#FraudDetection

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[govindhtech]

Extractive AI vs. Generative AI: Data Extraction & Precision

What Is Extractive AI?

The goal of the natural language processing (NLP) area of extractive AI is to locate and extract important information from pre-existing data sources. Extractive AI is superior at locating and condensing pertinent information from papers, databases, and other structured or unstructured data formats, in contrast to its generative AI cousin, which produces original material.

Consider it a superpowered search engine that can identify the precise lines or sections that address your question in addition to bringing up webpages. Extractive AI is perfect for applications demanding precision, transparency, and control over the extracted information because of its focused approach.

How Does Extractive AI Work?

A variety of NLP approaches are used by extractive AI, including:

Tokenization breaks text into words or phrases.

Named entity recognition (NER) categorizes people, places, and organizations.

Grammatical functions are assigned to phrase words by part-of-speech tagging.

Semantic analysis examines word meaning and relationships.

By using these methods, extractive AI algorithms examine the data, looking for trends and pinpointing the sections that most closely correspond to the user’s request or needed data.

Rise of Extractive AI in the Enterprise

The growing use of extractive AI across a variety of sectors is expected to propel the worldwide market for this technology to $26.8 billion by 2027. Companies are realizing how useful extractive AI is for improving decision-making, expediting procedures, and deriving more profound insights from their data.

The following are some of the main applications of extractive AI that are propelling its use:

Understanding and summarizing papers: Taking important details out of financial data, legal documents, contracts, and customer evaluations.

Enhancing the precision and effectiveness of search queries in business databases and repositories is known as information retrieval and search.

Collecting and evaluating news stories, social media posts, and market data in order to learn about rival tactics is known as competitive intelligence.

Customer care and support: increasing agent productivity, automating frequently asked questions, and evaluating customer feedback.

Finding suspicious behavior and trends in financial transactions and other data sources is the first step in fraud detection and risk management.

Benefits of Extractive AI

Precision Point Extraction

From unstructured data, such as papers, reports, and even social media, extractive AI is excellent at identifying important facts and statistics. Imagine it as a super-powered highlighter that uses laser concentration to find pertinent bits. This guarantees you never overlook an important element and saves you hours of laborious research.

Knowledge Unlocking

Information that has been extracted is knowledge that has yet to be unlocked; it is not only raw data. These fragments may then be analyzed by AI, which will uncover trends, patterns, and insights that were before obscured by the chaos. This gives companies the ability to improve procedures, make data-driven choices, and get a competitive advantage.

Efficiency Unleashed

Time-consuming and monotonous repetitive jobs include data input and document analysis. By automating these procedures, extractive AI frees up human resources for more complex and imaginative thought. Imagine a workplace where your staff members spend more time utilizing information to create and perform well rather of collecting it.

Transparency Triumphs

The logic of extractive AI is transparent and traceable, in contrast to some AI models. You can examine the precise source of the data and the extraction process. This openness fosters confidence and facilitates confirming the veracity of the learned lessons.

Cost Savings Soar

Extractive AI significantly reduces costs by automating processes and using data. A healthy bottom line is a result of simpler procedures, better decision-making, and lower personnel expenses.

Thus, keep in mind the potential of extractive AI the next time you’re overwhelmed with data. obtaining value, efficiency, and insights that may advance your company is more important than just obtaining information.

The Future Of Extractive AI

Extractive AI has made a name for itself in jobs like summarization and search, but it has much more potential. The following are some fascinating areas where extractive AI has the potential to have a big influence:

Answering questions: Creating intelligent assistants that are able to use context awareness and reasoning to provide complicated answers.

Customizing information and suggestions for each user according to their requirements and preferences is known as personalization.

Fact-checking and verification: Automatically detecting and confirming factual assertions in order to combat misinformation and deception.

Constructing and managing linked information bases to aid in thinking and decision-making is known as knowledge graph creation.

Read more on Govindhtech.com

[kodytechnolab]

http://kodytechnolab.com/blog/predictive-analytics-in-banking-industry/

Power Up Your Bank with Predictive Insights

The banking sector is no longer just about transactions. It’s about predicting behavior, minimizing risks, and maximizing growth, all with data.

This blog on predictive analytics in banking industry dives into how forward-thinking banks are using AI to: 🔍 Detect fraud before it happens 💡 Personalize customer experiences 📊 Optimize credit scoring and risk assessment

If you're a banker, fintech strategist, or decision-maker aiming to future-proof operations, this read is a must.

[gridlines000]

 KYC Fraud in the Digital Age: Understanding the Threat and Strengthening Defenses

 Explore the escalating threat of KYC fraud in today's digital landscape. This blog delves into common fraud types, their impacts on financial institutions, and how advanced solutions like Gridlines' KYC APIs can fortify defenses against evolving cyber threats.

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[ekychubofficial]

🔐 Prevent UPI Fraud with Ekychub’s Real-Time Verification API

The rapid growth of UPI (Unified Payments Interface) has revolutionized digital transactions in India. However, this surge in usage has also led to a sharp rise in UPI-related fraud. Fake UPI IDs, misrouted transactions, and impersonation scams are now major threats for businesses and customers alike.

To combat this, Ekychub offers the fastest and most secure UPI Fraud Prevention API, designed to help businesses verify UPI IDs in real time, safeguard their platforms, and build user trust.

🚨 Why Is UPI Fraud Prevention So Important?

Every second, thousands of UPI transactions are processed across platforms. Even a small lapse in verification can lead to:

Financial loss for the business or the customer

Damaged reputation due to failed or fraudulent transactions

Compliance issues in regulated industries

That’s where Ekychub steps in — providing a simple yet powerful solution to instantly verify UPI IDs before payment.

⚡ Ekychub’s UPI Fraud Prevention API – Key Features

✅ Real-Time UPI ID Verification

Our API instantly checks the validity of a UPI ID and confirms if it’s linked to a legitimate account.

👤 Fetch Account Holder Name

Match the UPI ID to the account holder’s name to detect impersonation or mismatches before processing payments.

🛡️ Fraud Detection & Prevention

Automatically flag suspicious UPI IDs or unknown patterns, minimizing the risk of fraud.

🔄 Seamless Integration

The API is lightweight, fast, and easily integrates into your existing payment system or app.

📈 Scalable & Secure

Whether you're handling hundreds or millions of transactions, our infrastructure is built to scale while keeping data encrypted and secure.

💼 Who Should Use This API?

Ekychub’s UPI Fraud Prevention API is ideal for:

Payment Gateways & Processors

Fintech Platforms & Digital Wallets

Lending & BNPL Apps

eCommerce & Marketplace Platforms

Subscription-Based Services

Basically, any business that accepts UPI payments should prioritize fraud prevention and user verification.

🧠 How It Works

Input the UPI ID into your platform (e.g., during checkout or onboarding).

Ekychub’s API validates the ID in real time.

Receive verified details including the account holder name and status.

Process the transaction securely with full confidence.

🚀 Benefits of Using Ekychub’s UPI Fraud Prevention API

Reduce failed transactions and chargebacks

Protect your customers and your brand

Speed up the verification process with automation

Build trust and ensure regulatory compliance

Lower operational costs related to fraud handling

🔗 Get Started Today

Don’t wait for fraud to affect your business. Integrate Ekychub’s UPI Verification API and take the first step toward secure, reliable UPI transactions.

[my-midlife-crisis]

"Elon is looking for fraud"

and he wasted it by leaving early.

And you wonder where the $5 trillion Musk is "looking for" went.

[mfilterit]

The Hidden Cost of Lead Generation Fraud in Pay-Per-Call Campaigns & How to Stop It

If you are selling products or services online in the US, there’s a good chance that you have dabbled with performance marketing or pay-per-call (PPC) campaigns, as they are commonly called. In fact, these days, in order to drive any kind of business online at scale, PPC campaigns have emerged as a necessity, and for good reason.

In a perfect world, PPC campaigns have the potential to provide businesses with high-intent leads that can easily be converted into paying customers. Many brands have deployed significantly large teams of sales experts to handle the leads generated by PPC campaigns and drive conversions.

Unfortunately, the world isn’t perfect, and modern PPC campaigns are plagued with lead generation fraud. Fraudsters use a variety of simple and sophisticated techniques to defraud advertisers and waste their ad spending while making a quick buck in the process. The worst part is that the wasted ad spend has just a short-term impact on such fraudulent activities. In the longer run, scammers can skew the metrics that advertisers use to plan their campaigns. As a result, lead generation fraud can have lasting effects on any brand’s PPC performance.

Let us look at this problem in more detail.

The Problem: How Lead Generation Fraud is Hurting Pay-Per-Call Campaigns

Lead generation fraud is a huge problem, especially for advertisers operating at a large scale. This problem can be made easy to understand by breaking it down into its components:

1. Fake and Invalid Leads

The most noticeable impact of fake campaigns is the fake lead generation with the use of bots and duplicates. Fraud publishers employ bots and in some cases, real people to click on ads and generate calls. However, since these calls are generated with the sole purpose of defrauding the advertisers and securing unethical monetary gains for the fraudsters, they have no real intent behind them and never convert into paying customers. The only losing party in this mix is the advertiser who ends up paying for calls that have no chance of converting, no matter how well a sales team works on them.

2. High Volume Of Fake Engagements

Fake and invalid leads generated with the use of bots of poorly paid employees of fraud publishers generate a lot of engagement but drive no real value. In fact, they drain ad budgets that could otherwise drive engagements that boost the business’ bottom line. Similarly, the fake calls generated also engage call center resources that could be otherwise used to engage with genuinely interested prospects. This reduces the operational efficiency of the call centers.

3. Lack Of Pre-Call Filtering Mechanisms

Most businesses don’t have a mechanism for lead validation designed for filtering out bad and fake leads before their call gets connected to their call center. This results in increased cost-per-acquisition (CPC) for the advertiser.

To know more about Impact of Lead Generation Fraud on Brands CLICK HERE

[govindhtech]

NVIDIA AI Workflows Detect False Credit Card Transactions

A Novel AI Workflow from NVIDIA Identifies False Credit Card Transactions.

The process, which is powered by the NVIDIA AI platform on AWS, may reduce risk and save money for financial services companies.

By 2026, global credit card transaction fraud is predicted to cause $43 billion in damages.

Using rapid data processing and sophisticated algorithms, a new fraud detection NVIDIA AI workflows on Amazon Web Services (AWS) will assist fight this growing pandemic by enhancing AI’s capacity to identify and stop credit card transaction fraud.

In contrast to conventional techniques, the process, which was introduced this week at the Money20/20 fintech conference, helps financial institutions spot minute trends and irregularities in transaction data by analyzing user behavior. This increases accuracy and lowers false positives.

Users may use the NVIDIA AI Enterprise software platform and NVIDIA GPU instances to expedite the transition of their fraud detection operations from conventional computation to accelerated compute.

Companies that use complete machine learning tools and methods may see an estimated 40% increase in the accuracy of fraud detection, which will help them find and stop criminals more quickly and lessen damage.

As a result, top financial institutions like Capital One and American Express have started using AI to develop exclusive solutions that improve client safety and reduce fraud.

With the help of NVIDIA AI, the new NVIDIA workflow speeds up data processing, model training, and inference while showcasing how these elements can be combined into a single, user-friendly software package.

The procedure, which is now geared for credit card transaction fraud, might be modified for use cases including money laundering, account takeover, and new account fraud.

Enhanced Processing for Fraud Identification

It is more crucial than ever for businesses in all sectors, including financial services, to use computational capacity that is economical and energy-efficient as AI models grow in complexity, size, and variety.

Conventional data science pipelines don’t have the compute acceleration needed to process the enormous amounts of data needed to combat fraud in the face of the industry’s continually increasing losses. Payment organizations may be able to save money and time on data processing by using NVIDIA RAPIDS Accelerator for Apache Spark.

Financial institutions are using NVIDIA’s AI and accelerated computing solutions to effectively handle massive datasets and provide real-time AI performance with intricate AI models.

The industry standard for detecting fraud has long been the use of gradient-boosted decision trees, a kind of machine learning technique that uses libraries like XGBoost.

Utilizing the NVIDIA RAPIDS suite of AI libraries, the new NVIDIA AI workflows for fraud detection improves XGBoost by adding graph neural network (GNN) embeddings as extra features to assist lower false positives.

In order to generate and train a model that can be coordinated with the NVIDIA Triton Inference Server and the NVIDIA Morpheus Runtime Core library for real-time inferencing, the GNN embeddings are fed into XGBoost.

All incoming data is safely inspected and categorized by the NVIDIA Morpheus framework, which also flags potentially suspicious behavior and tags it with patterns. The NVIDIA Triton Inference Server optimizes throughput, latency, and utilization while making it easier to infer all kinds of AI model deployments in production.

NVIDIA AI Enterprise provides Morpheus, RAPIDS, and Triton Inference Server.

Leading Financial Services Companies Use AI

AI is assisting in the fight against the growing trend of online or mobile fraud losses, which are being reported by several major financial institutions in North America.

American Express started using artificial intelligence (AI) to combat fraud in 2010. The company uses fraud detection algorithms to track all client transactions worldwide in real time, producing fraud determinations in a matter of milliseconds. American Express improved model accuracy by using a variety of sophisticated algorithms, one of which used the NVIDIA AI platform, therefore strengthening the organization’s capacity to combat fraud.

Large language models and generative AI are used by the European digital bank Bunq to assist in the detection of fraud and money laundering. With NVIDIA accelerated processing, its AI-powered transaction-monitoring system was able to train models at over 100 times quicker rates.

In March, BNY said that it was the first big bank to implement an NVIDIA DGX SuperPOD with DGX H100 systems. This would aid in the development of solutions that enable use cases such as fraud detection.

In order to improve their financial services apps and help protect their clients’ funds, identities, and digital accounts, systems integrators, software suppliers, and cloud service providers may now include the new NVIDIA AI workflows for fraud detection. NVIDIA Technical Blog post on enhancing fraud detection with GNNs and investigate the NVIDIA AI workflows for fraud detection.

Read more on Govindhtech.com

[kodytechnolab]

Stay Ahead of Fraud with Predictive Analytics in Fintech

In today's digital finance world, fraud moves fast but predictive analytics moves faster.

Learn how predictive analytics for fraud detection in fintech empowers businesses to: 🛡️ Spot suspicious activities before damage is done 📈 Minimize risks and boost customer trust ⚙️ Leverage machine learning for real-time decision-making

Whether you're a fintech founder, fraud analyst, or CTO, this blog reveals how data-driven strategies are becoming the new defense shield for financial institutions.

[gridlines000]

KYC Fraud in the Digital Age: Understanding the Threat and Strengthening Defenses

In an era where digital transactions are the norm, ensuring the authenticity of customer identities has become paramount. Know Your Customer (KYC) processes serve as the first line of defense against financial crimes. However, as technology advances, so do the tactics of fraudsters, making KYC fraud a pressing concern for financial institutions worldwide.

Understanding KYC and Its Importance

KYC involves verifying the identity of clients to assess potential risks in business relationships. It's essential for:​

Preventing money laundering and terrorist financing

Ensuring compliance with regulatory standards

Protecting institutions from fraud and reputational damage

Safeguarding the integrity of the financial system​

Despite stringent KYC measures, fraudsters continually devise methods to bypass these safeguards.​

Common Types of KYC Fraud

Identity Theft: Stealing personal information to impersonate individuals, enabling unauthorized account access or transactions.

Document Forgery: Creating counterfeit identification documents to deceive verification processes.​

Synthetic Identity Fraud: Combining real and fake information to create new, untraceable identities that can pass initial checks.​

Social Engineering: Manipulating individuals into revealing confidential information through phishing or impersonation tactics

Account Takeover: Gaining unauthorized access to existing accounts, often through stolen credentials, to conduct fraudulent activities

Mule Accounts: Using individuals, knowingly or unknowingly, to open accounts that facilitate illicit transactions.​

The Impact of KYC Fraud

The repercussions of KYC fraud are multifaceted:​

Financial Losses: Institutions face significant monetary damages due to fraudulent activities.

Reputational Damage: Incidents of fraud can erode public trust, leading to customer attrition.​

Regulatory Penalties: Non-compliance with KYC norms can result in hefty fines and increased scrutiny from regulatory bodies.​

Operational Disruption: Addressing fraud cases diverts resources and can hamper regular business operations.​

Leveraging Advanced Solutions: Gridlines' KYC APIs

To combat the evolving nature of KYC fraud, adopting advanced technological solutions is crucial. Gridlines offers robust KYC APIs designed to enhance verification processes:

Real-time Identity Verification: Utilizes advanced algorithms to authenticate identities swiftly, reducing the window for fraudulent activities.​

Document Authentication: Employs machine learning to assess the legitimacy of identification documents, detecting forgeries effectively.​

Facial Recognition: Incorporates biometric data to ensure the person undergoing verification matches the provided identification.​

These features not only bolster security but also streamline the onboarding process, enhancing customer experience.​

The Road Ahead

As fraudsters continue to refine their tactics, financial institutions must stay ahead by embracing innovative solutions and continuously updating their security protocols. Implementing advanced KYC verification tools, educating customers, and fostering a culture of vigilance are pivotal steps in safeguarding against KYC fraud.​

In conclusion, while the threat of KYC fraud looms large in the digital age, proactive measures and the integration of cutting-edge technologies like Gridlines' KYC APIs can significantly mitigate risks, ensuring a secure financial ecosystem for all stakeholders.

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