Financial Fraud Prevention using AI

Training machine learning models for fraud detection in banking using historical data to identify fraud patterns and victim patterns. The method involves assigning initial risk labels to accounts based on transaction and access behavior, then iteratively refining the labels by comparing account features to known fraud and victim patterns. This iterative labeling process helps the models learn fraud and victim behaviors more accurately by leveraging the institution's own data.

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Monitoring machine learning models used for detecting fraud in networks to maintain accurate fraud identification over time. The method involves actively monitoring features of the model to detect drift indicating behavioral changes. If a feature drifts from its expected distribution, it indicates the model is misidentifying fraud. Corrective actions like re-training the model with updated features are taken. This allows quicker identification and fixing of model degradation compared to waiting for misidentified frauds.

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Real-time fraud detection in data sparse environments using machine learning models trained in data sparse environments, a data transformation step to minimize latency, and an output that provides both a fraud detection metric and confidence level. The method involves receiving a communication, transforming it into a fixed-length feature input, passing it through a gradient boosted decision tree model trained in data sparse environments, and determining if to cancel the communication based on the output confidence. This allows accurate, real-time fraud detection with low latency and constant lookup time in sparse data environments.

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Identifying concept drift during online training of machine learning (ML) models, like fraud detection models, to more accurately update the models in real time as new data arrives. The method involves detecting changes in data relationships using uncertainty bounds (UB) metrics. It calculates UB metrics from classifiers without explicit margins to identify positive, negative, or uncertain classifications. By training multiple base models on subsets of features, it improves robustness and distributes weights across features. This allows determining UB differences between training and test sets to evaluate concept drift.

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This review examines the impact of using Federated Learning (FL) on No-Code AI tools and how it could change data security in BFSI industry. Despite keeping local each machine, FL allows different organizations to train models together. It gives an overview what components model contains, its functions accurate is at predicting things by comparing other machine learning that were used as references. Our work highlights successful cases uses innovative develop approach can boost prevention, analysis monitoring fraud, risks compliance BFSI. The challenges included paper are heterogeneous data, threats challenge scaling, along with future research ideas. These findings matter most specialists working AI, experts finance, who need more privacy their solutions.

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This study aims to compare the performance of two popular machine learning algorithms, Random Forest and K-Nearest Neighbor (KNN), in predicting creditworthiness online lending systems. The research uses publicly available Loan Approval Prediction Dataset from Kaggle, which contains borrower profiles such as employment status, number dependents, annual income, loan amount, term, credit score. Data preprocessing included cleaning, handling missing values, outlier removal, transformation through normalization encoding. dataset was divided into 80% training data 20% testing data. configured with 100 decision trees unlimited depth, while KNN used an optimal k value 5 determined by grid search. Model evaluated using accuracy, precision, recall, F1-score. results showed that outperformed consistently higher values (97%) across all metrics, demonstrating strong stability superior pattern recognition capabilities. KNN, accuracy 89%, still good can be considered a lightweight alternative for simpler applications.

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This study aims to develop a mechanism for cyclic-dynamic screening of the activities primary financial monitoring entities (PFMEs) under wartime conditions. The proposed identifies atypical behavioural patterns, enhances responsiveness rapidly evolving risks, and improves quality offence detection. constructed additive time series models two distinct periods: pre-war (20112019) (20212024). Smoothing techniques, decomposition, seasonal component estimation, analytical trend modelling were applied. In first period, model demonstrated high accuracy (coefficient determination R = 90,87%), while in second remained acceptable (R 65,80%), reflecting increased volatility environment. results highlight importance analyzing fluctuations trends detect suspicious transactions on improve effectiveness procedures during wartime. identifying anomalous operations, enables adaptive risk response, strengthens control flows. practical implementation this methodology contributes ensuring national stability security, which is critically important maintaining integrity state's system

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Phishing and digital arrest attacks pose significant threats to e-commerce security, necessitating advanced AI-driven detection mechanisms. In this context, study proposed a hybrid deep learning model integrating BERT for feature extraction, Atrous Spatial Pyramid Pooling (ASPP) multi-scale refinement, CNN-based classification network. The encoder captures semantic contextual features, while ASPP enhances spatial hierarchies using dilated convolutions. A CNN classifier with batch normalization ReLU activation ensures robust classification. Extensive experiments on Kaggle dataset demonstrate superior performance, achieving 97.81% accuracy, 98% precision, an AUC of 0.9972, outperforming GRU, LSTM, RNN, Transformer models.

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Abstract: This study presents an automated framework for threat intelligence gathering using Natural Language Processing (NLP) on dark web data. The growing sophistication of cyberattacks necessitates real-time detection emerging threats. Traditional manual analysis forums is time-consuming and insufficient. research proposes a hybrid NLP pipeline that integrates named entity recognition, sentiment analysis, topic modeling to extract actionable indicators from darknet discussions. A dataset comprising over 100,000 posts was analyzed, yielding high accuracy in identifying cyber actors, malware variants, planned attacks. proposed model outperformed baseline models by 17% F1 score. findings highlight the utility systems reducing human workload accelerating defense responses. Keywords Threat Intelligence, Processing, Dark Web, Cybersecurity, Automation, Topic Modeling

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<title>Abstract</title> <italic>With the rapid growth of financial data, extracting accurate and contextually relevant information remains a challenge. Existing question-answering (QA) models struggle with domain-specific terminology, long-document processing, answer consistency. To address these issues, this paper proposes Intelligent-Aware Transformer (IAT), QA system based on GLM4-9B-Chat, integrating multi-level aggregation framework. The employs Financial-Specific Attention Mechanism (FSAM) to enhance focus key terms, Dynamic Context Embedding Layer (DCEL) improve Hierarchical Answer Aggregator (HAA) ensure response coherence. Additionally, Knowledge-Augmented Textual Entailment (KATE) strengthens models generalization by inferring implicit knowledge. Experimental results demonstrate that IAT surpasses existing in tasks, exhibiting superior adaptability long-text comprehension reasoning. Future work will explore computational optimizations, advanced knowledge integration, broader applications.</italic>

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Against the backdrop of rapid advancements in fintech and continuous expansion credit markets, traditional risk assessment methods have revealed significant limitations. Machine learning offer new opportunities for management. This study focuses on applying machine to We utilize a dataset from Kaggle (Default Credit Card Clients Dataset) analyze compare performance logistic regression, decision tree, random forest models across multiple dimensions, including accuracy, recall rate, interpretability. The results demonstrate that tree model exhibits comprehensive default prediction. Future research could incorporate diverse data types, develop visualization tools, establish real-time monitoring dynamic updating systems, extend applications industries enhance accuracy foresight assessment, thereby promoting widespread adoption financial holds theoretical significance offers practical technical solutions real-world lending operations.

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Extensible fraud detection system for service providers that allows users to create custom fraud detection rules and immediately implement them, reducing fraud without blocking legitimate transactions. The system groups rule features based on data sources to reduce latency, vets new rules on historical data to test effectiveness, and warns about overly aggressive rules. It also caches frequently accessed features to further reduce latency. This allows users to easily create and deploy custom fraud detection rules without negatively impacting transaction processing time.

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Reducing false positives in financial transaction fraud detection using machine learning. The method involves training a false positive reduction model (FPRM) recursively using labeled transaction data from an initial fraud scoring model. The labeled data includes transactions marked as high risk that were later determined to be either false positives or true positives. The recursive training allows refining the FPRM to better distinguish true fraud from false positives. The FPRM is then used in production to further reduce false positives in fraud detection.

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This paper is aimed at developing a computational intelligence model for real-time detection and prevention of credit card fraudulent transactions within digital cyber forensic investigations. Decision Trees, Support Vector Machines Artificial Neural Networks were employed in the design system to ensure reliable efficient fraud detection. In order eliminate noise enhance accuracy analysis, actual transaction data entered set was used. The trained through supervised learning technique identify patterns real time. To verify effectiveness developed system, post-hoc comparisons done regarding models terms accuracy, precision, recall, f1 score. calculation revealed that provide best as it reached 98% precision correct activity identification. research has helped reduce rise ecosystem by employing contemporary approaches. It also assists investigators mitigate financial damage enhances security measures institutions.

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Machine learning model framework for accurately classifying data like transactions using multiple graph analysis techniques to overcome limitations of single-dimensional models. The framework uses multiple models that analyze actual attributes, fuzzy attributes, and community aspects of transactions in a graphical manner. It generates a graph representing connections between transactions and fuzzy attributes. This allows capturing relationships and patterns among transactions that would be missed by single-dimensional models. By analyzing multiple dimensions, the framework classifies transactions more accurately compared to conventional models that analyze just one or two dimensions.

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Generating targeted transaction processing rules for fraud prevention using machine learning without requiring expert guidance. The system identifies predictive attributes from historical transactions that indicate fraud, allows a user to select one, and sets a threshold value for that attribute. This generates a customized rule to block future transactions with that attribute value. The system processes transactions using the customized rule to detect and prevent fraud scenarios specific to the user.

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Automated generation of suspicious activity reports (SARs) using large language models (LLMs) trained on transaction data and bank databases. The system extracts transaction features, predicts fraud risk, and generates an LLM prompt based on the transaction. It then generates a SAR report using the LLM to format and summarize the transaction data. This provides consistent and complete SARs compared to manual preparation.

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Unified Payments Interface (UPI) has revolutionized digital transactions in India offering convenience and real-time processing. However, this rapid adoption also led to a surge fraudulent activities, challenging the efficacy of traditional rule-based fraud detection methods. These conventional systems often struggle adapt evolving patterns, necessitating more robust adaptive solutions. In response these challenges, researchers have explored machine learning techniques detect activities within UPI transactions. While existing strategies shown promise, they are frequently validated on limited or synthetic datasets, which may not fully capture complexities real- world scenarios. To address limitations, comprehensive evaluation prevalent classifiersincluding Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Naive Bayes, Decision Trees, Random Forests, Convolutional Neural Networks (CNNs)was conducted. Building upon analysis, we propose (CNNs) framework, designed enhance capabilities

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Detecting suspicious transaction patterns in financial networks using graph neural networks trained on synthetic patterns. The method involves generating a transaction graph for a reference network with synthetic suspect patterns, extracting subgraphs, training a GNN on those subgraphs, and applying it to new networks to find potentially fraudulent patterns. The subgraphs are dynamically defined based on node metadata to include correlated activity.

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Detecting and preventing fraud in financial accounts by analyzing user behavior and patterns to identify potential fraudsters. The system analyzes factors like phone numbers, call origins, voice profiles, and question-answer sequences to determine likelihood of fraud. If fraud is suspected, alerts are sent to users and restrictions are placed on account access. The system uses AI/machine learning to train models on known fraud methods and behavior patterns.

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1.ABSTRACT Frauds in online transactions is increasing like crazy these days, and old methods just ain't cutting it anymore. Banks businesses losing tons of money cause the fraudsters always coming up with new tricks. But guess what? AI might be hero we need - its getting really good at spotting fakes before they happen.. This paper gonna look how machine learning stuff helps catch frauds better than humans can. We talk about systems learns from past data, finds weird spending patterns that don't make sense, even predicts type scams spread. not perfect sometimes blocks normal people's purchases (so annoying!) or misses clever frauds, Also there Is big questions privacy since needs so much personal data's to work good. Looking real cases, see already helping lots companies reduce by 40-50%. technologys still got ways go can fully trust it. Main point making payments safer before, but gotta fix mistakes smarter.

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Real-time transaction approval process that improves speed and stability of approving transactions by using named entity restrictions based on machine learning models. The process involves identifying entities involved in transactions from past data, creating rules specifying authorized entities, and iteratively training machine learning models to accurately identify entities in real-time. The identified entities are compared against the rules to approve transactions. This allows faster approval compared to manual review while still enforcing entity restrictions.

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Generating a virtual asset wallet address blacklist using graph attention networks (GAT) to proactively detect high-risk virtual asset wallets that are prone to illegality. The blacklist is generated by training a GAT model using full node indices, common transaction data, and an existing blacklist. The model calculates scores for wallet addresses based on their connections and transactions. Addresses with high scores are added to the GAT blacklist.

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Sharing transaction-related data between entities of a payment network using a common transaction ID. The method involves generating a confidence score indicating the likelihood that two transactions are the same using a machine learning model. If the confidence score exceeds a threshold, a common transaction ID is generated based on the attributes of one transaction. This common ID is then used to request additional transaction data from the other entity referencing the common ID.

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Detecting potential fraudulent use of identification items like credit cards by estimating if multiple events using cards with identical partial codes are from the same card or different cards based on the time elapsed between events. This allows detecting fraud without exposing full card numbers. It's unlikely for multiple cards with matching partials to be used in quick succession, so a short time gap indicates potential fraud.

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Automated system to detect and freeze cryptocurrency wallets used for laundering illicit funds. The system traces cryptocurrency transactions through mixing organizations to identify all their wallets. It then initiates secondary transfers between known wallets using the mixing wallets. By monitoring these transfers, it can detect patterns indicative of obfuscating illicit funds transfers. Suspect wallets are then frozen.

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Fraud detection in service provider systems using machine learning models. The system involves using two machine learning models to improve fraud detection. The first model is a transaction-level model that determines fraud probability based on transaction features. The second model is a user-level model that determines user fraud probability based on user behavior patterns. The system combines the outputs of both models to make a final fraud decision. If the transaction-level model indicates high fraud probability but the user-level model indicates low fraud probability, it is a potential false positive. Similarly, if the transaction-level model indicates low fraud probability but the user-level model indicates high fraud probability, it is a potential false negative. In such cases, the system performs additional checks or escalates the transaction for manual review to mitigate errors.

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Using machine learning to identify combinations of attributes that cause abnormal behavior in devices or payment transactions. The approach involves training a machine learning model on historical data to correlate combinations of attribute values with abnormal outcomes. Then, for new devices or transactions, if the model indicates a high rate of abnormality for their attribute combination, it can flag them as potential issues. This allows faster and more scalable detection of problematic attribute combinations compared to brute force scanning.

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Detecting suspicious payment cards at point-of-sale terminals and ATMs to prevent card cloning fraud. The technique involves capturing images of inserted payment cards and analyzing them to determine if they are authentic or cloned. The images are compared against known characteristics of genuine cards to detect deviations. Factors like bank logos, card designs, and wear patterns are analyzed. If a card is flagged as suspicious, an alert is generated and the transaction can be blocked.

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System for detecting money laundering through gaming machines by monitoring and analyzing machine usage patterns. The system uses machine learning models to identify potential money laundering activity by generating inferences from learned patterns. It monitors gaming machines for behaviors indicative of money laundering and notifies the casino if likely laundering is detected. The models analyze factors like win/loss ratios, betting patterns, and transaction volumes to identify suspicious activity.

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Counter fraud system that uses device signatures to proactively detect and prevent fraud. The system compares a device's identifying parameters to signatures of devices associated with fraud. If similarities are found, it raises suspicion and can initiate security measures like 2FA or transaction denial. The device signatures are built by analyzing parameters of devices used in fraud.

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Monitoring electronic transactions to detect potential fraud by comparing the location of the user's device to the transaction location. When a transaction is initiated, the device location is checked against the last known location from cached data. If they don't match, an alert is generated. This leverages the fact that a user's device usually stays nearby during a transaction. If the device is far away, it could indicate fraud.

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Dynamic fraud detection system that prioritizes important fraud events based on factors like fraud type, activity, and temporal information. The system uses a fraud importance engine to calculate scores for fraud events that are fed into the fraud detection engine. This allows the detection engine to prioritize important frauds and optimize performance metrics. The fraud importance scores are generated from user-defined parameters like fraud type, cross-channel activity, etc.

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Verification of unstructured data like invoices using optical character recognition (OCR) and database lookup to accurately extract and analyze data elements like account numbers, company registrations, and signatures. The method involves identifying predetermined data elements in unstructured sources like invoices using OCR, then using those elements to access a database and retrieve verified data. This verified data can then be used for further analysis of the unstructured data. The system can also check for fake invoices by analyzing signatures and cross-referencing with a database.

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Reducing resource consumption and latency of fraud detection systems in commerce platforms with high transaction volumes. The method involves leveraging feature extraction and machine learning techniques to optimize fraud detection. Instead of generating and communicating all features for each transaction, a subset of critical features are identified and precomputed offline. These precomputed features are then cached and reused for similar transactions, reducing the resource-intensive feature generation and communication. The remaining features are dynamically computed on-the-fly. This partial feature reuse and dynamic computation reduces resource consumption and latency compared to generating all features for each transaction. The precomputed features are based on machine learning models that identify critical feature combinations for efficient fraud detection.

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Detecting and blocking card testing attacks (CTAs) in financial transactions without introducing excessive latency. The technique involves asynchronously scoring transactions for CTA risk using separate ML models and storing the CTA scores in a cache. When a transaction comes in, the stored CTA scores are retrieved and used to adjust the blocking score thresholds. The transaction is then evaluated against the adjusted thresholds to determine if it's allowed or blocked. This allows efficient CTA detection without adding latency to the charge path.

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Early detection of first party fraud in financial transactions by predicting future fraud instances. The method involves aggregating normalized attributes of multiple activities associated with a user from different sources over time, then feeding those normalized attributes into a machine learning model trained to output the likelihood of a future fraud instance for that user based on normalized attributes of past fraud instances. If the predicted likelihood exceeds a threshold, an alert is sent. This allows detecting potential fraud schemes before they become apparent by identifying users with elevated fraud risk based on patterns in their activity.

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Using machine learning to analyze past transactions to optimize network efficiency, enhance approval rates, mitigate fraud, and enrich customer profiles. The method involves converting groups of transactions into sequences of transaction features, generating tokens based on those sequences, feeding the tokens to an ML model to estimate future transactions, and using those estimates to approve new transactions. The ML model learns to predict transactions based on historical patterns.

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Using graph neural networks to analyze transaction and external data to identify merchants with negative settlement risk in electronic payment processing. The method involves generating a graph data structure with nodes representing merchants, transactions, etc., and edges connecting related nodes. Text representations of transaction and external data are concatenated to generate node embeddings. Risk scores are propagated through the graph to identify merchants with negative settlement risk. This allows proactive risk mitigation and transaction authorization based on graph analysis rather than just transaction parameters.

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Proactive fraud prevention system for payment cards that allows users to define and control rules for their cards. The system automatically creates rules based on transaction history and demographics, allows users to view and modify the rules, and enforces them to allow or restrict payments. It sends alerts when rules are triggered and updates rules in real-time based on user input.

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Detecting fraud in unstructured transaction sets using heuristic algorithms to improve fraud detection in customer service, money laundering, document verification, and account fraud applications. The method involves retrieving unstructured transaction data sets with context, executing heuristic algorithms on the sets with current context to predict future contexts, presenting context-relevant data, and training the algorithms with outcomes to improve.

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Efficient and scalable method for detecting money laundering in financial transactions using dimensionality reduction and clustering techniques. The method involves mapping individuals' financial data into a reduced dimensional space, defining clusters based on features of known money launderers, and flagging new individuals whose mappings fall near those clusters as potentially laundering money. This reduces the computational and data requirements compared to traditional methods for money laundering detection.

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Fraud detection system for table games like casino games that uses statistical analysis to identify suspicious patterns indicative of cheating. The system monitors bet amounts, win/loss, and game progress over time for individual players and tables. It compares calculated probabilities to actual results to detect significant differences that could indicate fraud. The system uses AI trained on past fraudulent games to further analyze the data. By continuously tracking player and table statistics, it can identify patterns that would be difficult to detect by just looking at isolated wins or small amounts.

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Detecting false data entities like fraudsters by consolidating and comparing lists from multiple sources. It ingests data from external sources daily, converts it to a common format, consolidates into a list, compares to previous lists for differences, and updates a global watchlist. This provides a more complete and accurate list of false entities compared to separately checking each source.

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Automated detection of non-corresponding or outlier electronic data using term patterns between term types. The system builds and trains machine learning term embedding models to analyze patterns between term types across different domains, including claims, terms, and context. It then predicts term types based on these patterns, scoring them against actual terms to determine unexpected or outlier claims. This enables automated analysis of large datasets to identify patterns indicative of fraud or anomalies, without requiring manual review or expertise.

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Generating personalized risk profiles using neural networks trained on hash trees. The method involves converting raw event data into a tree structure of Bayesian and Markovian wavelets. It replaces some wavelets with hashes to reduce size. The hash trees are then used to train a deep neural network. Anomaly detection is done by converting new events into wavelets, calculating harmonics, and adding if impact below a threshold. This allows efficient, scalable risk analysis using compact hash trees instead of raw data.

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Improving efficiency of data cleaning in financial crime detection by standardizing transaction time series data into signals for identifying patterns and determining periodicities. The technique involves converting time-based transaction data into frequency-based signals, aligning signals using lag measurements, and visualizing aligned signals as graphs to detect patterns. This allows efficient identification of patterns and periodicities in complex transaction data without manually cleaning each data point.

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A method for developing an intelligent claims submission interview process that dynamically adapts to user responses while preventing fraud. The method employs an artificial intelligence model to generate personalized interview scripts based on user characteristics and claim data patterns. The model continuously evaluates user responses and adjusts the interview script to ensure accurate claim processing, while also detecting and preventing fraudulent attempts. The system dynamically adjusts the interview approach based on user behavior, providing users with tailored questions and interventions to clarify information and prevent gaming the system.

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Automatically detecting illicit actions by users of self-service checkout machines to prevent fraud. The system analyzes video and optionally audio from the checkout area to detect abnormal user actions that indicate theft or other misconduct. It compares the recorded user actions against a normal sequence to flag potential illicit events. This allows real-time detection of checkout machine misuse without continuous video analysis, reducing computational load.

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Securing data in a decentralized environment like a blockchain by detecting and preventing suspicious data interactions that could lead to theft. The system involves using security bots to monitor user data interactions, detect anomalies, and block potential scams. It does this by simulating suspicious interactions, verifying them, and processing only if successful. This avoids scammers detecting and withdrawing their interactions. The bots also learn normal behavior and flag deviations. This allows blocking scam interactions without alerting scammers.

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