Quantum-ML Fraud Detection for Real-Time Secure Transactions

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Solution Overview

Problem

Current fraud detection algorithms in financial transactions are inefficient, producing high false positive rates and requiring manual human intervention, which prevents real-time detection and prevention of fraudulent transactions.

Innovation Solution

A system utilizing quantum computing and machine learning to process financial transactions, employing quantum resistive cryptography and trained models to generate encrypted data for real-time fraud detection and verification, reducing the need for human intervention.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If current fraud detection algorithms are used, then fraud detection capability is provided, but false positive rate increases and manual intervention is required

Engineering Contradiction:

Improvefraud detection accuracyVSAvoidtransaction processing speed

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system segments fraud detection into multiple specialized AI models (supervised learning, unsupervised learning, reinforcement learning) that operate in parallel to analyze different aspects of transactions, allowing comprehensive detection without requiring manual review of all flagged transactions

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent replaces manual mechanical review processes with automated AI/ML systems that can process and adjudicate fraud detection results in real-time, eliminating the bottleneck of human intervention while maintaining high accuracy through multiple algorithmic approaches

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If manual analysis is performed to verify flagged transactions, then detection accuracy improves, but time consumption increases

Engineering Contradiction:

Improvefraud detection precisionVSAvoidverification time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system implements self-service fraud verification through multiple AI models that automatically analyze and adjudicate flagged transactions without human intervention. The supervised, unsupervised, and reinforcement learning models work together to verify transactions autonomously, eliminating time-consuming manual review while maintaining high precision

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent performs preliminary fraud analysis using multiple AI models before transactions are completed, flagging and verifying suspicious activities in advance. This preliminary action allows the system to prevent fraudulent transactions before they affect the financial system, rather than requiring time-consuming post-event manual investigation

Inventive Principle:

Principle #10Preliminary action

3Speed

If quantum computing is used for transaction processing, then processing speed improves, but system complexity increases

Engineering Contradiction:

Improvetransaction processing speedVSAvoidcomputing system complexity

Core Design Contradiction:

SpeedVSDevice complexity

Solution Approach 1:

The patent merges quantum computing capabilities with classical AI/ML systems to create a hybrid architecture. Quantum processors handle specific computationally intensive tasks (such as cryptographic operations and optimization problems) while classical systems manage the broader fraud detection workflow, achieving speed improvements without requiring complete system replacement

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The system introduces quantum-resistant cryptographic algorithms as an intermediary layer that bridges quantum and classical computing environments. This intermediary enables secure communication and data exchange between quantum processors and classical AI models, managing complexity while enabling quantum acceleration

Inventive Principle:

Principle #24Intermediary (Mediator)

4Reliability

If quantum-resistant cryptography is implemented, then security against future threats improves, but computational overhead increases

Engineering Contradiction:

Improvetransaction securityVSAvoidcomputational energy consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The patent applies quantum-resistant cryptography selectively to only the portions of transaction processing that require highest security (key exchange, digital signatures, and sensitive data protection) rather than encrypting all data with quantum-resistant algorithms. This partial application maintains security against future quantum threats while minimizing computational overhead and energy consumption

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS12518272B2Systems and methods for secure transaction processing using machine learning and quantum computing

Publication Date: 2026.01.06 MCKINSEY & CO INC

AI summary

A computer-implemented method for secure transaction processing using machine learning and quantum computing includes receiving input parameters corresponding to a transaction at a processor. A quantum computing processor processes the input parameters using one or more sets of quantum resistive cryptography instructions and further generates encrypted transaction data from the input parameters. The quantum computing processor further generates an indication of whether a transaction is fraudulent based upon the encrypted transaction data by applying a machine learning algorithm or model. The processor then transmits the indication via an electronic network.