AI Methods for Enhanced Money Laundering Detection

A method for detecting anomalous activity, like fraud or money laundering, using a dynamic approach that combines transaction analysis and social network analysis. The method involves calculating anomaly scores for user transactions using unsupervised and supervised algorithms trained on transaction attributes. It also calculates network anomaly scores based on interconnected user profiles. The potential for anomalous activity is determined by combining the transaction and network scores. This provides dynamic detection from multiple angles rather than just transaction analysis alone.

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Secure electronic financial transactions system that uses AI, biometrics, and multimodal data analysis to prevent fraud and improve user experience. The system collects multimodal data from visual, auditory, and tactile sensors during transactions. It uses AI modules like biometric authentication, transaction anomaly detection, geospatial analysis, behavioral analysis, and third-party data integration to analyze this data for fraud prevention. The system also communicates with users through modalities like vision, audio, touch, taste, smell, temperature, pain, and balance to address fraud concerns or request verification.

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Determining the likelihood of a connection between funds entering and exiting a cryptocurrency tumbler, to identify laundered funds. The method involves using classical computers to formulate an optimization problem representing the tumbler's flows. This is converted to a quantum-computable quadratic unconstrained binary optimization (QUBO) form. A quantum computer solves the QUBO to find matrices representing the tumbler's internal flows. Classical post-processing generates a probability matrix showing likelihoods of connections between input and output funds.

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Detecting money laundering activities hidden in large volumes of legitimate transactions using neural networks to compare financial transaction patterns. The method involves generating target and baseline vectors representing transaction activity for a specific party and overall region, respectively. A Siamese neural network compares the vectors to detect deviations indicative of potential money laundering. A drift score between the vectors is calculated, constrained in a learned space between non-money laundering and money laundering transactions. An alarm is triggered if the drift score indicates potential money laundering.

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System for more effectively and efficiently identifying potentially suspicious cash structuring activity that may be related to money laundering or other financial crimes. The system uses a deep learning model with convolutional layers and non-linear activation functions to analyze daily transaction patterns and global transaction patterns separately. It extracts daily patterns from the daily transactions, aggregates them, and combines them with global transaction patterns. This aggregated input is fed into a classifier to generate scores for the account. If the score exceeds a threshold, it alerts for potential suspicious activity. The model uses techniques like dilated convolutions, L2 weight decay, Adam optimization, and ensemble teacher distillation to improve performance.

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Identifying suspicious activity using a multidimensional approach that goes beyond just transaction data to improve accuracy and adaptability compared to existing systems. The method involves preprocessing monitoring data using a plurality of vectors, applying rules to each vector, and triggering actions based on the rules. The vectors capture context beyond just transactions, such as party details, location, etc. This multidimensional approach allows identifying suspicious activity that would be missed by single-dimensional transaction-only analysis.

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Using AI to predict the growth of money laundering networks in financial systems and prioritize action against them. The method involves monitoring networks already identified as potential money laundering networks, predicting their future growth size based on past changes, and prioritizing networks with high growth potential for intervention. The prediction is done using AI models on historical network data.

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Using machine learning to improve anti-money laundering (AML) analysis by accurately identifying accounts and account holders with money laundering risk. The method involves obtaining a dataset of accounts and applying a trained algorithm to generate money laundering risk scores for each account holder. Accounts with high risk scores are flagged for investigation. The algorithm uses a dataset of aggregated financial data from multiple sources to analyze account variables like transactions, characteristics, and relationships. It provides actionable recommendations and prioritization for compliance teams.

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Machine learning techniques for reviewing alerts in regulatory settings like anti-money laundering (AML) that provide insights and a flexible interface to improve efficiency and accuracy. The techniques involve calculating context-aware representations of entities like customers and transactions using self-supervised graph neural networks. These representations can be used to derive insights like clustering, anomaly scoring, and period detection to aid in AML alert review. The representations capture entity behavior based on surrounding context from a bipartite graph.

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Using machine learning to identify potential criminal activity involving financial institutions' customers by analyzing news alerts. The method involves extracting entities from news alerts, resolving them to known customers, enriching the alerts with customer data, predicting the likelihood of a suspicious activity report, and transmitting the enriched alerts to the appropriate investigator.

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Detecting financial crimes like money laundering using machine learning models that leverage network effects between financial entities. The method involves generating network features by applying risk indicators to a graph model of financial entities and their relationships. These network features are fed into machine learning models trained on both network and non-network features to predict financial crimes. Alerts are generated when crimes are predicted, identifying the involved entities. The network representation helps reveal hidden connections and improve crime detection compared to traditional non-network features.

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Detecting fraud in financial networks without relying on hardcoded rules by using graph neural networks to identify suspicious transaction patterns. The method involves generating a transaction graph representing the financial network with synthetic suspect transactions. Subgraphs are extracted and used to train a graph neural network model to classify subgraphs as suspect. This model is then applied to new financial networks to locate potentially fraudulent transaction patterns. The subgraph extraction dynamically selects nodes based on their transaction history to capture related activity.

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A machine learning engine with a trained model for detecting potentially fraudulent or illegal transactions like money laundering, along with a narrative generation component that explains why the transaction was flagged. The model uses features like transaction type, accounts, amounts, addresses, sources, etc. to identify potentially suspicious transactions. If a transaction is flagged, the narrative generation component provides a detailed explanation of the reasons behind the flag. This helps human reviewers understand the rationale for the flagged transaction.

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Detecting transaction laundering in the payment ecosystem using machine learning models to flag potentially fraudulent merchants and transactions. The system uses multiple machine learning models to analyze merchant and transaction data for indicators of transaction laundering. If the models collectively indicate a high likelihood of laundered transactions, further investigation is triggered. The models can also have decision trees and weighted incorrect classifications to improve accuracy. The approach involves using multiple models in conjunction to improve detection of transaction laundering compared to relying on a single model.

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Detecting illicit activity like money laundering that escapes rule-based systems by training a neural network generator to create synthetic illicit activity and a discriminator to distinguish between real and synthetic activity. The generator is trained using feedback on how to avoid triggering rule-based systems, and a money laundering objective. The discriminator learns to distinguish between real and synthetic activity based on the tensor representation of connected graphs of transactions. By focusing on generating illicit activity that avoids rules and maximizes money laundering goals, the generator can identify weaknesses in rule-based systems.

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Detecting fraudulent financial activity involving cryptocurrencies and money mules using AI. The method involves analyzing user banking account activity to identify potential money mule accounts. It combines metrics like frequency of cryptocurrency transactions and technology adaptation with risk scores to more accurately detect money mule accounts compared to just using risk scores. The metrics are analyzed using machine learning models.

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Generating suspicious activity reports using machine learning that can efficiently analyze large transaction histories to identify potential financial crimes. The system compresses transaction data using lossy compression methods and inputs it into a trained neural network to score the likelihood of criminal activity. If the score exceeds a threshold, it triggers generation of a suspicious activity report. This allows leveraging historical transaction data without analyzing all variables simultaneously, reducing false positives and improving efficiency.

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Using AI to validate security events like fraud or money laundering by collecting user information related to specific events and analyzing it with trained AI models. The models compare user data against baselines to generate risk metrics. This allows investigating security events in context rather than relying solely on thresholds or anomalies.

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Transaction data processing method for financial analysis using graph convolutional networks (GCNs) and transformers to extract spatial-temporal features from transaction graphs. The method involves obtaining transaction data for an account over multiple time windows, extracting spatial features using GCNs and temporal features using transformers, and generating a feature representation for the account based on the combined spatial-temporal information. This representation can be used for downstream analysis tasks like credit risk modeling, fraud detection, and money laundering detection.

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Detecting money laundering using a machine learning system that prioritizes alerts and links accounts to improve detection accuracy. The system uses a profile-based feature construction technique to create features from entity data like KYC info and transaction history. It calculates an AML Threat Score using supervised learning on labeled cases to prioritize alerts. The score is calibrated over time using self-calibrating outlier detection. Accounts are efficiently linked through behavior-sorted lists that capture frequent transactions. This helps propagate risk information between associated accounts. The system can also be extended to analyze emerging payment systems like cryptocurrencies by leveraging KYC data and legal exchange information.

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