LinkedIn Redesign
Solving for scams and identity theft
The Challenge
With the digital trust that comes with using LinkedIn among its ~645 million users, scam and identity theft frequencies are going up.
LinkedIn has numerous resources on scam and fraudulent activity prevention on their website. In 2019, the platform banned 30 million scam accounts.
PROBLEM:
While this is a step towards the solution, the approach is only counteractive and not preventative. That means, a user will find out they have been scammed only after the event and only then is that account likely to be taken down.
LinkedIn has numerous resources on scam and fraudulent activity prevention on their website. In 2019, the platform banned 30 million scam accounts.
PROBLEM:
While this is a step towards the solution, the approach is only counteractive and not preventative. That means, a user will find out they have been scammed only after the event and only then is that account likely to be taken down.
The Strategy
To better understand the route of getting scammed and other fraudulent activities, I collected data on the recurrent features of scammers' profiles and their notable behaviours.
Informed by the existing problem among users on LinkedIn, I concluded that the best solution would be features within the app that inspire users to be cautious when providing sensitive data to new connections/messages.
Informed by the existing problem among users on LinkedIn, I concluded that the best solution would be features within the app that inspire users to be cautious when providing sensitive data to new connections/messages.
Value of this redesign
Emphasizing that tackling scammers and other fraudulent activity shouldn't just be a backend event. Showing through features that this issue is linked with the experience of the end users as well.
- Timeline: 1 Week
- Team: Me, myself & I
- Tools: Figma, Miro
Designing the experience
Recreating reusable assets from LinkedIn's UI
Smart Search
This feature is nested inside "Open to Opportunities". This features is targetted towards active job seekers. The main function is to help users recognize scams before it occurs.
Smart Search Scan- Use Case
Utilizing Google reverse imaging and Grammarly API to check the authenticity of a profile. This is useful because a large portion of scammers' profiles utilize stock photos and have a trend of bad grammar & spelling errors.
Prompted when a possible scammer messages a person with an offer or opportunity.
Prompted when a possible scammer messages a person with an offer or opportunity.
Recognizing identity theft
Scammers often use another user's profile picture, if they aren't using a stock photo. Here the interface shows a user getting notified when their profile is being used by someone else.
What if we wasted their time instead?
Scammers always find another way to scam people despite getting caught.
What if instead of just banning or deleting their profile, we could occupy their time, with a seemingly interested person (AKA a LinkedIn bot made to answer wisely and with interest). This feature was inspired by Youtuber and grey hat hacker, Jim Browning.
What if instead of just banning or deleting their profile, we could occupy their time, with a seemingly interested person (AKA a LinkedIn bot made to answer wisely and with interest). This feature was inspired by Youtuber and grey hat hacker, Jim Browning.
Here's more numbers on scams on LinkedIn
Prototype clickthrough of all experiences available here.
My drive for this project was to enable an approach of RECOGNIZE, REJECT, REPORT for the end user
stay safe, digitally!
Other work:-
Copyright 2021 – All Rights Reserved to Paloma Ningthoujam
