AI Call Fraud Detection With Neural Risk Scoring

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Solution Overview

Problem

Existing call fraud detection systems are inefficient and resource-intensive, often producing high false positives and requiring extensive manual intervention, while failing to scale effectively due to focusing on individual aspects and lacking comprehensive real-time analysis.

Innovation Solution

An AI-based system utilizing machine learning and blockchain technology for real-time fraud detection, integrating an Artificial Neural Network (ANN) to analyze call data, generate feature vectors, and provide fraud verdicts, while ensuring data immutability and transparency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional fraud detection systems continuously monitor all calls in real-time, then fraud detection capability is improved, but resource consumption and system complexity increase significantly

Engineering Contradiction:

Improvefraud detection capabilityVSAvoidresource consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The system applies fraud scoring selectively based on risk thresholds rather than analyzing every call in detail. High-risk calls receive comprehensive analysis while low-risk calls are processed quickly or skipped, reducing overall resource consumption while maintaining detection effectiveness

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The system pre-calculates and stores fraud scores and risk indicators in advance using batch processing of historical data. This preliminary analysis enables faster real-time decision-making without requiring intensive continuous computation during live call monitoring

Inventive Principle:

Principle #10Preliminary action

2Speed

If rule-based systems use simple predefined rules for fraud detection, then processing speed is improved, but detection accuracy decreases due to high false positive rates

Engineering Contradiction:

Improveprocessing speedVSAvoiddetection accuracy

Core Design Contradiction:

SpeedVSMeasurement precision

Solution Approach 1:

The system dynamically adjusts fraud scoring parameters and thresholds based on learned patterns from historical data. Instead of using fixed rules, the model adapts parameters like call duration weights, frequency thresholds, and risk factor priorities to optimize both speed and accuracy for different fraud scenarios

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent introduces a fraud scoring model as an intermediary layer between raw call data and detection decisions. This model synthesizes multiple features and rules into a unified risk score, reducing false positives by considering contextual relationships that simple rules miss while maintaining fast processing through efficient scoring algorithms

Inventive Principle:

Principle #24Intermediary (Mediator)

3Reliability

If fraud detection systems require extensive manual intervention for analysis, then detection thoroughness is improved, but response time increases and productivity decreases

Engineering Contradiction:

Improvedetection thoroughnessVSAvoidresponse time

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system automatically generates fraud scores, risk assessments, and detection reports without requiring manual analyst intervention for routine cases. The automated fraud scoring model independently evaluates calls, flags suspicious patterns, and generates actionable insights, enabling the system to serve itself and reducing dependency on human resources for basic detection tasks

Inventive Principle:

Principle #25Self-service

4Ease of manufacture

If existing systems focus on individual aspects of call analysis, then specific detection tasks are simplified, but comprehensive fraud detection coverage is reduced leading to detection gaps

Engineering Contradiction:

Improvedetection task simplicityVSAvoiddetection coverage

Core Design Contradiction:

Ease of manufactureVSReliability

Solution Approach 1:

The fraud scoring model serves multiple detection functions simultaneously - it evaluates call metadata, analyzes behavioral patterns, assesses voice characteristics, and identifies spoofing attempts within a single unified framework. This multi-functional approach provides comprehensive coverage without requiring separate specialized systems for each detection aspect

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12432294B1Method and system for AI-based real-time fraud detection based on call data

Publication Date: 2025.09.30 HOLLAND ROBERT

AI summary

A system for an automated real-time fraud detection based on predictive analytics of call data including a processor of a call analysis server (CAS) node configured to host a machine learning (ML) module and connected to at least one user-entity node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: capture user call data including a plurality of key elements from the at least one user-entity node; parse the user call data to extract a plurality of classifying features based on the plurality of key elements; query a local calls database to retrieve local historical calls-related data based on the plurality of classifying features; generate at least one feature vector based on the plurality of classifying features and the local historical calls-related data; provide the at least one feature vector to the ML module coupled to an Artificial Neural Network (ANN); receive a plurality of user call ranking parameters from a call predictive model generated by the ML module using outputs of the ANN based on the feature vector; and generate a fraud verdict for the user call based on the plurality of call ranking parameters.