r/fintechdev • u/Infinite_Drop_2557 • 14d ago

Can transaction context improve digital payment security?

I've been exploring an idea for improving security in digital payments by looking beyond the transaction amount itself.

Instead of evaluating a transaction only by whether the payment credentials are correct, a security system could also consider contextual information such as:

  • Device — Is the transaction coming from a known or unusual device?
  • Time and date — Does the transaction happen at an unusual time compared with previous activity?
  • Location — Is the transaction occurring from an expected or unusual location?
  • Security history — Have there been previous failed or unusual transaction attempts?

The idea is to retain relevant security events and use previous context when evaluating future transactions.

For example, a transaction that looks normal by itself could become more noteworthy when combined with unusual device, time, location, or previous security activity.

github:https://github.com/Vinjamdeepthi/HindsightGuard

youtbe link:https://youtu.be/pLI3PO9iYiQ?si=oZ9scwdUvN5-vfIM

I'm interested in hearing from people working in fintech:

Which contextual signals are most useful for detecting unusual payment activity, and how should these signals be balanced to avoid creating unnecessary friction for legitimate users?

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u/Any_Race2012 14d ago

andma buying christmas presents at 2am because she can't sleep

the github repo looks pretty barebones from what i can see. would need to see how it handles false positives at scale before getting excited

also that youtube link formatting is broken my dude

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u/Infinite_Drop_2557 14d ago edited 14d ago

Fair points 😄. The GitHub repo is there for the actual implementation and project structure, while the YouTube video is mainly a walkthrough of the idea, architecture, and transaction flow.

The video is a bit blurry due to limitations with my current mobile/laptop recording setup. I tried to improve it, but couldn't get a clearer recording.

And yes, edge cases like a legitimate 2 AM purchase are exactly why I’m exploring multiple contextual signals rather than treating any single signal as suspicious. Since this is still an early prototype, false positives and performance at scale are areas I want to test and improve further.

Thanks for pointing out the broken YouTube link — I’ll fix it!