An Afghan student receives a message offering a scholarship abroad. The message includes a university logo, a formal letter, and a deadline. To secure the place, the student must pay a “registration fee” within a few hours. Everything looks convincing, especially to someone who has spent months searching for an educational opportunity. Yet the offer may be false, and the payment may go directly to a scammer.
This example is hypothetical, but it shows how online fraud works. Criminals use people’s needs, hopes, and fears to encourage decisions that benefit the criminal. A fake job offer targets someone seeking income. A false emergency message pressures someone to help a relative. A copied shopping page attracts a buyer with a low price. Protecting Afghan users requires security systems that recognize these patterns and give people useful help before they lose money or control of their accounts.
Computer and data science can support this effort. Software can examine suspicious links, identify unusual account activity, and detect repeated scam messages. However, these tools must suit the people using them. For Afghanistan, that means clear language, support for Dari and Pashto, careful handling of personal information, and services that remain usable on basic devices and limited connections.
How a Convincing Message Becomes a Financial Loss
Online fraud often begins by borrowing someone else’s identity. A criminal may copy the name and photograph of a trusted person or build a page that resembles a real organization. Familiar images make the message appear safe before the reader has checked its source.
The next step is usually pressure. The victim may be told that an account will close, an offer will expire, or a relative needs immediate help. This pressure reduces the time available for checking. A request that might seem suspicious during a calm conversation can become persuasive when a person feels frightened or excited.
Sometimes the criminal wants a payment. In other cases, the goal is a password or a verification code. Once an account is taken over, the criminal may contact the victim’s friends and ask for money. The message then arrives through an account those friends already recognize.
These situations show why public advice should go beyond telling people to “be careful.” Users need clear actions. A scholarship applicant should confirm an offer through contact details found independently on the institution’s official website. Someone receiving an urgent request from a friend should verify it through another trusted channel. A familiar photograph or account name is not enough evidence.
Fraud prevention must also avoid blaming victims. A person who feels embarrassed may delay reporting, allowing the criminal more time to act. Services should make it easy to ask for help without being mocked or treated as careless.
What a Smarter System Could Notice
A useful security system could combine simple rules with machine learning. Simple rules identify known warning signs, such as a link already confirmed as malicious or repeated failed login attempts. Machine learning examines examples and learns patterns that may help identify new suspicious activity.
Consider a hypothetical payment service. An account normally makes small payments but suddenly attempts several large transfers to unfamiliar recipients after a password reset. Each event could have an innocent explanation. Together, they would give the service a reason to request additional confirmation.
The system should respond according to the level of concern. A moderately unusual action might produce a clear warning. A more suspicious transfer could require another identity check or a short review. Permanent restrictions should require stronger evidence and an appeal process.
Message detection could work in a similar way. A platform might notice that hundreds of accounts are sharing the same payment request and link. It could investigate the pattern rather than judging each message separately. User reports could provide additional evidence, although reports would need checking because people can misuse reporting tools.
Machine learning would not remove the need for human judgment. NIST’s guidance on trustworthy artificial intelligence calls for attention to both false positives and false negatives. In fraud detection, these mean wrongly flagging honest activity and failing to detect dishonest activity. Both errors matter when people depend on a service. AIRC
A useful system must therefore explain uncertainty. An alert should say that an action appears suspicious and describe the next step. It should avoid declaring someone a criminal merely because a model produced a high risk score.
Designing Protection for Afghan Users
Language support would be central to an effective Afghan fraud-detection project. Messages may contain Dari, Pashto, English, or a mixture of languages. Some users may write local languages using Latin letters. A detector trained mainly on formal English could miss important clues or misunderstand ordinary communication.
Researchers would need examples that reflect these differences. They could work with consenting users, service providers, and language specialists to create a carefully reviewed collection of messages. Personal details should be removed where possible. Each example should be checked before being labeled as fraudulent or legitimate.
The training material should include honest messages that resemble scams in some ways. A real employer may discuss payment, and a legitimate university may announce a deadline. If a model learns that every message containing these words is fraudulent, it will create unnecessary problems.
Clear warnings would matter as much as accurate detection. A technical message such as “authentication anomaly detected” tells many users very little. A warning that says, “Someone is trying to sign in from a new device. Was this you?” gives the user a decision they can understand.
Security features should also work with limited data. Text alerts and simple screens could provide essential protection without large downloads. Where voice guidance is offered, it should be optional and designed with privacy in mind, especially on shared phones.
Shared devices require careful design. Services could provide clear logout controls, avoid displaying sensitive information in notifications by default, and explain how to remove saved access. These measures would help users protect accounts without assuming that everyone owns a private smartphone.
Stronger Accounts and Safer Payments
Fraud detection works better when basic account security is already in place. Unique passwords reduce the chance that a password stolen from one service will open another account. Additional identity checks can make account theft harder.
CISA recommends multifactor authentication and encourages organizations to adopt methods resistant to phishing. This means that services should consider how well an authentication method protects against fake login pages, rather than assuming every extra step offers the same protection. CISA
Afghan service providers could introduce stronger authentication while offering understandable setup instructions and secure recovery options. Recovery deserves attention because users may lose a phone or change a number. An account that is secure but impossible for its rightful owner to recover creates another kind of harm.
Payment services could also place warnings at the moment of greatest risk. A first payment to an unfamiliar recipient might trigger a reminder to verify the recipient independently. A large transfer following an account change could require confirmation through an established channel.
These measures should be tested with users. Too many warnings can become background noise. A short, relevant message at the right moment is more useful than a long warning shown during every routine action.
When Prevention Fails
People need a response process after a suspected scam. A service should help users secure affected accounts, preserve relevant messages, and contact the payment provider quickly. Whether money can be recovered will depend on the payment method and circumstances; no responsible service should promise recovery in every case.
The U.S. Federal Trade Commission advises scam victims to contact the company used to send the money and ask whether the transaction can be reversed. This is general consumer guidance, and Afghan users would need to contact their own relevant bank, transfer service, or platform through verified channels. Consumer Advice
Victims also need protection from a second scam. The FTC warns that criminals may offer to recover lost money in exchange for a fee. A person desperate to recover savings may be especially vulnerable to such a promise. Consumer Advice
Reporting tools should request only the information needed to investigate. Access to reports must be limited, and organizations should set rules for storing and deleting them. A fraud-prevention database containing identity documents and private conversations could itself become a target.
A practical project could begin with one service and a narrow goal, such as detecting suspicious payment links. Developers could compare a basic rules system with a machine-learning model, test both on examples kept separate from training, and review mistakes with language specialists. Testing should also use later examples to check whether performance holds as scams change.
Expansion should depend on results: fewer missed scams, manageable false alerts, quick reviews, and users who understand the warnings. The number of accounts blocked would be a poor measure on its own.
Conclusion
Afghan users would benefit from security systems that recognize suspicious behavior, communicate clearly, and provide timely support. Machine learning could help detect patterns across messages and transactions, while stronger account protection could prevent some attacks from succeeding.
A sensible starting point is a limited, carefully tested service with Dari and Pashto support, minimal collection of personal data, and human review of difficult cases. Users should have a clear way to challenge mistakes and report suspected fraud. The aim is to help people study, work, shop, and communicate online with fewer losses and greater confidence in the services they use.
Mohammad Rahim Hakimi
References
Cybersecurity and Infrastructure Security Agency. (n.d.). Require multifactor authentication.
https://www.cisa.gov/audiences/small-and-medium-businesses/secure-your-business/require-multifactor-authentication
Federal Trade Commission. (n.d.). Refund and recovery scams.
https://consumer.ftc.gov/articles/refund-and-recovery-scams
Federal Trade Commission. (n.d.). What to do if you were scammed.
https://consumer.ftc.gov/articles/what-do-if-you-were-scammed
National Institute of Standards and Technology. (2023). AI risks and trustworthiness. In Artificial Intelligence Risk Management Framework.
https://airc.nist.gov/airmf-resources/airmf/3-sec-characteristics/