About 65–70% Indians are residents of rural and semi-urban areas, where Digital literacy is seen to be low, internet connectivity is poor and the specifications of smart phone are relatively plain.
- KYC processes are slow and error-prone in digital with low-end devices and poor internet.
- Novice users don’t understand sellers’ complex KYC process.
- Difficulties filing documents and concerns about data security.
- Offilne support and retry mechanisms are must due to connectivity black-holes. Task is to Design a Lightweight KYC Mobile App for Bharat targeting rural and semi-urban India, where smartphone specs are low-end, bandwidth is limited, and users may not be digitally literate.
- Digilocker-based KYC
- Document-based KYC (Aadhaar, PAN, DL, VoterID)
- Face authentication (liveness, facematch checks)
- Integration with other client apps (SDK integration, web-redirection, etc.)
- Offline & retry-friendly mode
- Product Design & UX:
- How will the app work for users with low digital literacy?
- What will the user flow look like (screens, guidance, fail-safes)? Create wireframes.
- What offline/low-network support will you offer?
- Features:
- Suggest and implement 2-3 innovative features to improve trust or ease of use using LLMs.
- How will you prioritise which features to build? Create a Prioritisation Matrix
- Technical Architecture:
- How will you optimize SDK size?
- What trade-offs will you make between on-device vs server-side processing (e.g., facematch, liveness)?
- How will you handle low bandwidth and retry/resume flows?
- Miscellaneous:
- How would you measure the success of the app?
- Any security concerns and how you’ll solve them?
- Any security concerns and how you’ll solve them?
“SecureKYC” is designed to keep digital onboarding simple for rural and semi-urban Bharat. The focus is on a simple interface and support for low bandwidth to accommodate literacy and connectivity challenges, with AI-powered voice-activated and visual assistance.
- Language selection and Easy to use for the user
- Next-gen LLM-powered voice & visual chatbots and local language guides.
- LLM based OCR (Optical Character Recognition) for Digilocker & document processing.
- Offline form filling with option to auto-save.
- Digilocker Integration and the document-based KYC (Aadhaar, PAN, Voter ID).
- Liveness and facematch checks for face authentication.
- Integrations into client apps through SDK & webRedirects
- Alert message to the registered mobile number for the form's interrupted submission.
- “SecureKYC” eliminates the language barrier problem by being multilingual, providing user the benefit to choose the language they are comfortable in.
- Simple Interface with clear icons providing easy navigation.
- Support in local language, anyway between application submission process.
- Intuitive onboarding and tutorials can help the users to understand well.
SecureKYC app leverages Large Language Models (LLMs) to provide personalized user guidance in preferred local language, provide protection against unauthorized document verification, making onboarding easier and more trustworthy also sends alert messages when offline to registered mobile numbers.
- Voice & Visual Chatbots: AI-based voice and visual guides help rural and semi-urban users in local languages and provide voice instructions in sequence for every screen, improving trust and ease of use.
- LLM based OCR for Document Verification: Straight away extract and validate KYC details from uploaded document or Digilocker documents, reducing manual entry and discrepancy.
- Offline and retry friendly mode with LLM and local data storage: Leveraging LLM to logically forecast and process form data in low bandwidth by compressing and interpreting data with the local caching and app design
- Using modular SDKs to load only needed features on demand or when required.
- Implementing model quantization to reduce the size of Machine Learning Model by lowering precision of weight. which intensely shrinks model storage along with minimal loss of accuracy.
- Model pruning to remove unwanted neural network connection, which reduces complexity hence reduces size.
- Employ lazy loading for extensive external dependencies (e.g. document image processing library), loaded when needed at runtime.
- Offload complex processes like identity verification to backend server using APIs.
- Analyzing number of user acquisition and retention rates.
- Successful completion and submission of KYC application.
- Time taken during Verification and error scores during digital onboarding.
- User satisfaction and overall feedback percentage by the users.
- Number of downloads by the users, will eventually represent app success.
- Data privacy: Encryption of sensitive user data using mathematical algorithms
- Device and session authentication: Using multi-factor authentication at the time login other than username and password for extra safety concerns.
- Fraud detection and prevention: Implementing biometric verification like (face liveness, document validation, and face recognition.
- Security via APIs: real-time identity verification and alert messages.
- Offline data privacy: Local data storage along with encryption and auto sync feature.