Most cybersecurity education for senior citizens fails at the moment it matters most.
People attend seminars, read PDF guides, and memorise lists of warning signs. Then a phone call comes through claiming to be from their bank, demanding immediate action, and every bullet point disappears under pressure. Knowing what a phishing attack looks like in theory doesn't prepare someone to recognise one when it arrives in their inbox at 9 PM with urgent language and official-looking logos.
Daksh Sawhney, a 17-year-old student at Inventure Academy, approached fraud prevention as a training problem, not an information problem. If pilots practise emergency scenarios in flight simulators before encountering real turbulence, why shouldn't people practise identifying scams before risking actual money?
That logic became ScamSlayer, a simulation-based fraud detection platform now deployed across more than 20 senior care facilities in Bengaluru, protecting over 1,000 users.
The app works through realistic scenario training. Users encounter fake UPI payment requests, phishing links disguised as government portals, and SMS messages designed to create panic. Each scenario requires a decision: approve the transaction, verify the sender, ignore the message, or report the attempt. The system tracks performance (which red flags get missed, where hesitation occurs, and which scam types prove hardest to identify) and adjusts difficulty accordingly.
Daksh designed the platform around a simple principle: people learn by doing, not by reading. Instead of listing warning signs in bullet points, ScamSlayer forces active decision-making under conditions that mirror real fraud attempts. After completing the training sequence, users can identify verification steps, distinguish legitimate communications from impersonation attacks, and recognise pressure tactics designed to bypass rational judgement.
The technical architecture logs error patterns across the user base. If 60% of users in a facility fall for a specific scam variant, that scenario gets additional training focus. If a particular instruction set causes confusion, the language gets simplified. The feedback loop is continuous: real-world failure modes inform the next iteration of the training modules.
Deployment started with pilot testing at three facilities. Daksh ran live sessions, observed where the interface caused confusion, and revised accordingly. Button sizes increased. Technical language disappeared. The app assumed zero prior smartphone fluency and built foundational digital literacy skills into the fraud-detection curriculum.
Within six months, the platform expanded to constituency-wide coverage. Administrators reported measurable results: residents who had previously avoided online banking began using government portals independently. Reported scam attempts declined. The shift wasn't just behavioural. It was structural. Users weren't just avoiding scams; they were developing the recognition skills to identify new threat variants on their own.
Daksh continues active development. New scam types get added to the training library as they emerge in the wild. The error-logging backend identifies confusion patterns and flags interface friction points for revision. Each deployment generates performance data that feeds back into the adaptive difficulty algorithm.
ScamSlayer now functions as onboarding infrastructure at multiple senior care homes. New residents complete the training modules as part of orientation programming. The platform has scaled from a single-developer project to a deployed system with measurable impact across a user base exceeding 1,000 people.
The work continues because the threat landscape evolves faster than static educational materials can address. Digital fraud isn't solved with awareness. It's solved with skill-building infrastructure that meets people where they are and trains them to recognise threats in real time. That's what ScamSlayer does.
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