Biometrics | Facial Recognition
Payme By HSBC, Hong Kong
Facial recognition is a biometrics feature enabling users to verify their identity by uploading an ID and undergoing a simple facial scan to ensure the user who registered is the same user of the wallet, to enhance payment security.
TIMELINEJuly 2020 - December 2020
MY ROLE & CONTRIBUTIONResearch owner.
Led user studies (Guerrilla testing, Interview, Usability testing).
Competitor analysis.
Wireframe, Prototype, End to end experience design.
THE TEAMUX UI Designer | Copy Writer | Product Manager | Developer | Vendor
BACKGROUNDNew Regulatory ID&V Requirements
In 2020, HKMA requires all digital products with SVF licences in Hong Kong to implement new biometric authentication, i.e. facial recognition to verify user’s identity.
This means, every user will be given a different account status, determined by the level of identification and verification (ID&V) users performed. Users with different ID&V levels will impact the top up and transaction amount users have per month.
OBJECTIVEBalance the Needs Between Customer, Business & Government
HKMA: Regulatory Compliance
Deliver facial recognition by July 2021.
Customer
Understand customer’s attitude, underlying concerns, expectations and trade-off of sharing facial data with PayMe.
PayMe
Design a journey with the objective of maintaining monthly active rate and limiting drop off for existing and new users.
CHALLENGESSecurity vs. Privacy
Hong Kongers value privacy. As facial recognition was initiated by HKMA (as part of the Hong Kong Government), wehave the responsibility to implement this feature to enhance the safety and security of our product for Hong Kong residents, as a bank.
Sensitive Timing
On the other hand, the request was initiated during a sensitive period, i.e. Anti Extradition Law movement in 2019. This required careful consideration of potential user hesitancy in sharing personal data, making the task of gaining user trust and acceptance more complex.
DESIGN PROCESS Competitor analysis 📖
Lightning Research 🗣️
Workshops 🎨
User Flow, Wireframe, Prototype 🎨
Interview & Usability Testing & Iteration 🗣️
Tech Development 👩💻
RESEARCH
Objective
Understand users' experiences and attitudes towards facial recognition technology, focusing on what decision-making factors for sharing facial data with PayMe, along with their concerns and expectations.
METHODOLOGY Lightning Research
Due to COVID-19, I led a 'Lightning Research' by asking Design, Product, and Tech teams with Q&As for remote interviews with family and friends, Engaging with their circles, we gathered data from a diverse pool and compiled our insights in Confluence.
KEY FINDINGS
🤔 Understanding and Necessity
There's a gap in user awareness about the purpose and advantages of facial recognition, with many feeling current security measures are sufficient.
🔍 Voluntary Adoption
Facial recognition should be introduced as a choice, rather than force.
🔒 Data Privacy Sensitivity
Users are wary of how facial recognition technology handles their data, underlining the need for clear communication on its safe and intended use.
🤳🏾 Selfie vs. Video
Preference for selfies indicates a concern for privacy, with video capture perceived as overly intrusive.
DESIGN
Design Goals
Simple
Simple, easy, efficient interactions.
Purposeful
Focus on meaningful enhancements that justify the introduction of new technology.
DESIGN CONSIDERATION
Prioritise user reassurance through transparent communication about data use and protection.
To balance user convenience and security enhacement, we carefully considered when and how facial recognition technology is introduced within the PayMe user experience.
FACIAL RECOGNITION INTERGRATION🤳🏾 Mode of Image Capturing
One time selfie verification
🔔 Timing
Ensure FR integration does not disrupt peak usage periods (Lunch/ Dinner).
Existing Users: In-app banner, top up.
New Users: Onboarding, presented as part of the initial PayMe experience.
⭐ Value Exchange
The decision-making process involves accessing the intention, rationale and value exchange of the request, we should articulate the value exchange that customer can receive in return for exchanging their facial data, above and beyond what we currently offer. To be explored in next research.
Creditable & Reassured
🤝 Adoption
Optional for user acceptance
🙇♂️ User Education
To prevent drop off and error, prepare guidlines detailing:
Accessory requirement, lighting requirement, stable environment.
Things needed (HKID).
T&Cs.
Estimated time and steps.
RESEARCH Interview & Usability Testing 2
When the prototype is ready, I conducted virtual Interview & Usability Testing with PayMe Insiders, to explore How can facial recognition be designed as an acceptable verification process for users, meanwhile meeting regulatory requirements.
KEY FINDINGSPayMe as a ‘Small Wallet’ does not justify the need for users to share their facial data.
Users view PayMe as suitable for low-value transactions without needing facial data sharing, finding existing ID&V measures adequate.
Increased app usage could shift this perception, potentially justifying enhanced security like facial verification.
KEY FINDINGSMisuse of data is the biggest concern and customers are looking for actionable promises of how data will be safeguarded.
Misuse of data is a primary user concern, with a focus on clear, enforceable data protection promises.
Users are wary of both deliberate misuse (improper storage, sharing, ownership) and accidental (breaches, theft).
KEY FINDINGSDespite T&Cs are considered as must-haves, users do not read, but scan.
Users tend to scan rather than thoroughly read terms and conditions, suggesting a need for more engaging methods to communicate this critical information.
KEY FINDINGSExclusive benefits are the key motivation for early adopters to try facial recognition.
Customers expect tangible long-term benefits for providing their facial data, beyond current offerings.
These include both monetary incentives (coupons, cashback, referrals) and service enhancements (higher transaction limits, account recovery options).
RESULTSIncrease top up limit for exclusively for users who performed facial recognition.
Launched in July 2022, PayMe launched Facial recognition, allowing users to scan their ID card and face for verification. Once the identity is confirmed, the users account will be upgraded and the monthly limit will also be increased correspondingly.
Top-up: $30,000 - $50,000
Annual payment and collection limit: $100,000 - $300,000
Annual transfer to bank: $100,000 - $500,000
DESIGNNew Entry Point
There are 3 entry points we direct users to enter the biometrics journey:
New users
Onboarding
Existing users
In-app banner
Top-up
DESIGNVerification
Currently, every user will be given a different account status: 1) Verified, 2) Unverified, determined by the level of identification and verification (ID&V) users performed. Users with different ID&V levels will impact the top up and transaction amount users have per month.
DESIGNSimple Instruction, Fast scan, Secured system.
We attempted to make the facial verification journey as simple and seamless as possible. Once user entered the journey, the will be provided Tip about things to be aware of, as well as video tutorial walkthrough about what to do. The journey is relative fast to be completed.
FEATURE DEMOTo prevent drop off and error, we prepared educational content (i.e. Video) to walk through users the things they need to be aware of.