Can artificial intelligence help catch cyber fraud before it happens — or will it be used to commit more fraud?

Artificial Intelligence (AI) presents a fascinating and somewhat terrifying dual-edged sword in the realm of cyber fraud.
It absolutely has the potential to help catch fraud before it happens, but it is also undeniably being leveraged by criminals to commit more sophisticated and widespread fraud.

How AI Can Help Catch Cyber Fraud Before It Happens (Defense):
AI and Machine Learning (ML) are transforming fraud detection and prevention, moving from reactive to proactive measures.

Real-Time Anomaly Detection and Behavioral Analytics:
Proactive Monitoring: AI systems constantly monitor user behavior (login patterns, device usage, geographic location, typing cadence, transaction history) and system activity in real-time. They establish a "normal" baseline for each user and identify any deviations instantaneously.

Predictive Analytics: By analyzing vast datasets of past fraudulent and legitimate activities, AI can identify subtle, emerging patterns that signal potential fraud attempts before they fully materialize. For example, if a user suddenly attempts a large transfer to an unusual beneficiary from a new device in a high-risk country, AI can flag or block it immediately.

Examples: A bank's AI might notice a user trying to log in from Taiwan and then, moments later, attempting a transaction from a different IP address in Europe. This could trigger an immediate MFA challenge or block.

Advanced Phishing and Malware Detection:
Natural Language Processing (NLP): AI-powered NLP can analyze email content, social media messages, and text messages for linguistic cues, sentiment, and patterns associated with phishing attempts, even if they're expertly crafted by other AIs. It can detect subtle inconsistencies or malicious intent that humans might miss.

Polymorphic Malware: AI can help detect polymorphic malware (malware that constantly changes its code to evade detection) by identifying its behavioral patterns rather than just its signature.

Identifying Fake Content: AI can be trained to detect deepfakes (fake audio, video, images) by looking for minute inconsistencies or digital artifacts, helping to flag sophisticated impersonation scams before they deceive victims.

Threat Intelligence and Pattern Recognition:
Rapid Analysis: AI can rapidly process and correlate massive amounts of threat intelligence data from various sources (dark web forums, security bulletins, past incidents) to identify new fraud typologies and attack vectors.

Automated Response: When a threat is identified, AI can automate responses like blocking malicious IPs, updating blacklists, or issuing real-time alerts to affected users or systems.

Enhanced Identity Verification and Biometrics:
AI-driven biometric authentication (facial recognition, voice analysis, fingerprint scanning) makes it significantly harder for fraudsters to impersonate legitimate users, especially during remote onboarding or high-value transactions.

AI can analyze digital identity documents for signs of forgery and compare them with biometric data in real-time.

Reduced False Positives:
Traditional rule-based fraud detection often generates many false positives (legitimate transactions flagged as suspicious), leading to customer friction and operational inefficiencies. AI, with its adaptive learning, can significantly reduce false positives, allowing legitimate transactions to proceed smoothly while still catching actual fraud.

How AI Can Be Used to Commit More Fraud (Offense):
The same advancements that empower fraud detection also empower fraudsters. This is the "AI arms race" in cybersecurity.

Hyper-Personalized Phishing and Social Engineering:
Generative AI (LLMs): Tools like ChatGPT can generate perfectly worded, grammatically correct, and highly personalized phishing emails, texts, and social media messages. They can mimic corporate tone, individual writing styles, and even leverage publicly available information (from social media) to make scams incredibly convincing, eliminating the "Nigerian Prince" typo giveaways.

Automated Campaigns: AI can automate the generation and distribution of thousands or millions of unique phishing attempts, scaling attacks exponentially.

Sophisticated Impersonation (Deepfakes):
Deepfake Audio/Video: AI enables criminals to create highly realistic deepfake audio and video of executives, family members, or public figures. This is used in "CEO fraud" or "grandparent scams" where a cloned voice or video call convinces victims to transfer money urgently. (e.g., the $25 million Hong Kong deepfake scam).

Synthetic Identities: AI can generate entirely fake personas with realistic photos, bios, and even documents, which can then be used to open fraudulent bank accounts, apply for loans, or bypass KYC checks.

Advanced Malware and Evasion:
Polymorphic and Evasive Malware: AI can be used to develop malware that adapts and changes its code in real-time to evade traditional antivirus software and intrusion detection systems.

Automated Vulnerability Scanning: AI can rapidly scan networks and applications to identify vulnerabilities (including zero-days) that can be exploited for attacks.

Automated Credential Stuffing and Account Takeovers:
AI can automate the process of trying stolen usernames and passwords across numerous websites, mimicking human behavior to avoid detection by bot management systems.

It can analyze breached credential databases to identify patterns and target high-value accounts more efficiently.

Enhanced Fraud Infrastructure:
AI-powered chatbots can engage victims in real-time, adapting their responses to manipulate them over extended conversations, making romance scams and investment scams more effective and scalable.

AI can optimize money laundering routes by identifying the least risky pathways for illicit funds.

The AI Arms Race:
The reality is that AI will be used for both. The fight against cyber fraud is becoming an AI arms race, where defenders must continually develop and deploy more advanced AI to counter the increasingly sophisticated AI used by attackers.

For individuals and organizations in Taiwan, this means:
Investing in AI-powered security solutions: Banks and large companies must use AI to fight AI.

Continuous Learning: Everyone needs to stay informed about the latest AI-powered scam tactics, as they evolve rapidly.

Focus on Human Element: While AI can detect patterns, human critical thinking, skepticism, and verification remain essential, especially when faced with emotionally manipulative AI-generated content.

Collaboration: Sharing threat intelligence (including AI-driven fraud methods) between industry, government, and cybersecurity researchers is more critical than ever.

The future of cyber fraud will be heavily influenced by AI, making the landscape both more dangerous for victims and more challenging for those trying to protect them.

How can banks and online platforms detect and prevent fraud in real-time?

Banks and online platforms are at the forefront of the battle against cyber fraud, and real-time detection and prevention are crucial given the speed at which illicit transactions and deceptive communications can occur. They employ a combination of sophisticated technologies, data analysis, and operational processes.

Here's how they detect and prevent fraud in real-time:
I. Leveraging Artificial Intelligence (AI) and Machine Learning (ML)
This is the cornerstone of modern real-time fraud detection. AI/ML models can process vast amounts of data in milliseconds, identify complex patterns, and adapt to evolving fraud tactics.

Behavioral Analytics:
User Profiling: AI systems create a comprehensive profile of a user's normal behavior, including typical login times, devices used, geographic locations, transaction amounts, frequency, spending habits, and even typing patterns or mouse movements (behavioral biometrics).

Anomaly Detection: Any significant deviation from this established baseline (e.g., a login from a new device or unusual location, a large transaction to a new beneficiary, multiple failed login attempts followed by a success) triggers an immediate alert or a "step-up" authentication challenge.

Examples: A bank might flag a transaction if a customer who normally spends small amounts in Taipei suddenly attempts a large international transfer from a location like Nigeria or Cambodia.

Pattern Recognition:
Fraud Typologies: ML models are trained on massive datasets of both legitimate and known fraudulent transactions, enabling them to recognize subtle patterns indicative of fraud. This includes identifying "smurfing" (multiple small transactions to avoid detection) or links between seemingly unrelated accounts.

Adaptive Learning: Unlike traditional rule-based systems, AI models continuously learn from new data, including newly identified fraud cases, allowing them to adapt to evolving scam techniques (e.g., new phishing email patterns, synthetic identity fraud).

Real-time Scoring and Risk Assessment:
Every transaction, login attempt, or user action is immediately assigned a risk score based on hundreds, or even thousands, of variables analyzed by AI/ML models.

This score determines the immediate response: approve, block, flag for manual review, or request additional verification.

Generative AI:
Emerging use of generative AI to identify fraud that mimics human behavior. By generating synthetic data that models legitimate and fraudulent patterns, it helps train more robust detection systems.

Conversely, generative AI is also used by fraudsters (e.g., deepfakes, sophisticated phishing), necessitating continuous updates to detection models.

II. Multi-Layered Authentication and Verification
Even with AI, strong authentication is critical to prevent account takeovers.

Multi-Factor Authentication (MFA/2FA):
Requires users to verify their identity using at least two different factors (e.g., something they know like a password, something they have like a phone or hardware token, something they are like a fingerprint or face scan).

Risk-Based Authentication: Stricter MFA is applied only when suspicious activity is detected (e.g., login from a new device, high-value transaction). For instance, in Taiwan, many banks require an additional OTP for certain online transactions.

Device Fingerprinting:
Identifies and tracks specific devices (computers, smartphones) used to access accounts. If an unrecognized device attempts to log in, it can trigger an alert or an MFA challenge.

Biometric Verification:
Fingerprint, facial recognition (e.g., Face ID), or voice authentication, especially for mobile banking apps, provides a secure and convenient layer of identity verification.

3D Secure 2.0 (3DS2):
An enhanced authentication protocol for online card transactions. It uses more data points to assess transaction risk in real-time, often without requiring the user to enter a password, minimizing friction while increasing security.

Address Verification Service (AVS) & Card Verification Value (CVV):

Traditional but still vital tools used by payment gateways to verify the billing address and the three/four-digit security code on the card.

III. Data Monitoring and Intelligence Sharing
Transaction Monitoring:

Automated systems continuously monitor all transactions (deposits, withdrawals, transfers, payments) for suspicious patterns, amounts, or destinations.

Real-time Event Streaming:
Utilizing technologies like Apache Kafka to ingest and process massive streams of data from various sources (login attempts, transactions, API calls) in real-time for immediate analysis.

Threat Intelligence Feeds:
Banks and platforms subscribe to and share intelligence on emerging fraud typologies, known malicious IP addresses, fraudulent phone numbers, compromised credentials, and scam tactics (e.g., lists of fake investment websites or scam social media profiles). This helps them proactively block or flag threats.

Collaboration with Law Enforcement: In Taiwan, banks and online platforms are increasingly mandated to collaborate with the 165 Anti-Fraud Hotline and law enforcement to share information about fraud cases and fraudulent accounts.

KYC (Know Your Customer) and AML (Anti-Money Laundering) Checks:

While not strictly real-time fraud detection, robust KYC processes during onboarding (identity verification) and continuous AML transaction monitoring are crucial for preventing fraudsters from opening accounts in the first place or laundering money once fraud has occurred. Taiwan's recent emphasis on VASP AML regulations is a key step.

IV. Operational Procedures and Human Oversight

Automated Responses:
Based on risk scores, systems can automatically:

Block Transactions: For high-risk activities.

Challenge Users: Request additional authentication.

Send Alerts: Notify the user via SMS or email about suspicious activity.

Temporarily Lock Accounts: To prevent further compromise.

Human Fraud Analysts:
AI/ML systems identify suspicious activities, but complex or borderline cases are escalated to human fraud analysts for manual review. These analysts use their experience and judgment to make final decisions.

They also investigate new fraud patterns that the AI might not yet be trained on.

Customer Education:
Banks and platforms actively educate their users about common scam tactics (e.g., investment scams, phishing, impersonation scams) through apps, websites, SMS alerts, and public campaigns (e.g., Taiwan's 165 hotline campaigns). This empowers users to be the "first line of defense."

Dedicated Fraud Prevention Teams:
Specialized teams are responsible for developing, implementing, and continually optimizing fraud prevention strategies, including updating risk rules and ML models.

By integrating these advanced technologies and proactive operational measures, banks and and online platforms strive to detect and prevent fraud in real-time, reducing financial losses and enhancing customer trust. However, the cat-and-mouse game with fraudsters means constant adaptation and investment are required.

Did You Know Social Media Platforms Often Promote Racism in Their Algorithms?
Algorithms Can Boost Hate, Suppress Black Voices, and Embed Racial Bias in Moderation

The internet promised to be a borderless space of free expression.
But behind the scenes, invisible walls of bias and discrimination shape what we see—and what is hidden.

Social media platforms use algorithms—complex software that decides what content to show and suppress.
Sadly, these algorithms often amplify racist content while silencing marginalized voices.

-“Even the internet has borders—just invisible ones.”
-How Algorithms Perpetuate Racism
-Amplifying Hate and Misinformation

Content with outrage, anger, and hate tends to get more engagement — so algorithms prioritize it.

This often means racist and xenophobic posts spread faster and wider than messages of unity or justice.

Suppressing Black and Minority Voices-
Black creators and activists report their posts being shadowbanned or removed more frequently.

Hashtags related to racial justice (e.g., #BlackLivesMatter) have been temporarily suppressed or hidden in trends.

Automated moderation systems fail to understand cultural context, leading to unjust takedowns.

Built-In AI Bias-
Algorithms are trained on data that reflects historical and societal biases.

Without careful design, AI can replicate and amplify racial stereotypes or prioritize dominant cultural narratives.

Examples include facial recognition tech misidentifying people of color or language models misunderstanding dialects.

Why It Matters-
Social media shapes public discourse, political mobilization, and cultural trends.

When racism is amplified and Black voices suppressed, inequality deepens online and offline.

The lack of transparency around algorithms hides these biases from public scrutiny.

Toward Ethical Tech and Digital Justice

Transparency: Platforms must reveal how algorithms work and impact marginalized groups.

Inclusive Design: Diverse teams should build and audit AI systems to reduce bias.

Community Control: Users, especially from affected communities, need a say in moderation policies.

Regulation: Governments and civil society must hold tech companies accountable for discrimination.

Digital Literacy: Users should be empowered to recognize and challenge algorithmic bias.

Final Word
The fight against racism must extend to the digital world — because algorithmic injustice affects real lives and real futures.

How Biometric Authentication Is Replacing Traditional Logins

Biometric Authentication is rapidly transforming how we access digital services by replacing traditional logins like passwords and PINs. Leveraging unique physical traits such as fingerprints, facial recognition, or iris scans, biometric authentication offers a more secure and user-friendly alternative. It minimizes the risks of password breaches, phishing, and identity theft. As more organizations adopt this technology, the future of secure and seamless access lies in biometrics. Discover how it's redefining authentication in the digital age.

More Info - https://www.loginradius.com/blog/identity/what-is-biometric-authentication

How Mobile Biometric Authentication Enhances Security

Mobile biometric authentication enhances security by using unique physical traits like fingerprints, facial recognition, or iris scans to verify user identity. Unlike traditional passwords, biometrics are nearly impossible to replicate, reducing the risk of unauthorized access. This method not only improves security but also offers convenience, allowing users to log in quickly without remembering complex credentials. By incorporating biometric authentication, mobile apps can protect sensitive data and provide a more seamless, secure user experience.

More Info - https://www.loginradius.com/blog/identity/what-is-mob-biometric-authentication/

Enhancing Security with Multi-Factor Authentication (MFA)

Enhancing Security with Multi-Factor Authentication (MFA) involves using multiple verification methods to ensure secure user access. MFA requires two or more types of authentication, such as something you know (password), something you have (security token or smartphone), and something you are (biometric verification like fingerprints or facial recognition). By combining these factors, MFA significantly reduces the risk of unauthorized access, protecting sensitive data and systems from cyber threats. Embrace MFA to enhance your security posture and safeguard against potential breaches.

More Info - https://www.loginradius.com/blog/identity/what-is-multi-factor-authentication/

ADOLESCENCE-
Why Proposed Social Media Bans Won't Keep Your Kids Safe.
Social media bans ignore the need for digital literacy and self-regulation
Reviewed by Lybi Ma

KEY POINTS-
Proposed legal bans on social media for kids are based on moral panics, not research.
The preoccupation with social media use ignores the needed skills to safely navigate the digital world.
Not only will the laws be difficult to enforce but verification proposals raise serious privacy issues for sensitive information.
Lawmakers could help kids more by funding digital literacy programs.
Politicians can't get their pens out fast enough to draft laws to regulate social media use by kids. Advertised as "child protection" laws, the proposed bans show how little politicians understand about social media, kids, and interpreting research. The proposals, however, are getting lots of media coverage as politicians make frightening, exaggerated, and often unsubstantiated claims about the negative effects of social media on mental health.

There are legitimate concerns about kids' mental health, but recent reviews of multiple research studies find little direct evidence to support the laser beam focus on technology use (Ferguson et al., 2022) and or screen time (Orben & Przybylski, 2019) to the exclusion of other factors. If improving the mental health of kids is the goal, the proposed bans not only won't get the job done but can cause more harm than good by taking our eyes off what really matters: teaching our kids to be media literate, responsible digital citizens.

Politicians have always used moral panics to generate votes. However well-intentioned, these laws will do nothing to help a child more successfully navigate in a digital world. No amount of restrictions will help kids develop the skills and critical thinking to be safe and productive in the digital space, such as self-regulation, dealing with bullying, ethical behavior, identifying misinformation, recognizing manipulation, understanding social influence, and protecting their privacy. If they want to do some good, why not fund media literacy programs in the schools to teach them what they need to know to have healthy and safe relationships with technology?

Restricting minor kids' unsupervised use of social media makes intuitive sense if you're a parent, especially when they are under the age of 13. These are critical years for cognitive and emotional development. The bans miss the mark by overlooking some fundamental factors and open the door to a host of unintended consequences.

Simply put, the bans will:
Make access more attractive to kids
Give parents a false sense of security
Do nothing to help kids make better decisions
Phones Aren't Phones, They Are Portals to a Social World
Online devices are a portal to the kids' social world. Social media is social currency—it's how kids keep up with pop culture, trends, and their friends. It's how they know what's going on in their world. Pew Research found that the three main uses of mobile devices by teens were: passing the time (90 percent), connecting with others (84 percent), and learning new things (83 percent). Being successful on social media has also become a desired career path, glamourized by the celebrated financial and social success of young Influencers (Kidfluencers) on YouTube, TikTok, and Instagram, many of whom are under 13, whose popularity brings earns big money from sponsors. In a 2019 survey, 86 percent of young Americans wanted to be social media influencers (Morning Consult, 2019).

You Need a Lot of Personal Data to Verify Age and Consent
To enforce the proposed laws, age and identity have to be verified. This raises serious privacy issues around the collection and use of personal information. Utah's laws could require kids, their parents, and other users to upload birth certificates and government IDs, use facial recognition technology, or provide biometric data so that social media platforms can verify age and identity. Talk about a hacker's and marketer's dream database.

Utah's bills are infantilizing to kids and disrespectful to parents. In addition to limiting access to social media between the hours of 10:30 p.m. to 6:30 a.m. by anyone under 18, they require a parent's express consent for minors to sign up for apps like Twitter, Instagram, and TikTok (Evans, 2023, April 4). The bill also would give parents administrative access rights to their kids' direct messages and interactions. If the politicians had read the research, they would see how this would undermine the trust and open communication necessary for providing kids with appropriate guidance (Wisniewski et al., 2022).

User-Centered Approaches: What's Best for the Kids?
Kids' needs for personal space change as they age. Autonomy is an important part of the developmental process as kids transition from a child to an adult and learn how to navigate their world. Like it or not, these kids live in a digital world. Risk-taking is part of the maturational process. Monitoring teens online and imposing authoritarian restrictions and privacy-invasive monitoring negate the developmental needs of teens. Parents are an important source of guidance, but the ultimate goal is helping kids make smarter and safer decisions, not turning parents into police.

Kang et al. (2022) found that parental restriction and lack of privacy boundaries resulted in backlash behavior, with teens making decisions without considering the risks to themselves and without exercising critical thinking. Yardi and Bruckman (2011) also found that teens are more seek out workarounds and are likely to engage in riskier behaviors to avoid parental observation, rules, and technology constraints.

If safety and well-being are the primary concerns of parents, empirical evidence shows that a more open and trust-based approach works best. Considering the needs and desires of the kids, rewarding positive behaviors, raising risk awareness, and negotiating age-appropriate online boundaries are the most effective at preparing kids to use technology well and become good digital citizens (Wisniewski et al., 2022). Teaching media literacy and digital citizenship is, admittedly, a lot more work than hoping the government can regulate the problem away.

Parents Are Important Role Models
Don't underestimate the importance of parental guidance. A study of nearly 4,000 teens found that 65 percent of the kids had positive parental communication, and these kids were more likely to have a healthy relationship with technology, greater well-being, and a better positive body image. Parental involvement included communication, parental attention to their own technology and social media use, and rules focused on content and activity rather than screen time. The 37 percent of remaining kids deemed 'at-risk' had screentime-based rules or no rules at all and, more importantly, no parental involvement.

Whether or not these laws can be enforced, or if the number of exclusions by powerful lobbyists makes them meaningless, there is no evidence that the proposed bans will achieve the intended goals. Instead of focusing exclusively on social media platforms, we need to teach kids age-appropriate, essential skills to be safe online without risking privacy or undermining their autonomy. The answer lies in creating user-centered approaches, such as teaching media literacy, negotiating age-appropriate boundaries, and rewarding positive behaviors, to effectively prepare children to become good digital citizens.