Case Study: Deploying Data Architecture for a KYC Program in a Fintech Company
Client
A growing fintech company focused on offering digital banking, investment, and financial marketing services.
Challenge
The fintech company needed a robust Know Your Customer (KYC) program to comply with regulatory requirements, reduce fraud, and enhance customer trust. The existing data systems lacked the necessary integration, scalability, and real-time processing capabilities to handle the influx of customer data and regulatory checks. The company needed a scalable, secure, and efficient data architecture to support the KYC process, including customer identity verification, risk assessments, and compliance reporting.
Solution
Requirements Gathering and Analysis
Regulatory Compliance: Ensure that the system adheres to AML (Anti-Money Laundering) regulations and GDPR for data privacy.
Real-Time Processing: Develop capabilities for near real-time identity verification and risk scoring.
Scalability: Plan for handling large volumes of customer data as the customer list grows.
Data Security: Implement encryption and strict access controls to safeguard sensitive customer data.
Audit Process: Systematic and ongoing review of customer data to ensure compliance and AML regulatory requirements.
Architecture Design
Central Data Hub: A cloud-based data lake was set up to store structured and unstructured customer data from various sources (e.g., user submissions, third-party verification services).
ETL Pipelines: Automated ETL (Extract, Transform, Load) pipelines were built using Fivetran and DBT to aggregate and process data from multiple sources, such as customer onboarding forms, government databases, and third-party KYC vendors.
Real-Time Data Processing: Enabled a real-time streaming of customer data using Apache Kafka, allowing immediate validation and risk scoring to improve the customer experience.
Identity Verification API: Integration with third-party APIs (e.g., government ID verification) allowed seamless identity checks during customer onboarding.
Risk Engine: A machine learning-based risk engine was integrated into the architecture to analyze historical data and flag high-risk customers automatically and potentially cross-sell additional services based on prospect profiles.
Deployment
CI/CD Pipeline: A continuous integration and deployment pipeline was implemented allowing for seamless updates to the program without downtime.
Cloud Infrastructure: AWS services (S3, Redshift, Lambda, RDS) were used for storage, processing, and database management, ensuring scalability and cost-efficiency.
Monitoring and Logging: A comprehensive logging system was set up using ELK Stack (Elasticsearch, Logstash, and Kibana) for real-time monitoring and auditing of KYC processes.
Manual Review Process: Assisted in establishing a manual review team for outlier cases. By digitizing the process in Hubspot, manual reviewers were highly efficient and able to clear cases within 48 hours.
Results
Improved Compliance: The new architecture ensured compliance with KYC and AML regulations through automated and real-time checks.
Enhanced Customer Experience: Real-time data processing enabled instant feedback for customers, reducing the onboarding time from days to in most cases minutes.
Scalability: The cloud-native architecture provided the scalability needed to support rapid user growth.
Operational Efficiency: Automation reduced the need for manual intervention in risk assessments and compliance reporting, saving time and resources for the operations team.
Conclusion
This deployment resulted in a streamlined KYC process that was both compliant with regulations and scalable to meet future demands as the fintech company grew. In addition, the company was able to harvest application data for informing potential new product prospecting and market targeting activities.