Best AI tools for< Predict Fraud >

Jumio is a leading digital identity verification platform that offers AI-driven services to verify the identities of new and existing users, assess risk, and help meet compliance mandates. With over 1 billion transactions processed, Jumio provides cutting-edge AI and ML models to detect fraud and maintain trust throughout the customer lifecycle. The platform offers solutions for identity verification, predictive fraud insights, dynamic user experiences, and risk scoring, trusted by global brands across various industries.

Socure is a revolutionary digital identity verification and fraud prevention platform that leverages advanced AI/ML technology to provide the most accurate and comprehensive identity verification and fraud prediction solutions. The platform offers a wide range of features including graph-defined identity verification, fraud risk assessment, compliance solutions, account intelligence, decisioning analytics, and reporting. Socure's ID+ platform integrates real-time intelligence from billions of predictions and outcomes to deliver maximum accuracy and eliminate the need for disparate products. With up to 98% auto-approvals across all demographics, Socure helps organizations prevent fraud, streamline compliance, and onboard good customers efficiently.

Obviously AI is a no-code AI tool that allows users to build and deploy machine learning models without writing any code. It is designed to be easy to use, even for those with no data science experience. Obviously AI offers a variety of features, including model building, model deployment, model monitoring, and integration with other tools. It also provides expert support from a dedicated data scientist.

BforeAI is an AI-powered platform that specializes in fighting cyberthreats with intelligence. The platform offers predictive security solutions to prevent phishing, spoofing, impersonation, hijacking, ransomware, online fraud, and data exfiltration. BforeAI uses cutting-edge AI technology for behavioral analysis and predictive results, going beyond reactive blocklists to predict and prevent attacks before they occur. The platform caters to various industries such as financial, manufacturing, retail, and media & entertainment, providing tailored solutions to address unique security challenges.

Kumo is an AI-powered platform that helps businesses personalize customer experiences, acquire new customers, understand customer behavior, improve planning and monitoring, resolve data inconsistencies, fight fraud and abuse, detect money laundering, and empower data scientists with advanced techniques. It offers cutting-edge solutions for various AI and machine learning tasks, such as predictive modeling, anomaly detection, entity resolution, and graph embeddings. Kumo's capabilities are designed to enhance customer interactions, optimize marketing campaigns, and provide valuable insights for businesses across different industries.

Beyond Limits is an industrial-grade, hybrid artificial intelligence company built for the most demanding sectors. Beyond traditional AI, Beyond Limits’ unique Hybrid AI technology combines numeric techniques like machine learning with knowledge-based reasoning to produce actionable intelligence.

Fusemachines is an AI company that offers AI products and solutions to democratize AI. Their AI Studio platform provides various AI engines for different use cases, such as GenAI Engines for predictive analytics and Fraud Detection, Forecast AI for demand forecasting, and more. The company aims to empower enterprises with tailored AI solutions and seamless integration of tools to enhance value across specific industries like Retail, Healthcare, Technology, Banking & Insurance, and Media & Entertainment.

Gradient AI is a leading provider of artificial intelligence solutions for the insurance industry. Their solutions improve loss ratios and profitability by predicting underwriting and claims risks with greater accuracy, as well as reducing quote turnaround times and claim expenses through intelligent automation.

JMIR AI is a new peer-reviewed journal focused on research and applications for the health artificial intelligence (AI) community. It includes contemporary developments as well as historical examples, with an emphasis on sound methodological evaluations of AI techniques and authoritative analyses. It is intended to be the main source of reliable information for health informatics professionals to learn about how AI techniques can be applied and evaluated.

QeDatalab is a leading data science consulting and AI company offering a wide range of services such as software consulting, generative AI consulting, artificial intelligence services, cloud enablement & automation, AI-driven mobile app development, IoT & IIoT data consulting, digital services, AI product development, MLOps consulting, and more. The company specializes in providing AI-powered solutions for industries like healthcare, manufacturing, retail, and education, helping businesses leverage data for informed strategic decision-making and accurate predictions. QeDatalab's team of experts offers end-to-end services, customized solutions, and a trusted partnership to ensure client success.

Gradient AI is a leading provider of artificial intelligence solutions for the insurance industry. Its solutions improve loss ratios and profitability by predicting underwriting and claims risks with greater accuracy, as well as reducing quote turnaround times and claim expenses through intelligent automation.

TAZI is an AI platform that provides explanations to business users on data, models, and results, allowing them to take actions or update data/models based on insights. It offers adaptive business solutions for growth, with features like GenAI AutoML, continuous self-learning, MLOps, security integrations, and solutions tailored for various industries and use cases. TAZI is known for its explainable and adaptive nature, delivering rapid value and scaling efficiently for enterprise-wide collaboration. The platform empowers businesses with AI technology to boost loyalty, predict churn, increase demand and revenue, and stay ahead of fraud.

Eye for AI is a comprehensive AI-powered platform that provides a wide range of tools and resources to help businesses and individuals harness the power of AI. With Eye for AI, users can access cutting-edge AI technologies, including natural language processing, computer vision, and machine learning, to automate tasks, improve decision-making, and gain valuable insights from data.

SAS Blogs is an AI tool that offers a platform for advanced analytics, artificial intelligence, and machine learning. It provides insights and resources on various topics such as customer intelligence, data management, risk management, and programming tips. The platform caters to a wide range of industries including banking, healthcare, manufacturing, and sports. Users can access a wealth of information, articles, and events related to SAS software and applications.

The Predict API is a powerful tool that allows you to forecast your data with simplicity and accuracy. It uses the latest advancements in stochastic modeling and machine learning to provide you with reliable projections. The API is easy to use and can be integrated with any application. It is also highly scalable, so you can use it to forecast large datasets. With the Predict API, you can gain valuable insights into your data and make better decisions.

AI Baby Generator is an AI application that predicts what your future child will look like based on parent photos and features. It uses advanced AI technology to generate hyper-realistic baby photos with a high facial match rate. The application offers different packages with varying features, including personalized baby photos at different ages, personality descriptions, and high-quality images delivered within 24 hours. Users can upload photos of themselves or celebrities to see what their future baby might look like. The service is for entertainment purposes only and does not provide medical or predictive information.

Numerai is a data science tournament platform where users can compete to build models that predict the stock market. The platform provides users with clean and regularized hedge fund quality data, and users can build models using Python or R scripts. Numerai also has a cryptocurrency, NMR, which users can stake on their models to earn rewards.

Neurons is a platform that uses AI to predict consumer responses and behavior. It offers a variety of solutions for businesses, including marketing agencies, designers, and e-commerce companies. Neurons' AI-powered tools can help businesses optimize their marketing campaigns, improve their product design, and better understand their customers.

Heatseeker is an AI-powered market experimentation tool that helps businesses predict customer preferences, conduct feature tests, and generate value propositions. It enables users to answer critical growth questions about market, audience, and product features through AI-powered experiments. Heatseeker provides insights into market trends, competitor analysis, and helps in making data-driven decisions. The platform offers curated recommendations, competitive intelligence, and continuous testing for refining strategies. It automates ad campaign generation, data collection, and provides recommendations for launching new products. Heatseeker is designed to help businesses optimize their marketing efforts and improve their product offerings.

ClosedLoop is a healthcare data science platform that helps organizations improve outcomes and reduce costs by providing accurate, explainable, and actionable predictions of individual-level health risks. The platform offers predictive analytics for various healthcare sectors, data science automation, and a healthcare content library to accelerate time to value. ClosedLoop's AI/ML platform is designed exclusively for the data science needs of modern healthcare organizations, enabling proactive interventions, improved clinical outcomes, and innovative healthcare offerings.

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, ...

Awesome AI is a curated list of artificial intelligence resources including courses, tools, apps, and open-source projects. It covers a wide range of topics such as machine learning, deep learning, natural language processing, robotics, conversational interfaces, data science, and more. The repository serves as a comprehensive guide for individuals interested in exploring the field of artificial intelligence and its applications across various domains.

Intel® Extension for Transformers is an innovative toolkit designed to accelerate GenAI/LLM everywhere with the optimal performance of Transformer-based models on various Intel platforms, including Intel Gaudi2, Intel CPU, and Intel GPU. The toolkit provides the below key features and examples: * Seamless user experience of model compressions on Transformer-based models by extending [Hugging Face transformers](https://github.com/huggingface/transformers) APIs and leveraging [Intel® Neural Compressor](https://github.com/intel/neural-compressor) * Advanced software optimizations and unique compression-aware runtime (released with NeurIPS 2022's paper [Fast Distilbert on CPUs](https://arxiv.org/abs/2211.07715) and [QuaLA-MiniLM: a Quantized Length Adaptive MiniLM](https://arxiv.org/abs/2210.17114), and NeurIPS 2021's paper [Prune Once for All: Sparse Pre-Trained Language Models](https://arxiv.org/abs/2111.05754)) * Optimized Transformer-based model packages such as [Stable Diffusion](examples/huggingface/pytorch/text-to-image/deployment/stable_diffusion), [GPT-J-6B](examples/huggingface/pytorch/text-generation/deployment), [GPT-NEOX](examples/huggingface/pytorch/language-modeling/quantization#2-validated-model-list), [BLOOM-176B](examples/huggingface/pytorch/language-modeling/inference#BLOOM-176B), [T5](examples/huggingface/pytorch/summarization/quantization#2-validated-model-list), [Flan-T5](examples/huggingface/pytorch/summarization/quantization#2-validated-model-list), and end-to-end workflows such as [SetFit-based text classification](docs/tutorials/pytorch/text-classification/SetFit_model_compression_AGNews.ipynb) and [document level sentiment analysis (DLSA)](workflows/dlsa) * [NeuralChat](intel_extension_for_transformers/neural_chat), a customizable chatbot framework to create your own chatbot within minutes by leveraging a rich set of [plugins](https://github.com/intel/intel-extension-for-transformers/blob/main/intel_extension_for_transformers/neural_chat/docs/advanced_features.md) such as [Knowledge Retrieval](./intel_extension_for_transformers/neural_chat/pipeline/plugins/retrieval/README.md), [Speech Interaction](./intel_extension_for_transformers/neural_chat/pipeline/plugins/audio/README.md), [Query Caching](./intel_extension_for_transformers/neural_chat/pipeline/plugins/caching/README.md), and [Security Guardrail](./intel_extension_for_transformers/neural_chat/pipeline/plugins/security/README.md). This framework supports Intel Gaudi2/CPU/GPU. * [Inference](https://github.com/intel/neural-speed/tree/main) of Large Language Model (LLM) in pure C/C++ with weight-only quantization kernels for Intel CPU and Intel GPU (TBD), supporting [GPT-NEOX](https://github.com/intel/neural-speed/tree/main/neural_speed/models/gptneox), [LLAMA](https://github.com/intel/neural-speed/tree/main/neural_speed/models/llama), [MPT](https://github.com/intel/neural-speed/tree/main/neural_speed/models/mpt), [FALCON](https://github.com/intel/neural-speed/tree/main/neural_speed/models/falcon), [BLOOM-7B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/bloom), [OPT](https://github.com/intel/neural-speed/tree/main/neural_speed/models/opt), [ChatGLM2-6B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/chatglm), [GPT-J-6B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/gptj), and [Dolly-v2-3B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/gptneox). Support AMX, VNNI, AVX512F and AVX2 instruction set. We've boosted the performance of Intel CPUs, with a particular focus on the 4th generation Intel Xeon Scalable processor, codenamed [Sapphire Rapids](https://www.intel.com/content/www/us/en/products/docs/processors/xeon-accelerated/4th-gen-xeon-scalable-processors.html).

This repository contains custom recipes for H2O Driverless AI, which is an Automatic Machine Learning platform for the Enterprise. Custom recipes are Python code snippets that can be uploaded into Driverless AI at runtime to automate feature engineering, model building, visualization, and interpretability. Users can gain control over the optimization choices made by Driverless AI by providing their own custom recipes. The repository includes recipes for various tasks such as data manipulation, data preprocessing, feature selection, data augmentation, model building, scoring, and more. Best practices for creating and using recipes are also provided, including security considerations, performance tips, and safety measures.

This repository contains a comprehensive collection of 63 must-know Large Language Models (LLMs) interview questions. It covers topics such as the architecture of LLMs, transformer models, attention mechanisms, training processes, encoder-decoder frameworks, differences between LLMs and traditional statistical language models, handling context and long-term dependencies, transformers for parallelization, applications of LLMs, sentiment analysis, language translation, conversation AI, chatbots, and more. The readme provides detailed explanations, code examples, and insights into utilizing LLMs for various tasks.

The repository 'Awesome-LLM4Cybersecurity' provides a comprehensive overview of the applications of Large Language Models (LLMs) in cybersecurity. It includes a systematic literature review covering topics such as constructing cybersecurity-oriented domain LLMs, potential applications of LLMs in cybersecurity, and research directions in the field. The repository analyzes various benchmarks, datasets, and applications of LLMs in cybersecurity tasks like threat intelligence, fuzzing, vulnerabilities detection, insecure code generation, program repair, anomaly detection, and LLM-assisted attacks.

This repository contains a collection of AI projects covering various areas of machine learning. Each project is accompanied by detailed articles on the associated blog sciblog. Projects range from introductory topics like Convolutional Neural Networks and Transfer Learning to advanced topics like Fraud Detection and Recommendation Systems. The repository also includes tutorials on data generation, distributed training, natural language processing, and time series forecasting. Additionally, it features visualization projects such as football match visualization using Datashader.

PIXIU is a project designed to support the development, fine-tuning, and evaluation of Large Language Models (LLMs) in the financial domain. It includes components like FinBen, a Financial Language Understanding and Prediction Evaluation Benchmark, FIT, a Financial Instruction Dataset, and FinMA, a Financial Large Language Model. The project provides open resources, multi-task and multi-modal financial data, and diverse financial tasks for training and evaluation. It aims to encourage open research and transparency in the financial NLP field.

The 'awesome-AI4MolConformation-MD' repository focuses on protein conformations and molecular dynamics using generative artificial intelligence and deep learning. It provides resources, reviews, datasets, packages, and tools related to AI-driven molecular dynamics simulations. The repository covers a wide range of topics such as neural networks potentials, force fields, AI engines/frameworks, trajectory analysis, visualization tools, and various AI-based models for protein conformational sampling. It serves as a comprehensive guide for researchers and practitioners interested in leveraging AI for studying molecular structures and dynamics.

Chronon is a platform that simplifies and improves ML workflows by providing a central place to define features, ensuring point-in-time correctness for backfills, simplifying orchestration for batch and streaming pipelines, offering easy endpoints for feature fetching, and guaranteeing and measuring consistency. It offers benefits over other approaches by enabling the use of a broad set of data for training, handling large aggregations and other computationally intensive transformations, and abstracting away the infrastructure complexity of data plumbing.

Ludwig is a declarative deep learning framework designed for scale and efficiency. It is a low-code framework that allows users to build custom AI models like LLMs and other deep neural networks with ease. Ludwig offers features such as optimized scale and efficiency, expert level control, modularity, and extensibility. It is engineered for production with prebuilt Docker containers, support for running with Ray on Kubernetes, and the ability to export models to Torchscript and Triton. Ludwig is hosted by the Linux Foundation AI & Data.

The LLM course is divided into three parts: 1. 🧩 **LLM Fundamentals** covers essential knowledge about mathematics, Python, and neural networks. 2. 🧑‍🔬 **The LLM Scientist** focuses on building the best possible LLMs using the latest techniques. 3. 👷 **The LLM Engineer** focuses on creating LLM-based applications and deploying them. For an interactive version of this course, I created two **LLM assistants** that will answer questions and test your knowledge in a personalized way: * 🤗 **HuggingChat Assistant**: Free version using Mixtral-8x7B. * 🤖 **ChatGPT Assistant**: Requires a premium account. ## 📝 Notebooks A list of notebooks and articles related to large language models. ### Tools | Notebook | Description | Notebook | |----------|-------------|----------| | 🧐 LLM AutoEval | Automatically evaluate your LLMs using RunPod |  | | 🥱 LazyMergekit | Easily merge models using MergeKit in one click. |  | | 🦎 LazyAxolotl | Fine-tune models in the cloud using Axolotl in one click. |  | | ⚡ AutoQuant | Quantize LLMs in GGUF, GPTQ, EXL2, AWQ, and HQQ formats in one click. |  | | 🌳 Model Family Tree | Visualize the family tree of merged models. |  | | 🚀 ZeroSpace | Automatically create a Gradio chat interface using a free ZeroGPU. |  |

This tool is a machine learning AI used to predict the winners and under/overs of NBA games. It takes all team data from the 2007-08 season to the current season, matched with odds of those games, and uses a neural network to predict winning bets for today's games. The tool achieves ~69% accuracy on money lines and ~55% on under/overs. It outputs expected value for teams' money lines to provide better insight and the fraction of your bankroll to bet based on the Kelly Criterion. A popular, less risky approach is to bet 50% of the stake recommended by the Kelly Criterion.

FootBallPrediction (FBP) is a software project that utilizes big data and machine learning to predict the outcome of football matches based on odds from gambling companies. The software has achieved an accuracy rate of over 80% in predicting match results. The current version, 22.0, successfully predicted eight out of nine matches from major football leagues. The project has a community of over 60 members who benefit from the predicted results. The author is seeking collaboration to further enhance the project and welcomes interested individuals to join. AI-FBP is a subscription service that provides daily football game predictions.

WeatherGFT is a physics-AI hybrid model designed to generalize weather forecasts to finer-grained temporal scales beyond the training dataset. It incorporates physical partial differential equations (PDEs) into neural networks to simulate fine-grained physical evolution and correct biases. The model achieves state-of-the-art performance in forecasting tasks at different time scales, from nowcasting to medium-range forecasts, by utilizing a lead time-aware training framework and a carefully designed PDE kernel. WeatherGFT bridges the gap between nowcast and medium-range forecast by extending forecasting abilities to predict accurately at a 30-minute time scale.

This repo contains official implementations of **Coach AI Badminton Project** from Advanced Database System Laboratory, National Yang Ming Chiao Tung University supervised by Prof. Wen-Chih Peng. The high-level concepts of each project are as follows: 1. Visualization Platform published at _Physical Education Journal 2020_ aims to construct a platform that can be used to illustrate the data from matches. 2. Shot Influence and Extension Work published at _ICDM-21_ and _ACM TIST 2022_ , respectively introduce a framework with a shot encoder, a pattern extractor, and a rally encoder to capture long short-term dependencies for evaluating players' performance of each shot. 3. Stroke Forecasting published at _AAAI-22_ proposes the first stroke forecasting task to predict the future strokes of both players based on the given strokes by ShuttleNet, a position-aware fusion of rally progress and player styles framework. 4. Strategic Environment published at _AAAI-23 Student Abstract_ designs a safe and reproducible badminton environment for turn-based sports, which simulates rallies with different angles of view and designs the states, actions, and training procedures. 5. Movement Forecasting published at _AAAI-23_ proposes the first movement forecasting task, which contains not only the goal of stroke forecasting but also the movement of players, by DyMF, a novel dynamic graphs and hierarchical fusion model based on the proposed player movements (PM) graphs. 6. CoachAI-Challenge-IJCAI2023 is a badminton challenge (CC4) hosted at _IJCAI-23_. Please find the website for more details. 7. ShuttleSet published at _KDD-23_ is the largest badminton singles dataset with stroke-level records. - An extension dataset ShuttleSet22 published at _IJCAI-24 Demo & IJCAI-23 IT4PSS Workshop_ is also released. 8. CoachAI Badminton Environment published at _AAAI-24 Student Abstract and Demo, DSAI4Sports @ KDD 2023_ is a reinforcement learning (RL) environment tailored for AI-driven sports analytics, offering: i) Realistic opponent simulation for RL training; ii) Visualizations for evaluation; and iii) Performance benchmarks for assessing agent capabilities.

This is the official Clarifai gRPC Python client for interacting with their recognition API. Clarifai offers a platform for data scientists, developers, researchers, and enterprises to utilize artificial intelligence for image, video, and text analysis through computer vision and natural language processing. The client allows users to authenticate, predict concepts in images, and access various functionalities provided by the Clarifai API. It follows a versioning scheme that aligns with the backend API updates and includes specific instructions for installation and troubleshooting. Users can explore the Clarifai demo, sign up for an account, and refer to the documentation for detailed information.

A master detective GPT, adept in analysis, deduction, and intuitive problem-solving.

The most comprehensive guide to ML theory and applications.

I predict DAU based on growth and retention.

Predict the future, opine on politics and controversial topics, and have GPT assess what is "true"

I predict future stock market prices. AI analyst. Your trading analysis assistant. Press H to bring up prompt hot key menu. Not financial advice.

Analyze Financial Statements step by step to Predict Earnings Direction

Predict fashion trends

AI expert in analyzing historical patterns to predict future events

Does Pool Performance of Elite Triathletes Predict Open-Water Performance?

A moot competition companion. & Trial Prep companion . Test and improve arguments- predict your opponent's reaction.

Personalized, analytical sports prediction AI.

あなたの顔を予想します

Predicts gender for first names using data

Our college entrance exam prediction app uses advanced algorithms and data analysis to provide accurate predictions for students preparing to take their college entrance exams.

Prédictif des tendances crypto à partir de la presse et des réseaux sociaux

Horoscopes--八字算命术预测你的健康、婚姻、事业、财富...

Analyzes and predicts the SSE 500 Index.

1620年代の主要な歴史的出来事を分析し未来の予測に役立てる。

A question ? Ask me for a prediction ... ( /help for help )

MMA Expert & Trainer