Best AI tools for< aml monitoring >

Pascal is an AI-powered risk-based KYC and AML screening and monitoring platform that allows its users to assess findings faster and more accurately than other compliance tools. It uses AI, machine learning, and Natural Language Processing to analyze a range of open-source data and corporate-owned client-specific data to identify and assess risks. Pascal can read, interpret, and structure adverse media in nearly all frequently used languages, making it a valuable tool for compliance professionals.

Flagright is an AML compliance and fraud prevention platform that uses AI to help businesses identify and mitigate financial crime risks. The platform provides a range of features, including real-time transaction monitoring, case management, AI forensics, and customer risk assessment. Flagright is trusted by fintechs, banks, and other businesses around the world to help them stay compliant and protect their customers from financial crime.

SymphonyAI NetReveal Financial Services is an AI-powered platform that offers solutions for financial crime prevention in various industries such as banking, insurance, financial markets, and private banking. The platform utilizes predictive and generative AI applications to enhance efficiency, reduce fraud, streamline compliance, and maximize output. SymphonyAI provides a fundamentally different approach to AI by combining high-value AI capabilities with industry-leading predictive and generative AI technologies. The platform offers a range of solutions including transaction monitoring, customer due diligence, payment fraud detection, and enterprise investigation management. SymphonyAI aims to revolutionize financial crime prevention by leveraging AI to detect suspicious activity, expedite investigations, and improve compliance operations.

Napier AI is an AI-powered Anti-Money Laundering platform designed to combat evolving threats in the financial industry. It offers a suite of intelligent compliance products that aim to transform organizations' attitudes towards compliance by focusing on efficiency and outcomes. The platform integrates multiple compliance solutions into one master dashboard, provides flexible deployment options, and offers AI-enhanced insights to empower compliance teams to make faster and more accurate decisions. Napier AI is trusted by leading data providers and financial organizations worldwide for its innovative approach to financial crime compliance.

Unit21 is a customizable no-code platform designed for risk and compliance operations. It empowers organizations to combat financial crime by providing end-to-end lifecycle risk analysis, fraud prevention, case management, and real-time monitoring solutions. The platform offers features such as AI Copilot for alert prioritization, Ask Your Data for data analysis, Watchlist & Sanctions for ongoing screening, and more. Unit21 focuses on fraud prevention and AML compliance, simplifying operations and accelerating investigations to respond to financial threats effectively and efficiently.

Brighterion AI, a Mastercard company, offers advanced AI solutions for financial institutions, merchants, and healthcare providers. With over 20 years of experience, Brighterion has revolutionized AI by providing market-ready models that enhance customer experience, reduce financial fraud, and mitigate risks. Their solutions are enriched with Mastercard's global network intelligence, ensuring scalability and powerful personalization. Brighterion's AI applications cater to acquirers, PSPs, issuers, and healthcare providers, offering custom AI solutions for transaction fraud monitoring, merchant monitoring, AML & compliance, and healthcare fraud detection. The company has received several prestigious awards for its excellence in AI and financial security.

Shufti Pro is an award-winning global identity verification platform that provides businesses with a suite of tools to verify the identities of their customers. The platform uses artificial intelligence (AI) to automate the identity verification process, making it faster, more accurate, and more secure. Shufti Pro's solutions are used by businesses in a variety of industries, including banking, fintech, crypto, forex, gaming, insurance, education, healthcare, e-commerce, and travel.

Veriff is an AI-powered identity verification platform that helps businesses fight fraud, build trusted digital communities, improve UX, and drive growth. It uses a combination of AI and in-house human verification teams to ensure that bad actors are kept at bay and genuine users experience minimal friction in their customer journey.

DataVisor is a modern, end-to-end fraud and risk SaaS platform powered by AI and advanced machine learning for financial institutions and large organizations. It helps businesses combat various fraud and financial crimes in real time. DataVisor's platform provides comprehensive fraud detection and prevention capabilities, including account onboarding, application fraud, ATO prevention, card fraud, check fraud, FinCrime and AML, and ACH and wire fraud detection. The platform is designed to adapt to new fraud incidents immediately with real-time data signal orchestration and end-to-end workflow automation, minimizing fraud losses and maximizing fraud detection coverage.

Awesome MLOps is a curated list of tools related to Machine Learning Operations, covering areas such as AutoML, CI/CD for Machine Learning, Data Cataloging, Data Enrichment, Data Exploration, Data Management, Data Processing, Data Validation, Data Visualization, Drift Detection, Feature Engineering, Feature Store, Hyperparameter Tuning, Knowledge Sharing, Machine Learning Platforms, Model Fairness and Privacy, Model Interpretability, Model Lifecycle, Model Serving, Model Testing & Validation, Optimization Tools, Simplification Tools, Visual Analysis and Debugging, and Workflow Tools. The repository provides a comprehensive collection of tools and resources for individuals and teams working in the field of MLOps.

A curated list of data science & AI guided projects to start building your portfolio. The repository contains guided projects covering various topics such as large language models, time series analysis, computer vision, natural language processing (NLP), and data science. Each project provides detailed instructions on how to implement specific tasks using different tools and technologies.

ClearML is a suite of tools designed to streamline the machine learning workflow. It includes an experiment manager, MLOps/LLMOps, data management, and model serving capabilities. ClearML is open-source and offers a free tier hosting option. It supports various ML/DL frameworks and integrates with Jupyter Notebook and PyCharm. ClearML provides extensive logging capabilities, including source control info, execution environment, hyper-parameters, and experiment outputs. It also offers automation features, such as remote job execution and pipeline creation. ClearML is designed to be easy to integrate, requiring only two lines of code to add to existing scripts. It aims to improve collaboration, visibility, and data transparency within ML teams.

The Azure-Analytics-and-AI-Engagement repository provides packaged Industry Scenario DREAM Demos with ARM templates (Containing a demo web application, Power BI reports, Synapse resources, AML Notebooks etc.) that can be deployed in a customer’s subscription using the CAPE tool within a matter of few hours. Partners can also deploy DREAM Demos in their own subscriptions using DPoC.

A fine-tuning guide for both OpenAI and Open-Source Large Language Models on Azure. Fine-Tuning retrains an existing pre-trained LLM using example data, resulting in a new 'custom' fine-tuned LLM optimized for task-specific examples. Use cases include improving LLM performance on specific tasks and introducing information not well represented by the base LLM model. Suitable for cases where latency is critical, high accuracy is required, and clear evaluation metrics are available. Learning path includes labs for fine-tuning GPT and Llama2 models via Dashboards and Python SDK.

The Microsoft Health Intelligence Machine Learning Toolbox is a repository that provides low-level and high-level building blocks for Machine Learning / AI researchers and practitioners. It simplifies and streamlines work on deep learning models for healthcare and life sciences by offering tested components such as data loaders, pre-processing tools, deep learning models, and cloud integration utilities. The repository includes two Python packages, 'hi-ml-azure' for helper functions in AzureML, 'hi-ml' for ML components, and 'hi-ml-cpath' for models and workflows related to histopathology images.

Project Miyagi showcases Microsoft's Copilot Stack in an envisioning workshop aimed at designing, developing, and deploying enterprise-grade intelligent apps. By exploring both generative and traditional ML use cases, Miyagi offers an experiential approach to developing AI-infused product experiences that enhance productivity and enable hyper-personalization. Additionally, the workshop introduces traditional software engineers to emerging design patterns in prompt engineering, such as chain-of-thought and retrieval-augmentation, as well as to techniques like vectorization for long-term memory, fine-tuning of OSS models, agent-like orchestration, and plugins or tools for augmenting and grounding LLMs.

InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions. Interpretability is essential for: - Model debugging - Why did my model make this mistake? - Feature Engineering - How can I improve my model? - Detecting fairness issues - Does my model discriminate? - Human-AI cooperation - How can I understand and trust the model's decisions? - Regulatory compliance - Does my model satisfy legal requirements? - High-risk applications - Healthcare, finance, judicial, ...

Jailbreak is a comprehensive guide exploring iOS 17 and its various versions, discussing the benefits, status, possibilities, and future impact of jailbreaking iOS devices. It covers topics such as preparation, safety measures, differences between tethered and untethered jailbreaks, best practices, and FAQs. The guide also provides information on specific jailbreak tools like Palera1n, Serotonin, NekoJB, Redensa, and Dopamine, along with their features and download links. Users can learn about supported devices, the latest updates, and the status of jailbreaking for different iOS versions. The tool aims to empower users to unlock new possibilities and customize their devices beyond Apple's restrictions.

This repository is a compilation of excellent ML and DL tutorials created by various individuals and organizations. It covers a wide range of topics, including machine learning fundamentals, deep learning, computer vision, natural language processing, reinforcement learning, and more. The resources are organized into categories, making it easy to find the information you need. Whether you're a beginner or an experienced practitioner, you're sure to find something valuable in this repository.

Awesome Large Weather Models (LWMs) is a curated collection of articles and resources related to large weather models used in AI for Earth and AI for Science. It includes information on various cutting-edge weather forecasting models, benchmark datasets, and research papers. The repository serves as a hub for researchers and enthusiasts to explore the latest advancements in weather modeling and forecasting.

Cleanlab helps you **clean** data and **lab** els by automatically detecting issues in a ML dataset. To facilitate **machine learning with messy, real-world data** , this data-centric AI package uses your _existing_ models to estimate dataset problems that can be fixed to train even _better_ models.

Cognita is an open-source framework to organize your RAG codebase along with a frontend to play around with different RAG customizations. It provides a simple way to organize your codebase so that it becomes easy to test it locally while also being able to deploy it in a production ready environment. The key issues that arise while productionizing RAG system from a Jupyter Notebook are: 1. **Chunking and Embedding Job** : The chunking and embedding code usually needs to be abstracted out and deployed as a job. Sometimes the job will need to run on a schedule or be trigerred via an event to keep the data updated. 2. **Query Service** : The code that generates the answer from the query needs to be wrapped up in a api server like FastAPI and should be deployed as a service. This service should be able to handle multiple queries at the same time and also autoscale with higher traffic. 3. **LLM / Embedding Model Deployment** : Often times, if we are using open-source models, we load the model in the Jupyter notebook. This will need to be hosted as a separate service in production and model will need to be called as an API. 4. **Vector DB deployment** : Most testing happens on vector DBs in memory or on disk. However, in production, the DBs need to be deployed in a more scalable and reliable way. Cognita makes it really easy to customize and experiment everything about a RAG system and still be able to deploy it in a good way. It also ships with a UI that makes it easier to try out different RAG configurations and see the results in real time. You can use it locally or with/without using any Truefoundry components. However, using Truefoundry components makes it easier to test different models and deploy the system in a scalable way. Cognita allows you to host multiple RAG systems using one app. ### Advantages of using Cognita are: 1. A central reusable repository of parsers, loaders, embedders and retrievers. 2. Ability for non-technical users to play with UI - Upload documents and perform QnA using modules built by the development team. 3. Fully API driven - which allows integration with other systems. > If you use Cognita with Truefoundry AI Gateway, you can get logging, metrics and feedback mechanism for your user queries. ### Features: 1. Support for multiple document retrievers that use `Similarity Search`, `Query Decompostion`, `Document Reranking`, etc 2. Support for SOTA OpenSource embeddings and reranking from `mixedbread-ai` 3. Support for using LLMs using `Ollama` 4. Support for incremental indexing that ingests entire documents in batches (reduces compute burden), keeps track of already indexed documents and prevents re-indexing of those docs.

CoML (formerly MLCopilot) is an interactive coding assistant for data scientists and machine learning developers, empowered on large language models. It offers an out-of-the-box interactive natural language programming interface for data mining and machine learning tasks, integration with Jupyter lab and Jupyter notebook, and a built-in large knowledge base of machine learning to enhance the ability to solve complex tasks. The tool is designed to assist users in coding tasks related to data analysis and machine learning using natural language commands within Jupyter environments.

Specializes in Anti-Money Laundering/Counter-Financing of Terrorism compliance and analysis.

I'm a ML engineer who formats code.