Financial Fraud Detection and Prevention using AI

Machine learning system for processing transaction data that improves anomaly detection in transaction sequences. The system has a categorical history module with decay logic and update logic stages. It stores state data for transactions by category and entity, decaying older data based on time difference. New transaction input is updated and stored. The decayed and updated states are combined to output a scalar value representing anomaly likelihood in sequence. This allows detecting sequential patterns of abnormal transactions.

Source

Automated detection of anomalous user-specified values in financial transactions to prevent fraud and errors. The method involves training a model to classify whether a user-specified value for a financial transaction is anomalous. When a transaction is verified, the user-specified value is compared to the verified value to generate a feature vector. This vector is fed into the trained model to classify as anomalous or non-anomalous. If classified as anomalous, the user is alerted and allowed to dispute the transaction to prevent execution. This allows catching errors and fraud in user-specified values without requiring a source of truth for the correct values.

Source

Detecting and controlling fraud in multi-tiered centralized processing systems like electronic benefits accounts. The system uses clustering, modeling, and locking to detect and prevent fraud at the group level. It clusters transactions by intermediary, generates models from historical data, detects fraud in a tier based on the models, and locks resources in that tier if fraud is found. This stops fraud at the tier level rather than just the transaction level to prevent masking in groups.

Source

A system for detecting abnormal user transactions and identifying potential fraud using machine learning to continuously monitor user behavior and context. The system captures contextual and behavioral factors of users at a smart data hub to develop bio-behavioral models through machine learning. When a transaction request comes in, recent user data is sent to the hub to update the models. The updated models are then compared to the transaction factors to determine abnormality and risk. This allows identifying abnormal transactions based on deviations from a user's normal behavior and context.

Source

Detecting malware in financial accounts by leveraging anomalous cross-customer financial transactions. The method involves detecting financial anomalies among groups of financial account customers experiencing similar financial losses. It links affected accounts with corresponding protection accounts to identify user devices. Then it collects and identifies artifacts disproportionately likely to appear on those devices as potential malware. This allows identifying and protecting users from harmful malware campaigns targeting financial accounts.

Source

Training a cash return fraud recognition model using deep reinforcement learning to accurately identify fraudulent cash returns in transactions. The model uses a two-network architecture with a primary network for recognition and a secondary network for training. The primary network takes transaction features as input to predict if it's a cash return. The secondary network takes the primary prediction along with transaction details to calculate a label value. This label value is used as the return for reinforcement learning to train the secondary network. The primary network's weights are periodically transferred to the secondary network for improved recognition. This allows the model to learn transaction features that indicate high-value cash returns.

Source

Distributing fraud detection for consumer transactions to groups of customers rather than using a centralized model. The method involves training a fraud detection model using multiple customer purchase histories to detect fraud and classify transactions. The model is then distributed to groups of customers, who can use it to detect fraud for their own transactions without sending data to a central server. This enables localized, decentralized fraud detection that is more scalable and private compared to a centralized model.

Source

Detecting fraud in a decentralized electronic payment network using machine learning. The system estimates the route of a payment based on origin and destination, inputs it into a neural network to determine fraud probability, and takes action against high-risk payments and systems involved in fraud.

Source

Detecting and preventing misuse of third-party extensions in financial applications by monitoring operations for migrating user data to competitors. The financial application allows extensions developed using its SDK access based on a single license. It monitors extension operations for indications of data migration. If risk indices exceed a threshold, it limits or denies future extension access. This mitigates misuse of extensions like migrating user data to competitors.

Source

System and method for assessing fraud risk in digital financial transactions involving third-party accounts. It uses secured authentication and inspection to analyze account data for interaction assessment. The method involves accessing a user's external account, retrieving data, analyzing factors like identity, funding, transaction history, and assessing risk. This score is used to determine action like augmenting, flagging, or denying transactions based on fraud potential.

Source

Detecting fraudulent merchants in a commerce system that provides financial processing services for merchants and their agents. The fraud detection involves using machine learning models to analyze merchant activity and characteristics to identify fraudulent merchants. The models can include individual merchant scoring, transaction scoring, and cohort clustering and scoring. The individual merchant scoring involves training models to predict fraud based on merchant attributes and transaction history. The transaction scoring involves training models to predict fraud based on transaction attributes. The cohort clustering involves clustering merchants into groups based on shared attributes and scoring the clusters to predict fraud. Periodically analyzing merchant activity over time allows identifying cohorts associated with fraud even if individual merchants are not flagged.

Source

Detecting fraudulent merchants using machine learning techniques in a commerce system. The system involves a fraud detection engine that analyzes merchant activity to identify potential fraud. The engine uses multiple fraud detection models that analyze different aspects of merchant behavior, like transaction patterns, agent usage, and decline rates. It also clusters merchants into cohorts based on similar attributes and scores those cohorts for fraud risk. This redundant check using cohort scoring helps catch merchants that may not be flagged by individual models. The fraud detection is performed periodically and asynchronously to analyze historical data and identify fraud trends.

Source

Simplifying creation of fraud detection models in services by using authenticated user actions to train the models instead of manually labeled data. The system acquires authenticated user actions from services after authentication, and creates fraud detection models using those authenticated actions to learn what valid actions look like. This leverages known good user behavior to train the models instead of requiring manual labeling of training data.

Source

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.

Source

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.

Source

Automated system for validating workflow completion in tasks like property maintenance using machine learning models to analyze images. The system receives images of work actions and determines if they are suitable for validation using a filter model. If so, a second expert model classifies the images as before, during, or after the work. The hierarchical modeling process can be customized for different workflows. The system also checks image metadata like location and duplication to flag potentially fraudulent activity.

Source

Securely storing and analyzing transaction data using a distributed register and machine learning for dispute resolution. The system allows secure, immutable storage of resource transactions using a distributed register. Disputes can be initiated by users, analyzed using machine learning, and resolutions determined based on historical data. The distributed register prevents unauthorized data manipulation. Users can access verified data via multiple channels.

Source

Preventing fraud in electronic transactions by connecting merchants with known fraudulent phone numbers and rejecting transactions from similar numbers that have been flagged. The system also verifies transactions with separate responsible parties to combat spoofing where fraudsters change their displayed number.

Source

Reducing risk of transactions like ACH transfers by predicting account balances at settlement time using historical data and machine learning models. When a request is received to initiate a transaction, the system retrieves the account history and uses a trained model to project the balance at settlement. This allows assessing the risk of the transaction completing successfully without needing real-time account balance checks.

Source

Identifying fraud transactions in transactions classified as legit by a ML model using separate trained ML models for novelty detection. The method involves training separate ML models, one for fraud and one for legit transactions, using samples of historical labeled data. After deployment, transactions classified as legit by the main ML model are sent to the legit model to mark as similar or novel. Novel transactions are further sent to the fraud model. High novelty transactions are reported as potential unknown fraud and high similarity transactions are reported as potential missed fraud.

Source