Stop the misuse of legitimate features: account takeover, credential stuffing, bots, payment and promotion fraud, impersonation and deepfakes, and harmful content on platforms.
Estimated time: 15–29 months · At about 10 hours a week.
Learning path
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Train a simple fraud model on a public transaction dataset and evaluate precision and recall.
Build a graph of accounts sharing devices and payment details and find a fraud ring.
Design step-up rules that cut account takeover without blocking most good users.
Done when you can…
Ship a risk score that reduces fraud with acceptable friction.
Uncover a fraud ring through link analysis.
Intermediate
Bot detection and management
Separate good bots from bad ones, detect automation through behaviour and challenges, and keep up as attackers use real browsers and residential proxies.
Document checks, selfie matching and data verification stop fake accounts, but deepfakes and synthetic identities are improving fast. Layer the checks.
Write a content policy and enforcement guide for a sample platform feature.
Design a verification flow for high-value requests that resists deepfake calls.
Find and document lookalike domains impersonating a sample brand.
Done when you can…
Run a trust and safety process with policy, detection, review and appeal.
Show defences adapted to AI-enabled scams.
Advanced
Trust and safety
Harmful content, harassment, scams between users and child safety are platform integrity problems. Learn policy, detection, review operations and appeals.
Write clear rules, enforce them consistently, give users notice and appeal, and publish transparency reports. Laws such as the EU Digital Services Act now require it.
Generated documents, cloned voices, deepfake video calls and mass-personalised scams. Add verification steps that do not rely on what people can see or hear.
Fraud and abuse teams fight attackers who use a product exactly as designed, just for the wrong ends: logging in with stolen passwords, creating fake accounts, abusing promotions, scamming users or posting harmful content. There is often no vulnerability to patch, only behaviour to detect.
The field combines security engineering, data science and operations: building signals and risk scores, designing friction that stops abusers but not customers, running investigations, and working with policy and legal on platform rules. Trust and safety extends it to harmful content and user protection.
Why it matters
Fraud and abuse cost companies and their customers billions, and AI is making scams and fake identities cheaper. Every product with accounts, payments or user content needs people who can keep it trustworthy without driving away genuine users.
Typical job roles
EntryFraud analystInvestigates cases and tunes rules.
Mid-levelAnti-abuse engineerBuilds detection signals and defences into products.
Mid-levelTrust and safety specialistHandles harmful content and user protection.
LeadHead of trust and safetyOwns fraud, abuse and platform integrity.