Tax Return Fraud Detection via Data Entry Analytics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Tax return preparation systems face challenges in identifying and preventing fraudulent activities, particularly stolen identity refund fraud, which can lead to financial losses and damage trust between users and service providers.
Innovation Solution
A security system that compares new data entry characteristics of tax returns with prior data to generate a risk score using analytics models, delaying suspicious filings and communicating with users to cancel fraudulent submissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If tax return preparation systems implement comprehensive fraud detection measures, then security and reliability improve, but system complexity and processing time increase
Solution Approach 1:
The system performs preliminary fraud risk assessment by analyzing data entry characteristics before tax return filing. Analytics models evaluate multiple characteristics (typing speed, cursor movement, field completion patterns) in advance to generate risk scores, enabling early identification of potentially fraudulent returns without requiring complex real-time intervention mechanisms.
Solution Approach 2:
The system introduces an intermediary analytics model layer between data entry and fraud determination. This model acts as a mediator that processes raw data entry characteristics and translates them into meaningful risk scores, simplifying the overall system architecture by centralizing the complex analysis logic in a dedicated component rather than distributing it throughout the entire system.
2Measurement precision
If the system analyzes multiple data entry characteristics to improve fraud detection, then measurement precision improves, but the difficulty of detecting and measuring increases
Solution Approach 1:
The system segments fraud detection into multiple independent characteristic analyses. Instead of attempting to analyze all data entry features simultaneously as a single complex task, the system evaluates individual characteristics (typing speed, cursor movement patterns, field completion sequences, time spent on sections) separately and combines their risk scores. This segmentation reduces the immediate analytical complexity while maintaining comprehensive detection precision.
Solution Approach 2:
The system transforms raw data entry characteristics into standardized risk score parameters. By converting diverse measurements (time stamps, cursor coordinates, text input rates) into a unified risk score scale, the system simplifies the aggregation and comparison of multiple characteristics, making the overall detection process more manageable without sacrificing measurement precision.
3Reliability
If the system implements risk reduction actions such as delaying filing, then fraud prevention improves, but loss of time for legitimate users increases
Solution Approach 1:
The system applies differentiated risk reduction actions based on local risk characteristics. Instead of uniformly delaying all tax returns, the system targets only those with elevated risk scores for delayed filing or additional verification. Legitimate returns with low risk scores proceed through normal processing channels without delay, minimizing time loss for the majority of users while maintaining strong fraud prevention for high-risk cases.
Data Source
AI summary
Stolen identity refund fraud is one of a number of types of Internet-centric crime (i.e., cybercrime) that includes the unauthorized use of a person's or business' identity information to file a tax return in order to illegally obtain a tax refund from, for example, a state or federal revenue service. Because fraudsters use legitimate identity information to create user accounts in tax return preparation systems, it can be difficult to detect stolen identity refund fraud activity. Methods and systems of the present disclosure identify and address potential fraud activity. The methods and systems analyze data entry characteristics of tax return content that is provided to a tax return preparation system to identify potential fraud activity and perform one or more risk reduction actions in response to identifying the potential fraud activity.