#machine learning in fraud detection

[lesliedodge5]

Doriel Abrahams, Principal Technologist at Forter, on AI, Fraud Prevention, & Digital Trust Future

In this episode of Discover Dialogues, we’re joined by Doriel Abrahams, Principal Technologist at Forter, who shares his expert insights on how AI is reshaping the landscape of fraud detection and how businesses can leverage this technology to protect their customers and build digital trust. Doriel has been leading AI-driven fraud prevention at Forter for over a decade, helping businesses tackle one of the most pressing challenges in digital commerce. He discusses how real-time fraud detection and AI models are revolutionizing how businesses handle fraud prevention, allowing them to identify fraudulent activities before they cause harm. With the rise of digital transactions, AI-powered systems are becoming an indispensable tool for businesses to automate fraud detection and reduce the burden of manual oversight.

[nearlearn6]

Discover how Machine Learning is shaping the modern world! This infographic explores 5 powerful real-world applications of Machine Learning across industries like healthcare, finance, transportation, and more. From fraud detection to self-driving cars, these examples show how ML is driving innovation and solving real-world problems.

Whether you're a tech enthusiast, student, or professional, this infographic offers a quick and visual insight into the practical power of Machine Learning.

🔍 Learn how you can master these skills with industry-relevant training at Nearlearn — a trusted name in AI & ML education.

Checkout the nearlearn website :https://nearlearn.com/courses/ai-and-machine-learning/machine-learning-with-python-training

[thetatechnolabsusa]

How AI and ML Can Optimize Medicine Supply Chains and Order Processing

The pharmaceutical supply chain is undergoing a transformation with AI and Machine Learning, improving demand forecasting, order processing, logistics, and compliance management. By integrating AI-driven solutions, pharma companies can optimize inventory, reduce costs, and enhance efficiency. Theta Technolabs provides cutting-edge AI development services to streamline supply chain operations and boost order management automation for a smarter, data-driven pharmaceutical industry.

[technology-insights]

Top 5 Fraud Detection Software of 2025

Fraud has existed for centuries, evolving from simple deception to complex cybercrimes like identity theft and payment fraud. To combat these threats, businesses rely on Fraud Detection Software, which uses AI, machine learning, and real-time monitoring to detect and prevent fraud.

What Is Fraud Detection Software?

Fraud Detection Software analyzes transactions and user behavior to identify anomalies, flagging high-risk activities for further investigation. By automating fraud prevention, businesses can minimize financial losses and enhance security.

Top 5 Fraud Detection Software of 2025

1. Sift

Sift employs AI-driven fraud detection, analyzing over a trillion events annually. It offers real-time monitoring and risk assessment, though some users report occasional inconsistencies in its scoring system.

2. LexisNexis® ThreatMetrix®

ThreatMetrix leverages global transaction data, device intelligence, and behavioral biometrics to prevent fraud. While praised for its analytics, its interface can be overwhelming for non-technical users.

3. Signifyd

Designed for e-commerce, Signifyd uses machine learning to prevent fraud throughout the buyer journey. Users appreciate its automation and chargeback protection but desire more customization options.

4. Kount

Owned by Equifax, Kount reduces false positives while improving approval rates using AI-driven risk assessment. However, some users find its interface and setup process complex.

5. Riskified

Riskified enhances fraud detection with global transaction analysis and chargeback protection. While effective, it can sometimes be overly cautious in rejecting orders.

Conclusion

Fraud Detection Software is essential for modern businesses, helping to mitigate risks while ensuring security. These AI-powered solutions provide proactive fraud prevention, protecting businesses from evolving threats.

[olivergisttv]

How to Use AI to Predict and Prevent Cyberattacks

In today’s rapidly evolving digital landscape, cyberattacks are becoming more frequent, sophisticated, and devastating. As businesses and individuals increasingly rely on technology, the need to bolster cybersecurity has never been more critical. One of the most promising solutions to combat this growing threat is Artificial Intelligence (AI). AI can enhance cybersecurity by predicting,…

[eastnetsblogs]

3 Benefits of Bank Fraud Detection Using Machine Learning

Bank fraud remains a growing concern in the financial sector, with criminals adopting increasingly sophisticated tactics. To stay ahead, financial institutions are turning to machine learning (machine learning) for fraud detection. This technology revolutionises how banks identify and prevent fraudulent activities. Here are three key benefits of using machine learning in fraud detection. 

1. Real-Time Fraud Detection:

Machine learning algorithms analyse massive amounts of data in real time, identifying suspicious patterns or anomalies as they occur. Unlike traditional methods, which often rely on retrospective analysis, machine learning offers proactive fraud detection. This swift response minimises financial losses and protects customer accounts from potential breaches. 

2. Improved Accuracy and Reduced False Positives:

Manual fraud detection methods can result in false positives, leading to customer dissatisfaction and operational inefficiencies. Machine learning models continuously learn and adapt to evolving fraud trends, enabling them to distinguish genuine transactions from fraudulent ones with greater precision. This reduces false alarms, allowing banks to focus resources on actual threats. 

3. Cost Efficiency and Scalability:

By automating fraud detection processes, machine learning reduces the reliance on manual interventions, cutting operational costs. Additionally, machine learning systems are scalable for banking fraud detection, making them suitable for banks of all sizes, whether handling a few transactions or millions daily. 

Embracing machine learning empowers banks to stay ahead of fraudsters, ensuring customer trust and operational efficiency in an ever-evolving financial landscape. The best you can do is to get connected with a reliable software provider. Request a demo today!

[mohsinshield]

Device fingerprinting is a powerful tool for online platform owners and managers to protect their websites or apps from fraud, validate user identities, and enhance digital advertising efforts. It involves collecting distinct data points from a user's device each time they access the platform. These data points, like screen resolution, browser version, or IP address, create a unique "fingerprint" for the device. This fingerprint allows businesses to compare devices across sessions and assess the risk associated with particular interactions.

Just as a human fingerprint is used to identify individuals, a device fingerprint can track a specific device as it moves between different apps or websites. This capability is crucial for identifying and preventing fraudulent activity in real time.

Device fingerprint plays a critical role in preventing fraud on digital platforms by assessing the level of risk throughout the customer journey, from registration to transaction. By continuously profiling user sessions, the platform can detect unusual patterns or behaviors that indicate potential fraud. This ongoing evaluation enables fraud detection teams to take immediate action—automatically blocking high-risk devices or flagging them for further manual review. This proactive approach ensures that businesses can make informed decisions, minimizing the risk of fraudulent activity while maintaining a smooth user experience for legitimate customers.

Device fingerprinting, combined with machine learning and AI, empowers online platforms to detect and adapt to emerging fraud patterns in real-time. This powerful combination continuously strengthens security, staying one step ahead of evolving threats.

[techtoio]

Unlocking Insights: How Machine Learning Is Transforming Big Data

Introduction

Big data and machine learning are two of the most transformative technologies of our time. At TechtoIO, we delve into how machine learning is revolutionizing the way we analyze and utilize big data. From improving business processes to driving innovation, the combination of these technologies is unlocking new insights and opportunities. Read to continue

[mobiloitteuk]

UK’s Pioneering Role in AI Regulation: A Pro-Innovation Blueprint for AI Technology

The realm of artificial intelligence (AI) is rapidly evolving, and with it comes the imperative for robust regulation. The United Kingdom, a hub for AI solutions and AI-powered mobile apps, has emerged as a trailblazer in this domain. This article delves into the UK’s recent White Paper on AI regulation, its implications for the global AI technology community, and how it aims to enhance customer experiences.

[parker-natalie]

How Does Machine Learning Algorithm Prevent Fintech Fraud Detection

In the ever-evolving landscape of financial technology (fintech), where digital transactions and online banking have become the norm, the risk of fraudulent activities has also risen significantly. Fintech companies are constantly seeking innovative ways to combat fraud while ensuring a seamless user experience. This is where machine learning algorithms step in as powerful tools that not only enhance fraud detection but also adapt and learn from emerging threats. In this article, we delve into how machine learning algorithms play a pivotal role in preventing fintech fraud and revolutionizing the way we secure our financial transactions.

Understanding Fintech Fraud Detection

Fintech fraud can encompass a wide range of deceptive activities, including account takeovers, identity theft, phishing attacks, and more. Traditional rule-based fraud detection systems have limitations in keeping up with rapidly evolving fraud patterns. This is where machine learning shines continue reading...

[glitchlight]

Oh No! I got mad about something someone I dont know posted on the internet and I am brooding and angry about it! Instead of posting I will relax and reflect and do something more productive like:

Scuba diving

Yoga

National Park Travelers Club

Becoming A Nudist

Jigsaw puzzles

Wikipedia editing

Inventing A Time Machine

Woodworking

Masturbating

Succumbing To The Amulet

Genealogy

Masturbating

Dark Alchemy

Robot combat

Bungee jumping

Electronics repair

Beekeeping

Lego sets

Shuffleboard

Slacklining

Eating Lugnuts Off The Cars In the Walmart Parking Lot

Photography

Metalworking

Hacking

Golfing

Paintball

Transcending the Limitations of Flesh

Welding

Thrifting

Sleeping

Abolishing The Division of Night and Day

Pet fostering

Meteorology

Getting Gone

Bowling

Dumpster Diving

Book collecting

Amateur radio

Meditating On My Uncountable Failures

Weaving

Ice skating

Graphic design

Brewing

Masturbating

Car racing

Stealing

Camping

Teaching Crows How To Commit Tax Fraud

Getting Really Good At Beatboxing

Cooking

Getting My Stink Salted

Bird watching

Crocheting

Gymnastics

Screaming Into the Night Sky At God

Metal detecting

Masturbating

Driving Off A Bridge

Sleeping

Thinking about Masturbating

Revisiting Classics To See If They Hold Up

Origami

Drinking

Masturbating

Billiards

Chess

Sleeping

Geocaching

Bread making

Launching rockets

Calligraphy

Archery

Jewelry making

Smoking

Video games

Needlepoint

Water skiing

Animal breeding

Stealing

Podcasting

Fantasy sports

Learning Spanish

Wine tasting

Backpacking

Getting Way Too Into Sports

Alchemy

Karaoke

Stealing

Traveling

Turning Straight Women Gay

Taxidermy

Masturbating

Horseback riding

Fishing

Being a DJ

Quilting

Juggling

Record collecting

Baking

Glassblowing

Drones

Stealing Infant Teeth

Crossfit

Improvisation

Attuning Myself To Crystals For the Purposes of Psychic Attacks

Drinking

Playing a musical instrument

Stand-up comedy

Throwing Myself Into A Volcano

Skiing

Remote cars

Bonsai

Furniture restoration

Quitting While I'm Ahead

Drinking

Writing

Smoking

Meterology

Local historical society

Disappearing In A Mysterious Accident

Assassination

Painting

Handball

Masturbating

Cheese-making

Martial arts

Astronomy

App making

Table tennis

Web design

Letting All The Demons Out of Hell

Farming

Hiking

Home improvement projects

Swimming

Skydiving

Volunteering

Animal grooming

Forbidden Alchemy

Remote airplanes

Gardening

Burying A Bunch Of Eggs

Becoming The Worlds Preeminent White Maoist

Digging A Hole To The Center of the Earth

Trivia

Journaling

Video production

Masturbating

Drinking

Crossword puzzles

Vehicle restoration

Candle-making

Drinking

Reading

Art collecting

Drawing

Makeup

Smoking

Running

Dancing On the Graves of My Enemies

Sleeping

Kayaking

Poetry

Knitting

Sleeping

Designing clothing

Sailing

Acting

Rock climbing

Disc golfing

Scrapbooking

Winemaking

Wood burning

Running Away

Museum visiting

Pottery

Escape rooms

Soap making

LARPing

Freestyling

Flying

Smoking

Snowboarding

Board games

Just Eating A Bunch of Candy

Surfing

Masturbating

Mixology

Smoking

Card games

Kite surfing

Masturbating

Composting

Dancing

Creating The Perfect French Fry

Powerlifting

Model trains

The Rites And Rituals Forbidden To Me

Movie reviews

Frisbee Wizardry

[justinspoliticalcorner]

Don Moynihan at Can We Still Govern?:

Members of the DOGE network rarely offer thoughtful accounts that depart from the Musk narrative (government is broken, full of talentless hacks, DOGE is fixing things). So I was interested to see a DOGEr express genuine circumspection. This came from Sahil Lavingia, a startup founder, who was interviewed by Ernie Smith about his experience with DOGE. Lavignia left his start-up, Gumroad, to join Veteran’s Affairs. Here is the key passage:

[Now that he’s there, he says he finds himself surrounded by people who “love their jobs,” who came to the government with a sense of mission driving their work. “In a sense, that makes the DOGE agenda a little bit more complicated, because if half the government took [a buyout offer], then we wouldn’t have to do much more,” he says, implying software can replace departing employees. “We’d just basically use software to plug holes. But that’s not what’s happening.”…when it comes down to it, what he’s found is a machine that largely functions, though it doesn’t make decisions as fast as a startup might. “I would say the culture shock is mostly a lot of meetings, not a lot of decisions,” he says. “But honestly, it’s kind of fine—because the government works. It’s not as inefficient as I was expecting, to be honest. I was hoping for more easy wins.”]

For anyone with a modicum of experience studying or working in government, nothing Lavignia discovered is novel. Public servants care about public service! There is not really that much waste in government! There are too many meetings and decisionmaking is too slow! Indeed, sir! All true! To his credit, Lavignia was willing to acknowledge his prior beliefs were wrong. That is a hard thing for any human to do, and something I have not seen from any other DOGE official, certainly not Musk. The exception offers insight to the rule: DOGErs simply don’t understand the government they are destroying, and are unwilling to learn.

DOGE could have learned a lot by just talking to public employees

This sounds obvious to say, but people handed extraordinary power that affects the lives of others should know what they are doing. If they don’t know important stuff, they should want to learn it before exercising consequential decisions. But DOGE has failed to meet even these banal standards. A trademark of DOGE was the toxic combination of arrogance and ignorance. Some DOGE members have real and impressive achievements, some do not. But all of those achievements are in the private sector. They know almost nothing about government except conspiracy theories from the internet, or negative interactions with the regulators who oversee their businesses. They did not understand where government spent its money, and that it was not full of waste. They could have talked to career public employees or nonpartisan government experts. But they didn’t because they did not want to know. Their closest advisors about how government worked were partisan ideologues who want to upend the constitution, Stephen Miller and Russ Vought. Random X posters seem to have more input on the fate of USAID than any policy experts.

This arrogance has had real consequences, including half-assed administrative changes. DOGE ignored their own pick to run Social Security when he told them that their fraud numbers were wrong. They insisted on administrative changes to detect fraud in SSA phone calls. As Natalie Alms reports, the result was that they found two cases of probably fraudulent phone calls out of 110,000. (Or a fraud rate of 0.0018%). But the extra checks they put in place to chase the phantom fraud were not costless: they slowed the processing of retirement claims by 25%. Why did SSA employees not resist the change? According to a former senior SSA official: “People lacked the fortitude to tell DOGE there was no fraud because they were afraid to lose their jobs. They knew there was no fraud.” It’s not just that DOGE was unwilling to listen: they would fire anyone willing to speak up.

A missed opportunity

A lot of people really wanted DOGE to succeed, or more specifically, they wanted the idealized version of DOGE — smart tech folks disrupting bureaucracy — to succeed. This includes me: when I warned that DOGE would be a disaster right before the Trump inauguration, I felt real ambivalence. Was this just my natural cynicism? Just a couple of days later Will Oremus in the Washington Post asked me, Jen Pahlka and Bridget Dooling to make the case for the best and worst case scenarios for DOGE. Needless to say, the worst case scenarios proved more accurate, and even understated the case, but it was not hard to imagine how things might go well. [...] There are lots of things DOGE does not get about how to manage in the public sector. This overview of the senior talent that DOGE has pushed out of government is simply an extraordinary indictment of it’s incompetence. But there is one particular type of knowledge that DOGE really missed out on which is unforgivable: how tech works in government. Some of those who wanted DOGE to succeed were government technologists, frustrated at the slow pace of change they saw during their time in government. Many were ready to welcome fellow technologists with the influence to transform government. But they were isolated, ignored, fired or resigned. In doing so, DOGE missed their best opportunity to actually live up to their brand as tech disruptors who could fix things. Now, their brand will be the tech bros who broke government. The civic tech movement in the federal government was a decade old when DOGE arrived (the US Digital Service and 18F were created in 2014). I have been tracking the evolution of civic tech, because I think it represents one path for the future of American government. That path could have merged with DOGE, creating a single broad vision of tech-driven change in government. Instead, in now offers a competing vision. I think its worth unpacking some of those differences in a bit more detail. [...]

DOGE’s undemocratic workarounds will fail

There are a couple of big differences between DOGE and civic tech. One is motivational. The civic tech movement is, broadly, driven by prosocial motivations. They believed that government it slow and unwieldy, but that at some basic level it remains a fundamental means to help a lot of people. If you believe that, it makes sense to work at making the structure of government function. It make sense to invest time in building cool products that can be iterated and improved upon over time (Direct File is a good example).

DOGE mostly believes that government is irredeemably broken, wasteful and fraudulent. If you believe that, it makes sense to downsize government as much as possible, and contract out what you cannot. It makes sense not to build cool products (DOGE killed Direct File) but to build AI that cuts the humans out of the process and automate as much as possible. The fact that DOGE claims about fraud have proven to be erroneous should be a huge red flag about how the assumptions that will be embedded into their AI builds will prove to be wrong in ways that could be catastrophic. The other main difference between DOGE and civic tech was the attitudes to constraints. Both DOGE and civic tech employees would probably agree that government has too many constraints. The civic tech solution was largely to find ways to work with or manage those constraints. This included user guides to “hack your bureaucracy.” The DOGE approach has been largely to ignore the constraints. The Privacy Act of 1974 that civic tech people complained about? Pretend it does not exist. Pursue the type of data pooling for surveilling in the context of an increasingly aggressively police state.

The problem of Musk, and DOGE is not a problem of technological capabilities enabled by data sharing and AI. Those capabilities are here, present in the private sector as well as public. The problem is a governance one: laws that exist to constrain the use of data are being ignored. Writing new laws will not solve the problem of people ignoring existing laws. Civic tech folks complained about their limited ability to shape government. They never had a champion like Musk. But they also largely respected hierarchy and lines of control. Some of the turf issues that occur in any government happened. But the idea of the US Digital Service, rather than the Secretary of the Treasury, trying to appoint the acting head of the IRS is unthinkable. But Musk did so. The idea that a Cabinet Secretary would hand over all operational decisions to 18F would have been considered bizarre. But some Cabinet Secretaries have done this with DOGE. [...] Much of what DOGE is doing is illegal. Much of it will create long-term and lasting damage to America. This is the clearest example of an action that was illegal, anti-democratic, but also sped past any moral grey zones to being deeply evil. Even still, 60,000 tons of food, enough to keep millions from starving, are rotting in warehouses, blocked from release by another DOGEr, Jeremy Lewin. Per an analysis in Nature, up to 25 million people will die because of US cuts to foreign aid. Those who die will be largely the most vulnerable people in their world, their fates decided by some of the most privileged and protected people in the world.

DOGE has been nothing but a disaster to both the tech and government service.

[augustablog]

AI, Machine Learning, Artificial Neural Networks.

This week we learnt about the above topic and my take home from it is that Artificial Intelligence (AI) enables machines to mimic human intelligence, driving innovations like speech recognition and recommendation systems. Machine Learning (ML), a subset of AI, allows computers to learn from data and improve over time.

Supervised vs. Unsupervised Learning are types of AI

Supervised Learning: Uses labeled data to train models for tasks like fraud detection and image recognition.

Unsupervised Learning: Finds patterns in unlabeled data, used for clustering and market analysis.

Artificial Neural Networks (ANNs)

ANNs mimic the human brain, processing data through interconnected layers

Input Layer: Receives raw data.

Hidden Layers: Extract features and process information.

Output Layer: Produces predictions.

Deep Learning, a subset of ML, uses deep ANNs for tasks like NLP and self-driving technology. As AI evolves, understanding these core concepts is key to leveraging its potential.

It was really quite enlightening.

[pankukaushal]

𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐀𝐈-:

𝐖𝐡𝐚𝐭 𝐢𝐬 𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 ?

Artificial intelligence (AI) refers to computer systems capable of performing complex tasks that historically only a human could do, such as reasoning, making decisions, or solving problems.

𝐂𝐮𝐫𝐫𝐞𝐧𝐭 𝐀𝐈 𝐂𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬-:

AI today exhibits a wide range of capabilities, including natural language processing (NLP), machine learning (ML), computer vision, and generative AI. These capabilities are used in various applications like virtual assistants, recommendation systems, fraud detection, autonomous vehicles, and image generation. AI is also transforming industries like healthcare, finance, transportation, and creative domains. 

𝐀𝐈 𝐀𝐩𝐩𝐬/𝐓𝐨𝐨𝐥𝐬-:

ChatGpt, Gemini, Duolingo etc are the major tools/apps of using AI.

𝐑𝐢𝐬𝐤𝐬 𝐨𝐟 𝐀𝐈-:

1. Bias and Discrimination: AI algorithms can be trained on biased data, leading to discriminatory outcomes in areas like hiring, lending, and even criminal justice. 

2. Security Vulnerabilities: AI systems can be exploited through cybersecurity attacks, potentially leading to data breaches, system disruptions, or even the misuse of AI in malicious ways. 

3. Privacy Violations: AI systems often rely on vast amounts of personal data, raising concerns about privacy and the potential for misuse of that data. 

4. Job Displacement: Automation driven by AI can lead to job losses in various sectors, potentially causing economic and social disruption. 

5. Misuse and Weaponization: AI can be used for malicious purposes, such as developing autonomous weapons systems, spreading disinformation, or manipulating public opinion. 

6. Loss of Human Control: Advanced AI systems could potentially surpass human intelligence and become uncontrollable, raising concerns about the safety and well-being of humanity. 

𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐀𝐈:-

Healthcare:AI will revolutionize medical diagnostics, personalize treatment plans, and assist in complex surgical procedures. 

Workplace:AI will automate routine tasks, freeing up human workers for more strategic and creative roles. 

Transportation:Autonomous vehicles and intelligent traffic management systems will enhance mobility and safety. 

Finance:AI will reshape algorithmic trading, fraud detection, and economic forecasting. 

Education:AI will personalize learning experiences and offer intelligent tutoring systems. 

Manufacturing:AI will enable predictive maintenance, process optimization, and quality control. 

Agriculture:AI will support precision farming, crop monitoring, and yield prediction.