {
    "name": "john doe",
    "address": "123 Main Street, Anytown, USA",
    "hobbies": ["coding", "hiking", "reading"]
}

# Compare the JSON objects
comparison_results = compare_json(json1, json2)

# Generate final matching result
model1 = genai.GenerativeModel("gemini-2.0-flash-thinking-exp")
result_matching = model1.generate_content("综合这些信息,你认为可以判断两个数据来自同一主体吗?"+json.dumps(comparison_results, ensure_ascii=False, indent=4))
print(result_matching.text)

Contributing

Contributions are welcome! Please open an issue or submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

If you have any questions or suggestions, please contact me:

Wechat qrcode_for_gh_643efb7db5bc_344\(1\)

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