Best AI tools for< Reduce Fraud Cases >

Autohost is an AI-powered guest screening and verification platform designed for the hospitality industry. It offers a comprehensive solution to streamline the guest screening process, enhance security, and improve the overall guest experience. By leveraging automated systems and advanced technology, Autohost helps hosts in the hospitality and event industry to reduce risks and ensure a safe environment for their guests.

ThetaRay is an AI-powered transaction monitoring platform designed for fintechs and banks to detect threats and ensure trust in global payments. It uses unsupervised machine learning to efficiently detect anomalies in data sets and pinpoint suspected cases of money laundering with minimal false positives. The platform helps businesses satisfy regulators, save time and money, and drive financial growth by identifying risks accurately, boosting efficiency, and reducing false positives.

Fraud.net is an AI-powered fraud detection and prevention platform designed for enterprises. It offers a comprehensive and customizable solution to manage and prevent various types of fraud, such as account takeover, application fraud, and payment fraud. The platform utilizes AI and machine learning technologies to provide real-time monitoring, analytics, and reporting, helping businesses reduce fraud losses and improve customer trust. Fraud.net is trusted by various industries, including financial services, e-commerce, gaming, government, and insurance, to combat fraud schemes and ensure secure transactions.

Sardine is an AI-powered platform for fraud prevention and compliance. It offers a comprehensive suite of products to help banks, retailers, and fintechs detect fraud patterns, prevent money laundering, and stop sophisticated scams. Sardine combines deep device intelligence, behavior biometrics, and identity signals to provide a precise risk score for every customer interaction. The platform also features machine learning models, a rules engine, network graph analysis, anomaly detection, and generative AI capabilities to fight modern threats. Sardine helps reduce fraud rates, decrease false positives, and streamline risk operations with its fully integrated solutions.

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.

Trust Stamp is a global provider of AI-powered identity services offering a full suite of identity tools, including biometric multi-factor authentication, document validation, identity validation, duplicate detection, and geolocation services. The application is designed to empower organizations across various sectors with advanced biometric identity solutions to reduce fraud, protect personal data privacy, increase operational efficiency, and reach a broader user base worldwide through unique data transformation and comparison capabilities. Founded in 2016, Trust Stamp has achieved significant milestones in net sales, gross profit, and strategic partnerships, positioning itself as a leader in the identity verification industry.

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.

Nametag is an identity verification solution designed specifically for IT helpdesks. It helps businesses prevent social engineering attacks, account takeovers, and data breaches by verifying the identity of users at critical moments, such as password resets, MFA resets, and high-risk transactions. Nametag's unique approach to identity verification combines mobile cryptography, device telemetry, and proprietary AI models to provide unmatched security and better user experiences.

LogicLoop is an all-in-one operations automation platform that allows users to set up alerts and automations on top of their data. It is designed to help businesses monitor their operations, identify risks, and take action to prevent problems. LogicLoop can be used by businesses of all sizes and industries, and it is particularly well-suited for businesses that are looking to improve their efficiency and reduce their risk.

AppZen is an AI-powered application designed for modern finance teams to streamline accounts payable processes, automate invoice and expense auditing, and improve compliance. It offers features such as Autonomous AP for invoice automation, Expense Audit for T&E spend management, and Card Audit for analyzing card spend. AppZen's AI learns and understands business practices, ensures compliance, and integrates with existing systems easily. The application helps prevent duplicate spend, fraud, and FCPA violations, making it a valuable tool for finance professionals.

MindCommerce is a leading provider of AI-powered solutions for the retail industry. Our mission is to help retailers unlock the power of AI to improve their customer experience, increase sales, and reduce costs. We offer a range of AI-powered solutions, including personalized recommendations, automated customer service, and fraud detection.

AizenFlow is a sales and automation platform designed to simplify supply chains and facilitate relationships between small and medium-sized 3PLs, shippers, and carriers across the US, Mexico, and Canada. It offers advanced features such as carrier identity verification, AI-powered semantic search, and fraud prevention to streamline freight operations and ensure compliance. AizenFlow aims to help businesses in the freight industry reduce risk, increase efficiency, and protect their operations from fraud.

Pongo is an AI-powered tool that helps reduce hallucinations in Large Language Models (LLMs) by up to 80%. It utilizes multiple state-of-the-art semantic similarity models and a proprietary ranking algorithm to ensure accurate and relevant search results. Pongo integrates seamlessly with existing pipelines, whether using a vector database or Elasticsearch, and processes top search results to deliver refined and reliable information. Its distributed architecture ensures consistent latency, handling a wide range of requests without compromising speed. Pongo prioritizes data security, operating at runtime with zero data retention and no data leaving its secure AWS VPC.

DailyBot is an AI-powered toolkit for teams that want automation, better reporting, and customization. It offers a range of features to enhance team visibility, reduce meetings, and improve collaboration. With DailyBot, teams can run asynchronous standups, retros, and other meetings, send kudos and recognition, create surveys and collect data, and access a variety of add-ons like watercoolers and random coffees. DailyBot also integrates with popular tools like Zapier, Jira, and Trello, making it easy to connect with the tools teams already use. Trusted by leading companies and backed by Y Combinator, DailyBot is a valuable tool for teams looking to improve their collaboration and productivity.

SentinelOne is an advanced enterprise cybersecurity AI platform that offers a comprehensive suite of AI-powered security solutions for endpoint, cloud, and identity protection. The platform leverages AI technology to anticipate threats, manage vulnerabilities, and protect resources across the enterprise ecosystem. SentinelOne provides real-time threat hunting, managed services, and actionable insights through its unified data lake, empowering security teams to respond effectively to cyber threats. With a focus on automation, efficiency, and value maximization, SentinelOne is a trusted cybersecurity solution for leading enterprises worldwide.

Codimite is an AI-assisted offshore development company that provides a range of services to help businesses accelerate their software development, reduce costs, and drive innovation. Codimite's team of experienced engineers and project managers use AI-powered tools and technologies to deliver exceptional results for their clients. The company's services include AI-assisted software development, cloud modernization, and data and artificial intelligence solutions.

SentinelOne is an advanced enterprise cybersecurity AI platform that offers a comprehensive suite of AI-powered security solutions for endpoint, cloud, and identity protection. The platform leverages artificial intelligence to anticipate threats, manage vulnerabilities, and protect resources across the entire enterprise ecosystem. With features such as Singularity XDR, Purple AI, and AI-SIEM, SentinelOne empowers security teams to detect and respond to cyber threats in real-time. The platform is trusted by leading enterprises worldwide and has received industry recognition for its innovative approach to cybersecurity.

SnapMeasureAI is an AI-powered application that provides 99% accurate body measurements without the need to visit a tailor. It uses advanced AI technology to analyze body shapes and measurements from photos or videos, offering unparalleled precision and reliability. The application aims to reduce returns, save costs, and increase shopping confidence by ensuring a perfect fit for users. SnapMeasureAI is designed to accommodate any body type, skin tone, pose, or background, making it a versatile and user-friendly tool for personalized body measurements.

Video Highlight is an AI-powered tool that helps you summarize and take notes from videos. It uses the latest AI technology to generate timestamped summaries and transcripts, highlight key moments, and engage in interactive chats. With Video Highlight, you can save hours of research time and focus on exploring, analyzing, and absorbing content.

Hypergro is an AI-powered platform that specializes in UGC video ads for smart customer acquisition. Leveraging the 4th Generation of AI-powered growth marketing on Meta and Youtube, Hypergro helps businesses discover their audience, drive sales, and increase revenue through real-time AI insights. The platform offers end-to-end solutions for creating impactful short video ads that combine creator authenticity with AI-driven research for compelling storytelling. With a focus on precision targeting, competitor analysis, and in-depth research, Hypergro ensures maximum ROI for brands looking to elevate their growth strategies.

Mobius is an AI infra platform including realtime computing and training. It is built on Ray, a distributed computing framework, and provides a number of features that make it well-suited for online machine learning tasks. These features include: * **Cross Language**: Mobius can run in multiple languages (only Python and Java are supported currently) with high efficiency. You can implement your operator in different languages and run them in one job. * **Single Node Failover**: Mobius has a special failover mechanism that only needs to rollback the failed node itself, in most cases, to recover the job. This is a huge benefit if your job is sensitive about failure recovery time. * **AutoScaling**: Mobius can generate a new graph with different configurations in runtime without stopping the job. * **Fusion Training**: Mobius can combine TensorFlow/Pytorch and streaming, then building an e2e online machine learning pipeline. Mobius is still under development, but it has already been used to power a number of real-world applications, including: * A real-time recommendation system for a major e-commerce company * A fraud detection system for a large financial institution * A personalized news feed for a major news organization If you are interested in using Mobius for your own online machine learning projects, you can find more information in the documentation.

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.

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.

last_layer is a security library designed to protect LLM applications from prompt injection attacks, jailbreaks, and exploits. It acts as a robust filtering layer to scrutinize prompts before they are processed by LLMs, ensuring that only safe and appropriate content is allowed through. The tool offers ultra-fast scanning with low latency, privacy-focused operation without tracking or network calls, compatibility with serverless platforms, advanced threat detection mechanisms, and regular updates to adapt to evolving security challenges. It significantly reduces the risk of prompt-based attacks and exploits but cannot guarantee complete protection against all possible threats.

ai-collective-tools is an open-source community dedicated to creating a comprehensive collection of AI tools for developers, researchers, and enthusiasts. The repository provides a curated selection of AI tools and resources across various categories such as 3D, Agriculture, Art, Audio Editing, Avatars, Chatbots, Code Assistant, Cooking, Copywriting, Crypto, Customer Support, Dating, Design Assistant, Design Generator, Developer, E-Commerce, Education, Email Assistant, Experiments, Fashion, Finance, Fitness, Fun Tools, Gaming, General Writing, Gift Ideas, HealthCare, Human Resources, Image Classification, Image Editing, Image Generator, Interior Designing, Legal Assistant, Logo Generator, Low Code, Models, Music, Paraphraser, Personal Assistant, Presentations, Productivity, Prompt Generator, Psychology, Real Estate, Religion, Research, Resume, Sales, Search Engine, SEO, Shopping, Social Media, Spreadsheets, SQL, Startup Tools, Story Teller, Summarizer, Testing, Text to Speech, Text to Image, Transcriber, Travel, Video Editing, Video Generator, Weather, Writing Generator, and Other Resources.

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.

Lobe Icons is a collection of popular AI / LLM Model Brand SVG logos and icons. It features lightweight and scalable icons designed with highly optimized scalable vector graphics (SVG) for optimal performance. The collection is tree-shakable, allowing users to import only the icons they need to reduce the overall bundle size of their projects. Lobe Icons has an active community of designers and developers who can contribute and seek support on platforms like GitHub and Discord. The repository supports a wide range of brands across different models, providers, and applications, with more brands continuously being added through contributions. Users can easily install Lobe UI with the provided commands and integrate it with NextJS for server-side rendering. Local development can be done using Github Codespaces or by cloning the repository. Contributions are welcome, and users can contribute code by checking out the GitHub Issues. The project is MIT licensed and maintained by LobeHub.

Baal is an active learning library that supports both industrial applications and research use cases. It provides a framework for Bayesian active learning methods such as Monte-Carlo Dropout, MCDropConnect, Deep ensembles, and Semi-supervised learning. Baal helps in labeling the most uncertain items in the dataset pool to improve model performance and reduce annotation effort. The library is actively maintained by a dedicated team and has been used in various research papers for production and experimentation.

Voicechat2 is a fast, fully local AI voice chat tool that uses WebSockets for communication. It includes a WebSocket server for remote access, default web UI with VAD and Opus support, and modular/swappable SRT, LLM, TTS servers. Users can customize components like SRT, LLM, and TTS servers, and run different models for voice-to-voice communication. The tool aims to reduce latency in voice communication and provides flexibility in server configurations.

Consistency Large Language Models (CLLMs) is a family of efficient parallel decoders that reduce inference latency by efficiently decoding multiple tokens in parallel. The models are trained to perform efficient Jacobi decoding, mapping any randomly initialized token sequence to the same result as auto-regressive decoding in as few steps as possible. CLLMs have shown significant improvements in generation speed on various tasks, achieving up to 3.4 times faster generation. The tool provides a seamless integration with other techniques for efficient Large Language Model (LLM) inference, without the need for draft models or architectural modifications.

This repository contains codes related to Generative AI as per YouTube video. It includes various notebooks and files for different days covering topics like map reduce, text to SQL, LLM parameters, tagging, and Kaggle competition. The repository also includes resources like PDF files and databases for different projects related to Generative AI.

MaskLLM is a learnable pruning method that establishes Semi-structured Sparsity in Large Language Models (LLMs) to reduce computational overhead during inference. It is scalable and benefits from larger training datasets. The tool provides examples for running MaskLLM with Megatron-LM, preparing LLaMA checkpoints, pre-tokenizing C4 data for Megatron, generating prior masks, training MaskLLM, and evaluating the model. It also includes instructions for exporting sparse models to Huggingface.

AutoGPTQ is an easy-to-use LLM quantization package with user-friendly APIs, based on GPTQ algorithm (weight-only quantization). It provides a simple and efficient way to quantize large language models (LLMs) to reduce their size and computational cost while maintaining their performance. AutoGPTQ supports a wide range of LLM models, including GPT-2, GPT-J, OPT, and BLOOM. It also supports various evaluation tasks, such as language modeling, sequence classification, and text summarization. With AutoGPTQ, users can easily quantize their LLM models and deploy them on resource-constrained devices, such as mobile phones and embedded systems.

This repository contains an implementation of Ring Attention, a technique for processing large sequences in transformers. Ring Attention splits the data across the sequence dimension and applies ring reduce to the processing of the tiles of the attention matrix, similar to flash attention. It also includes support for Striped Attention, a follow-up paper that permutes the sequence for better workload balancing for autoregressive transformers, and grouped query attention, which saves on communication costs during the ring reduce. The repository includes a CUDA version of the flash attention kernel, which is used for the forward and backward passes of the ring attention. It also includes logic for splitting the sequence evenly among ranks, either within the attention function or in the external ring transformer wrapper, and basic test cases with two processes to check for equivalent output and gradients.

Codefuse-ModelCache is a semantic cache for large language models (LLMs) that aims to optimize services by introducing a caching mechanism. It helps reduce the cost of inference deployment, improve model performance and efficiency, and provide scalable services for large models. The project caches pre-generated model results to reduce response time for similar requests and enhance user experience. It integrates various embedding frameworks and local storage options, offering functionalities like cache-writing, cache-querying, and cache-clearing through RESTful API. The tool supports multi-tenancy, system commands, and multi-turn dialogue, with features for data isolation, database management, and model loading schemes. Future developments include data isolation based on hyperparameters, enhanced system prompt partitioning storage, and more versatile embedding models and similarity evaluation algorithms.

This repository documents how to optimize common algorithms based on CUDA. It includes subdirectories with code implementations for specific optimizations. The optimizations cover topics such as compiling PyTorch from source, NVIDIA's reduce optimization, OneFlow's elementwise template, fast atomic add for half data types, upsample nearest2d optimization in OneFlow, optimized indexing in PyTorch, OneFlow's softmax kernel, linear attention optimization, and more. The repository also includes learning resources related to deep learning frameworks, compilers, and optimization techniques.

llmc is an off-the-shell tool designed for compressing LLM, leveraging state-of-the-art compression algorithms to enhance efficiency and reduce model size without compromising performance. It provides users with the ability to quantize LLMs, choose from various compression algorithms, export transformed models for further optimization, and directly infer compressed models with a shallow memory footprint. The tool supports a range of model types and quantization algorithms, with ongoing development to include pruning techniques. Users can design their configurations for quantization and evaluation, with documentation and examples planned for future updates. llmc is a valuable resource for researchers working on post-training quantization of large language models.

The NVIDIA TensorRT Model Optimizer is a library designed to quantize and compress deep learning models for optimized inference on GPUs. It offers state-of-the-art model optimization techniques including quantization and sparsity to reduce inference costs for generative AI models. Users can easily stack different optimization techniques to produce quantized checkpoints from torch or ONNX models. The quantized checkpoints are ready for deployment in inference frameworks like TensorRT-LLM or TensorRT, with planned integrations for NVIDIA NeMo and Megatron-LM. The tool also supports 8-bit quantization with Stable Diffusion for enterprise users on NVIDIA NIM. Model Optimizer is available for free on NVIDIA PyPI, and this repository serves as a platform for sharing examples, GPU-optimized recipes, and collecting community feedback.

RLAIF-V is a novel framework that aligns MLLMs in a fully open-source paradigm for super GPT-4V trustworthiness. It maximally exploits open-source feedback from high-quality feedback data and online feedback learning algorithm. Notable features include achieving super GPT-4V trustworthiness in both generative and discriminative tasks, using high-quality generalizable feedback data to reduce hallucination of different MLLMs, and exhibiting better learning efficiency and higher performance through iterative alignment.

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