AI Blockchain Fraud Detection With Smart Contract Intervention
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Solution Overview
Problem
Existing fraud detection technologies are inadequate in adapting to evolving fraud strategies, require large volumes of labeled training data, fail to detect sophisticated fraud schemes, and produce high false positives, leading to reactive and costly recovery efforts.
Innovation Solution
A computer-implemented method using predictive analytics, permissions-based distributed ledger systems, and smart contracts for real-time fraud detection, incorporating multi-modal sensor data and machine learning algorithms to analyze transaction patterns and execute automated risk assessments and interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If rule-based systems are used for fraud detection, then the system is simple to implement, but it cannot adapt to evolving fraud strategies and has low detection accuracy
Solution Approach 1:
The patent implements dynamic adaptability by enabling the system to learn from new fraud patterns continuously. The machine learning models are trained on evolving fraud data, allowing the detection rules to adapt automatically without manual reconfiguration, thus resolving the contradiction between implementation simplicity and adaptability to changing fraud strategies.
Solution Approach 2:
The system performs self-learning and self-adjustment through automated machine learning training on new fraud patterns. This self-service capability allows the system to improve its detection accuracy autonomously without requiring constant manual intervention to update rules, maintaining ease of implementation while gaining adaptability.
2Adaptability or versatility
If statistical and machine learning-based systems are used, then the ability to learn from historical data is improved, but the requirement for large volumes of labeled training data increases
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on available historical data before deployment. This allows the system to have initial detection capabilities with limited data, and then continuously improve as more data becomes available, reducing the upfront data requirement while maintaining learning ability.
Solution Approach 2:
The system implements continuous learning where models are repeatedly trained on new data streams after initial deployment. This continuous action allows the system to accumulate learning capacity over time, reducing the need for large initial training datasets while maintaining the ability to learn from historical patterns.
3Reliability
If existing fraud detection technologies are used, then the system can identify fraudulent activities after they occur, but it produces high false positives and cannot prevent fraud proactively
Solution Approach 1:
The patent implements preliminary action by using machine learning models to predict and flag potentially fraudulent transactions before they are completed. The system analyzes patterns in real-time and intervenes preemptively, transforming the reactive detection approach into a proactive prevention mechanism that reduces false positives through more accurate prediction.
Solution Approach 2:
The system incorporates feedback loops where detection results and outcomes are fed back into the training data, continuously improving model accuracy. This feedback mechanism reduces false positives over time by learning from previous errors and refining detection thresholds based on actual fraud patterns and legitimate transaction behaviors.
Data Source
AI summary
Disclosed herein are methods and systems related to fraud detection, risk assessment, and intervention. More specifically, this is related to conducting and monitoring transactions on a blockchain through the use of artificial intelligence, AI, and machine learning. This combination can be applied to financial systems to create an Artificial Intelligence Financial Technology Blockchain, AIFTB. The AIFTB is able to receive a variety of data and use it to predict actions being taken by users and detect if there are any risks or legal concerns with the activity. The AIFTB is then able to apply its training to execute smart contracts on the blockchain based on the risk and legality of the data it received. Finally it will alert appropriate authorities based on the predictions.