govCDOiq’s Post

💻 Calling All Technical Problem-Solvers: Help Build a Platform for Justice We are building a cutting-edge platform to combat systemic corruption and collusion in the Canary Islands. Politically exposed individuals (PEPs) with ties to powerful networks have exploited judicial systems, misused public funds, and silenced whistleblowers. Now, we are developing the technology to expose the truth and protect the integrity of justice. --- The Technical Challenge We are tackling a complex data management and analytics problem, requiring an end-to-end solution that is secure, automated, and scalable: 1. Data Integration: Centralize over 100GB of diverse data (emails, financial records, legal documents) from scattered sources into a searchable repository. Tag and index evidence for fast categorization and retrieval. 2. AI-Powered Automation: Use advanced machine learning models to: Summarize large volumes of unstructured data. Extract key patterns, links, and insights from complex datasets. Generate legal drafts and investigative reports automatically. 3. Real-Time Query Capabilities: Build a natural language search engine to allow investigators to ask questions and retrieve actionable insights in seconds. Ensure seamless keyword tagging and advanced filtering for precision. 4. Security and Compliance: Protect sensitive evidence with end-to-end encryption, role-based access controls, and activity logging. Align the platform with Directive (EU) 2019/1937 to ensure whistleblower protections. 5. Transparency Tools: Design interactive dashboards for sharing findings with investigators, legal teams, and media. Present evidence in ways that are accessible and impactful for public transparency. --- Who We’re Looking For We need innovators and technical experts with experience in: Data Engineering: Designing and optimizing data pipelines for large-scale integration. Artificial Intelligence: Building NLP-based models for summarization, pattern recognition, and query optimization. Cloud Infrastructure: Creating secure, scalable architectures for sensitive data. Cybersecurity: Implementing advanced encryption and access control measures. --- Why This Matters This is a once-in-a-generation opportunity to apply technology to solve a systemic problem: Protect hundreds of millions in public funds from misuse. Defend whistleblowers who are exposing the truth. Enable legal teams and investigators to uncover fraud faster and more efficiently. If you have the expertise—or know someone who does—join us in building a platform that combines the best of technology and justice. 📩 Let’s connect and make an impact together. --- #AI #DataEngineering #Cybersecurity #JusticeTech #Transparency

You can't monitor or screen transactions w/out __ You can't conduct investigations w/out __ You can't risk rate customers w/out __ You can't conduct the risk assessment w/out __ You can't report to the board w/out __ You can't do a staffing or capacity analysis w/out __ In Financial Crime Risk and Compliance you pretty much can't do anything (correct at least) w/out __ __ DATA If you're building tech in the FinCrime space you better understand and have a strategy for __ DATA If you're running a FinCrime program you better understand the ecosystem and have a strategy for __ DATA If you've made it this far in the post and you're saying “duh, or obviously Vic” — you do not have a good pulse on the reality of this industry: 1/ We continue to lack good fundamentals around data 2/ We rarely have any real strategy in place for data in general (let alone exploitation) 3/ We endlessly talk about AI/ML w/out addressing the data problem DATA is how you unlock the utilities of advanced tech and DATA is how you drive a good program.

Task 4 #Digital empowerment pakistan Enhancing Financial Security through Credit Card Fraud Detection Description: In an era of digital transactions, ensuring the security of credit card operations is paramount. Our latest project focuses on developing a robust system for detecting fraudulent credit card transactions, leveraging advanced data analytics and machine learning techniques. Objectives: Develop a predictive model to detect fraudulent credit card transactions. Minimize false positives to avoid unnecessary disruptions for legitimate transactions. Enhance the security measures of financial institutions. Methodology: Data Collection: Sources: Historical transaction data from financial institutions. Data Points: Transaction ID, timestamp, transaction amount, merchant details, geographical location, user details, transaction type. Data Preprocessing: Cleaning and normalizing data. Handling missing values and outliers. Encoding categorical variables. Balancing the dataset to address class imbalance between fraudulent and non-fraudulent transactions. Exploratory Data Analysis (EDA): Analyzing transaction patterns and distributions. Identifying correlations between variables. Visualizing data to uncover insights and trends. Feature Engineering: Creating new features based on transaction history and patterns. Aggregating transaction data over time windows. Extracting behavioral patterns of users. Model Selection and Training: Algorithms: Logistic Regression, Decision Trees, Random Forest, Gradient Boosting Machines (GBM), Neural Networks. Validation Techniques: Cross-validation, train-test split, and hyperparameter tuning. Tools: Scikit-learn, XGBoost, TensorFlow, Keras. Model Evaluation: Metrics: Precision, Recall, F1 Score, Area Under the ROC Curve (AUC-ROC). Evaluating model performance and selecting the best-performing model. Anomaly Detection: Implementing techniques to detect anomalous transactions. Using clustering algorithms (e.g., DBSCAN, Isolation Forest) and statistical methods. Combining anomaly detection with supervised learning models for improved accuracy. Deployment: Integrating the model into the transaction processing system. Real-time monitoring and alerting for suspected fraudulent transactions. Providing a user-friendly interface for analysts to review flagged transactions. Results and Impact: Significant reduction in fraudulent transactions. Improved trust and security for customers. Enhanced reputation and reliability of financial institutions. Key Findings: Identification of common patterns in fraudulent transactions. Insights into user behavior and transaction anomalies. Effective use of machine learning techniques to detect and prevent fraud. Future Directions: Continuous model refinement with new transaction data. Hashtags: #FraudDetection #CreditCardSecurity #MachineLearning #DataScience #FinancialSecurity #AnomalyDetection #ArtificialIntelligence

**Language of adversarial events** From the knowledge gained from #llms or large language models, if we think about a system that can be exploited to cause adversarial events, an attempt can be made to decode these adversarial events. Let us start by the #data. For every #interaction with the #system, a datapoint can be added to indicate all of the steps that are taken in that interaction. You do not worry about recording the exact content of the step, just an indicator say #callback 232 was followed by #callback 342 where #callbacks indicates #callback of a button click or a menu choice or a mouse click, etc. We are going to convert these to tokens anyway loosing any extra information. This way every #data_point is a #sequence of #callbacks or sub-interactions with the system. Let us assume that for the training data we have classifications available for various adversarial or non-adversarial events. A #decoder_only #transformer can be created to work with tokens that are #tokenizing the #callbacks as mentioned above. The #head that is placed on top of the decoder-only transformer can be a #linear_layer of #hidden_size mapping to #hidden_size, followed by another #linear_layer mapping from #hidden_size to #event_classes, where #hidden_size would be the parameter of the #classifier which can be increased depending on the complexity of the system, the #event_classes on the other hand as name indicates is the size of the set of all #event classes. This model can be trained on the dataset mentioned above. The outputs can be treated as your standard #logits distributing over the #event_classes. I think this would work for any web-application, SIEM, government system that keep tracks of all its subfunctions, transactions in a banking procedure or app, security monitoring systems, etc. P.S.: As mentioned above, I merely tried to imagine an application of decoder-only models to domains outside large language models. I don't know how this can work, this requires experimentation. But I welcome any and all criticism. Thanks!

Boundless_Informant Boundless Informant (stylized as BOUNDLESSINFORMANT) is a big data analysis and data visualization tool used by the United States National Security Agency (NSA). It gives NSA managers summaries of the NSA's worldwide data collection activities by counting metadata. The existence of this tool was disclosed by documents leaked by Edward Snowden, who worked at the NSA for the defense contractor Booz Allen Hamilton.  Those disclosed documents were in a direct contradiction to the NSA's assurance to United States Congress that it does not collect any type of data on millions of Americans. Intelligence gathered by the United States government inside the United States or specifically targeting US citizens is legally required to be gathered in compliance with the Foreign Intelligence Surveillance Act of 1978 (FISA) and under the authority of the Foreign Intelligence Surveillance Court (FISA court). NSA global data mining projects have existed for decades, but recent programs of intelligence gathering and analysis that include data gathered from inside the United States such as PRISM were enabled by changes to US surveillance law introduced under President Bush and renewed under President Obama in December 2012. Boundless Informant was first publicly revealed on June 8, 2013, after classified documents about the program were leaked to The Guardian. This report contained a Top Secret heat map produced by the Boundless Informant program summarizing data records from 504 separate DNR and DNI collection sources or SIGADs. In the map, countries that are under surveillance are assigned a color from green to red (which does not correspond to intensity of surveillance) https://lnkd.in/dC7-mf9k

Data Preprocessing for Fraud Detection: Tackling Imbalanced Datasets In fraud detection, one of the key challenges is dealing with imbalanced datasets, where fraudulent transactions are vastly outnumbered by legitimate ones. This imbalance can lead to biased models that overlook fraud. Ensuring models effectively detect fraud requires specialized data preprocessing techniques to handle these imbalances. Key Techniques for Handling Imbalanced Datasets: 🔹 Resampling Techniques Oversampling the Minority Class (e.g., SMOTE) to generate synthetic fraud samples. Undersampling the Majority Class by reducing the number of legitimate transactions. 🔹 Anomaly Detection Algorithms like Isolation Forest and One-Class SVM are great for detecting anomalies (fraud) within large datasets of legitimate transactions. 🔹 Cost-Sensitive Learning Modifying the algorithm to penalize misclassifications of fraud more heavily, improving sensitivity towards fraudulent activity. 🔹 Ensemble Methods Random Forest and XGBoost are popular ensemble methods that can help identify hard-to-predict fraud cases by combining multiple weak models. 🔹 Data Augmentation Using GANs (Generative Adversarial Networks) to generate synthetic fraud data, helping to balance the dataset and improve detection rates. 🔹 Feature Engineering Leveraging features like transaction frequency and location to better distinguish fraud from legitimate transactions. Why It Matters: Properly addressing data imbalance helps to reduce false negatives (missed fraud), improving the overall performance of fraud detection systems. By applying these techniques, financial institutions can better protect themselves and their customers from financial fraud. In a world of evolving fraud tactics, continuous adaptation of preprocessing methods and models is crucial to staying ahead of fraudulent activities. #FraudDetection #MachineLearning #DataScience #ImbalancedDatasets #DataPreprocessing #AI #FinTech

Financial crime is advancing faster than ever, with schemes growing in complexity and scope. Isolated efforts are no longer enough to combat these global threats. The stakes are higher than ever, requiring financial institutions and regulators to work together like never before. Advances in technology are making this collaboration not just possible but transformative. Take homomorphic encryption, for instance—an innovative tool that allows organisations to securely analyse shared data while maintaining strict privacy. By leveraging technologies like this, institutions can uncover critical global patterns, respond to emerging threats, and address complex regulatory demands—all without compromising confidentiality. Collaboration isn’t optional—it’s essential. By working together, we can build an agile, united front to tackle financial crime at its roots. 🌟 As financial crime becomes increasingly global, what partnerships or technologies do you believe will shape the future of detection and prevention? Share your thoughts below! #FinancialCrime #DataQuality #DataAnalytics #MachineLearning #DataScience #SRE #Innovation #DataEngineering #AI #Bigdata #Bigspark

In this insightful piece by Wee Tee Lim on @EconomicTimes, we explore how data serves as the foundational bedrock upon which robust fraud detection mechanisms are built. From analyzing vast troves of data to uncovering intricate patterns, the role of data in combating fraud is undeniable. At the heart of this discussion lies www.fraudgraph.ai, a cutting-edge platform that epitomizes the fusion of innovation and security. By leveraging advanced AI algorithms and harnessing the power of data analytics, FraudGraph.ai empowers organizations to stay ahead in the battle against fraudsters. Join the conversation on #FraudDetection, #AI, and #DataStrategy as we explore how these elements converge to safeguard businesses and consumers alike. Dive deep into the world of fraud prevention with www.fraudgraph.ai and stay one step ahead in today's ever-evolving digital landscape. #Security #Innovation #DataAnalytics https://lnkd.in/eAn-xT74

Midjourney bans all Stability AI employees over alleged data scraping #adviceguru #blog https://lnkd.in/gpCtEcFn #adviceguru #alleged #bans #data #employees #midjourney #scraping #stability #technologyblog

📋 3 in 100 roles in financial services remain unfilled, according to the Future Skills 2024 report from the Financial Services Skills Commission. #Data, software, #cyber and #risk roles were cited as the hardest to fill, while the research also identified a substantial supply-demand gap for #machinelearning and #AI skills. With data from the Unit for Future Skills highlighting that #financialservices is set to be more exposed to AI than other sectors, finding ways to bridge this skills gap and put in place the appropriate #training and #reskilling programmes for those who build, implement and use the technology is more important than ever.  Download the full report here 👉 https://lnkd.in/dSrbq8TH