Data Science Dojo

APRIL 18, 2024

Use cases of AI in the e-commerce industry Artificial Intelligence (AI) plays a transformative role in e-commerce through various applications that enhance both the customer experience and operational efficiency. Using machine learning, Netflix analyzes viewing habits to predict what movies or shows users might enjoy next.

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ML @ CMU

MARCH 30, 2023

We observe that explanations do not in fact help with concrete applications such as fraud detection and paper matching for peer review. XAI methods are typically optimized for diverse but narrow technical objectives disconnected from their claimed use cases. How do you rigorously evaluate explanation methods?

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Data Science Dojo

NOVEMBER 21, 2023

Get more information: Generative AI in BFSI Market Applications of generative AI in BFSI fraud detection and prevention: GenAI is a game-changer in the realm of fraud detection. Here are some real-world examples and use cases: Document analysis : Many finance and accounting firms use generative AI for document analysis.

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Data Science Dojo

SEPTEMBER 26, 2023

This plot is particularly useful for tasks like hypothesis testing, anomaly detection, and model evaluation. By using a KS Plot, you can visually assess if there’s a significant difference in the distributions. You use a SHAP plot to explain to stakeholders why a certain applicant’s loan was approved or denied.

Data Science 370

Data Science Decision Trees Clustering Power BI 370

Data Science Dojo

AUGUST 22, 2023

Unlocking the Power of LLM Use-Cases: AI applications now excel at summarizing articles, weaving narratives, and sparking conversations, all thanks to advanced large language models. LLM Use-Cases Applications of large language models Language serves as a conduit for various forms of communication.

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Becoming Human

JUNE 5, 2023

AI in banking encompasses a range of applications, including customer service chatbots, fraud detection systems, risk assessment models, and predictive analytics. We will delve into specific use cases demonstrating AI technology’s tangible benefits in banking.

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IBM Journey to AI blog

MAY 8, 2024

The 2024 report showed a 71% increase in valid identities used in cyberattacks year-over-year. What really puts it into perspective is the realization that you are just as likely to have your valid identity used in a cyberattack as you are to see a phishing attack in your organization. Hackers don’t hack in; they log in.

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Analytics Analytics 89

ODSC - Open Data Science

MARCH 9, 2023

Be sure to check out his full-day training session, “ Advanced Fraud Modeling & Anomaly Detection with Python & R ,” there! For example, splitting data into two (or even three) pieces to validate your model results is useful. However, in the field of fraud detection, things just typically work a little differently.

Data Science 98

Data Science Python Data Scientist Analytics 98

IBM Journey to AI blog

DECEMBER 19, 2023

As organizations collect larger data sets with potential insights into business activity, detecting anomalous data, or outliers in these data sets, is essential in discovering inefficiencies, rare events, the root cause of issues, or opportunities for operational improvements. But what is an anomaly and why is detecting it important?

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AWS Machine Learning Blog

MAY 24, 2023

In this three-part series, we present a solution that demonstrates how you can automate detecting document tampering and fraud at scale using AWS AI and machine learning (ML) services for a mortgage underwriting use case. These fraud attempts can be challenging for mortgage lenders to capture.

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AWS Machine Learning Blog

APRIL 17, 2023

As more businesses increase their online presence to serve their customers better, new fraud patterns are constantly emerging. Traditional rule-based fraud detection systems are capped in their ability to quickly iterate as they rely on predefined rules and thresholds to flag potentially fraudulent activity.

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Dataconomy

JANUARY 23, 2024

AI programs for commercial use have been labelled as controversial and infringing, especially in creative industries. The technology has found potential use in the marketing industry, from statistics to copywriting, and is causing a storm. AI can be used to streamline marketing strategies, improve efficiency, and analyse data.

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Smart Data Collective

MAY 10, 2022

They also use it to detect fraud in massive financial networks with success. The human operator has to double-check only when the AI detects red flags in a borrower’s credit history. Fraud Detection. Fraud is a severe problem for every financial services company. AI in fintech is here to stay.

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Data Science Dojo

SEPTEMBER 8, 2023

Feature Predictive Analytics Artificial Intelligence Definition Uses historical data to identify patterns and predict future outcomes. Uses machine learning to learn from data and make decisions without being explicitly programmed. Techniques Uses statistical models, machine learning algorithms, and data mining.

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Becoming Human

MAY 19, 2023

Banking solution providers are using AI to rewrite decades-old processes and deliver robust and profitable banking solutions. In this article, we’ll talk about AI in banking use cases to understand how the banking industry is leveraging AI to enhance its capabilities. This is where AI and ML can be extremely useful.

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Dataversity

DECEMBER 30, 2021

In addition to the usual graph use cases – which include fraud detection, anti-money […]. Graph companies have attracted hundreds of millions of dollars in funding this year, as more organizations scale up (and out) with graph.

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Google Research AI blog

FEBRUARY 8, 2023

Arik, Research Scientists, Google Research, Cloud AI Team Anomaly detection (AD), the task of distinguishing anomalies from normal data, plays a vital role in many real-world applications, such as detecting faulty products from vision sensors in manufacturing , fraudulent behaviors in financial transactions , or network security threats.

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Data Science Dojo

MAY 12, 2023

In this blog, we will discuss the importance of probability in data science, its applications, and how it can be used to make data-driven decisions. In data science, it is used to quantify the uncertainty associated with data. It is also used to build models that can predict future events or outcomes based on past data.

Data Science 195

Data Science Data Scientist Machine Learning Machine Learning 195

IBM Journey to AI blog

MAY 17, 2024

Four AI in commerce use cases are already transforming the customer journey: modernization and business model expansion; dynamic product experience management (PXM); order intelligence; and payments and security. Meanwhile, 43% are using the technology to inform strategic decisions.

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Data Science Dojo

AUGUST 3, 2023

These vectors are mathematical representations of features or attributes, and they can be used to represent a wide variety of data, such as text, images, and audio. This vector representation is then stored in our memory and can be used to retrieve the information later. Milvus is used by companies such as Alibaba, Baidu, and Tencent.

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Database Natural Language Processing Machine Learning Machine Learning 195

Cambridge Intelligence

FEBRUARY 6, 2024

AI and data visualization can do a lot of the heavy lifting for analysts and investigators working with connected data – helping them detect, understand and even predict risks and threats. In the world of fraud, for example, criminals adapt constantly to exploit new vulnerabilities. But the case for human oversight is strong.

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Data Science Dojo

SEPTEMBER 17, 2023

Fraud Detection and Risk Assessment AI brings an exceptional skill to the table – the ability to detect irregular patterns within financial data. Fraud Detection and Risk Assessment AI brings an exceptional skill to the table – the ability to detect irregular patterns within financial data.

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ODSC - Open Data Science

MARCH 31, 2023

If you’re unfamiliar, these models use neural network architectures and vast amounts of data to generate text, answer questions, and perform language-related tasks. This is just one of the many popular use cases for large language models. This is why businesses are turning to LLMs to gain an edge against fraud.

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Smart Data Collective

SEPTEMBER 22, 2021

Cyber fraud statistics and preventions that every internet business needs to know to prevent data breaches in 2021. The key types of cyber security frauds in 2021. Cyber fraud is not just one or two types. Identity fraud is the most common form of cyber fraud. Online banking fraud can be a type of identity fraud.

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Big Data Big Data 107

Snorkel AI

AUGUST 8, 2023

Banks leverage email surveillance as a critical tool to combat fraud. First used in 1996 to detect fraudulent wire transfers, email surveillance helps banks thwart phishing attacks, detect money laundering, and protect customer data. subject lines, attachment types, keywords) that might indicate fraud or phishing.

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Snorkel AI

AUGUST 8, 2023

Banks leverage email surveillance as a critical tool to combat fraud. First used by Citibank in 1996 to detect fraudulent wire transfers, email surveillance helps banks thwart phishing attacks, detect money laundering, and protect customer data. Banks also commonly surveil emails to: Identify potential insider threats.

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ODSC - Open Data Science

APRIL 27, 2023

And even when you can find it, costs or privacy concerns may prevent you from using it. But what is synthetic data being used for? What’s more, when using video clips. What’s more, models trained on synthetic data have proved to be more accurate than models trained on real-world data in certain cases during recent tests.

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Machine Learning Machine Learning Data Science Data Preparation 52

Towards AI

MARCH 21, 2023

In this article, I’ll share what I’ve learned, where hybrid systems can be used, and what benefits you get by introducing them to an ML pipeline. Below are a few examples: Fraud Detection: In fraud detection, rule-based systems can be used to flag and investigate suspicious transactions based on predefined rules quickly.

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ML ML Machine Learning Machine Learning 94

Pickl AI

SEPTEMBER 3, 2023

Introduction Anomaly detection is identified as one of the most common use cases in Machine Learning. The following blog will provide you a thorough evaluation on how Anomaly Detection Machine Learning works, emphasising on its types and techniques. Billion which is supposed to increase by 35.6% CAGR during 2022-2030.

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IBM Journey to AI blog

MAY 1, 2023

It turned out to be a fraud, with a human player behind the machine. Soon after, AI’s capabilities extended to Speech and Natural Language processing, such as with IBM Watson, and for Image Recognition, which is now ubiquitously used for unlocking phones and other biometric security.

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Data Science Dojo

MARCH 14, 2023

During the introductory session, a comprehensive overview of GraphDB was provided, highlighting its unique features and the ideal use cases for graph technology. Following this, the session focused on the specific use case of fraud detection and featured a demonstration of a potential graph-based solution.

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IBM Journey to AI blog

MAY 10, 2024

Underpinning most artificial intelligence (AI) deep learning is a subset of machine learning that uses multi-layered neural networks to simulate the complex decision-making power of the human brain. In deep learning use cases, FPGAs are valued for their versatility, power efficiency and adaptability.

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Towards AI

SEPTEMBER 28, 2023

Deployment & Serving: Exploring 6 Key MLOps Questions using AWS SageMaker This article is part of the AWS SageMaker series for exploration of ’31 Questions that Shape Fortune 500 ML Strategy’. Use Case & Dataset We will reuse the Fraud Detection use-case, dataset generator, and generated customer & transaction dataset.

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Dataconomy

MARCH 27, 2024

Small businesses can use AI to improve their decision-making, streamline operations, and boost productivity. AI analyses vast amounts of data to find patterns and make predictions using that data. AI can detect fraud in the financial industry. Businesses that fail to upskill their employees risk falling behind.

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Mlearning.ai

MARCH 26, 2023

In this research paper, oversampling is presented as a solution to the challenge that fraudulent transactions are in most cases a minority compared with the population of transactions, and thus the entire fraud class can be mis-predicted when the overall accuracy rate is reaching a satisfying percentage of over 90%.

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Smart Data Collective

MAY 23, 2021

Artificial intelligence (AI) and machine learning (ML) are two technologies that use digital technology to improve conventional financial services and solutions. Why not use customer-facing systems like AI interfaces and Chatbots instead of a conventional call center that gets mixed reviews? Detection and prevention of fraud.

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ODSC - Open Data Science

AUGUST 31, 2023

The Role of AI in Counterfeit Detection Experts must bolster AI tools to be more proficient at being an anti-counterfeit technology than one to make illegal products. The more robust and curated the information is, the more accurately it can detect specific types of counterfeiting.

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