Case Study: AI-Powered Customer Onboarding

Financial Services

AI-Powered Customer Onboarding

The Challenge: Overcoming Onboarding Bottlenecks and Rising Risks

A prominent financial services firm, dealing with high volumes of new account applications, faced significant operational hurdles in its customer onboarding process. Their existing workflow relied heavily on manual data entry and verification for critical Know Your Customer (KYC) and Anti-Money Laundering (AML) checks. This manual approach was not only slow and resource-intensive but also prone to inconsistencies and human error, leading to compliance gaps. Customers frequently experienced frustrating delays, sometimes waiting days for account activation, which resulted in a high application drop-off rate, particularly among digitally savvy demographics. Furthermore, the increasing complexity of regulatory requirements meant that the manual nature of compliance checks exposed the firm to substantial potential risks, including hefty fines and reputational damage, while straining already limited operational resources.

Our Solution: Intelligent Automation with AI and Cloud Data

Lydatum architected and deployed a comprehensive, AI-powered onboarding platform, leveraging the robust and scalable infrastructure of Amazon Web Services (AWS). The goal was to automate tedious processes, enhance accuracy, and provide a seamless customer experience. The core components included:

• Automated Document Intake and Verification: We implemented an intelligent document processing pipeline using Amazon SageMaker. Machine learning models trained on diverse document types performed Optical Character Recognition (OCR) to accurately extract data from identification documents (passports, driver's licenses, utility bills). Advanced computer vision algorithms then automatically verified document authenticity, checked for tampering, and cross-referenced extracted information against global sanctions lists and Politically Exposed Persons (PEP) databases in near real-time, drastically accelerating the KYC/AML verification stages.
• Centralized Real-time Data Hub: Snowflake was integrated as the central, cloud-native data warehouse. This provided a single source of truth, consolidating onboarding data from various customer touchpoints (web forms, mobile apps, internal systems). It enabled real-time tracking of application statuses through intuitive dashboards, allowed for proactive identification of bottlenecks in the workflow (e.g., specific document types causing delays), and facilitated the automated generation of comprehensive compliance reports for auditors.
• Streamlined Digital Workflow: The AI-driven verification and centralized data platform orchestrated a significantly improved workflow. Many previously manual steps, such as data entry duplication and checklist reviews, were automated using AWS Lambda and Step Functions. This created a smoother, faster, and more transparent onboarding journey, providing customers with timely updates and reducing the manual workload for internal onboarding and compliance teams.

The Impact: Measurable Gains in Efficiency, Conversion, and Compliance

The implementation of Lydatum's AI-powered solution yielded transformative and quantifiable results for the financial institution:

Beyond these core metrics, the solution delivered substantial qualitative benefits. The dramatically improved speed and convenience significantly enhanced the overall customer experience, reflected in higher Net Promoter Scores (NPS) and positive feedback. The automation freed up compliance officers and onboarding specialists from repetitive tasks, allowing them to focus their expertise on handling more complex edge cases and providing higher-value customer support. The robust, immutable audit trail captured within Snowflake significantly strengthened the firm's compliance posture, simplifying regulatory reporting and reducing the time required for internal and external audits. The scalable cloud architecture also positioned the firm to easily adapt to future growth and evolving regulatory landscapes.

Technologies Used: Amazon SageMaker, Snowflake, Amazon S3, AWS Lambda, AWS Step Functions, Amazon Textract