Crop monitoring, yield prediction, resource optimization.

Compliance & Onboarding Hub Built Securely: From Manual Checks to Automated, Auditable Flows

Intro summary – Client & outcome. A major bank sought to digitalise its corporate‑customer onboarding process while strengthening compliance screening. By adopting an AI‑driven adverse‑media‑screening platform and automating manual checks, the bank reduced its average onboarding cycle time from 12 days to 2 days and cut process time for customers and colleagues by over 50 %, sometimes up to 75 %–80 %.

1 Context & Challenge

The bank’s corporate onboarding involved paper forms, manual adverse‑media searches and repetitive data entry. Cycle times averaged nearly two weeks, and compliance officers struggled to keep pace with growing volumes of applications. FinTech challengers were offering faster digital experiences; to remain competitive, the bank needed to modernise and automate the process.

2 Goal / Success criteria

• Accelerate onboarding. Reduce the mean onboarding cycle from multiple days to a matter of hours while maintaining regulatory compliance.

• Automate risk screening. Replace manual searches with AI‑powered adverse‑media and sanctions screening.

• Improve adaptability. Enable configuration of screening rules and thresholds to adapt quickly to regulatory changes.

3 Approach / Implementation

• Adverse‑media screening API. The bank integrated a third‑party AI service that automatically scans news articles and watch‑lists for negative information about applicants. Settings (thresholds, search strings) are configurable, allowing analysts to refine risk criteria.

• Cloud migration. Customer‑data processing moved off‑premise into a secure cloud environment, enabling scalability and easier integration with digital channels.

• Workflow automation. The onboarding process was redesigned so that information flows seamlessly from application to screening to approval, with audit logs for compliance.

4 Outcomes & Metrics

• Cycle‑time reduction. The mean onboarding cycle time dropped from 12 days to 2 days.

• Process efficiency. Customers and colleagues experienced more than 50 % reduction in process time; in some cases, time savings reached 75 %–80 %.

• Operational flexibility. Analysts can now adjust screening parameters quickly as regulations evolve.

5 Challenges & Lessons Learned

• Balancing speed and risk. Automating adverse‑media screening required careful calibration to avoid false positives and negatives.

• Change management. Staff training and communication were essential to ensure adoption of the new workflow.

• Data governance. Migrating sensitive data to the cloud demanded robust encryption and compliance with privacy regulations.

6 Next Steps / Extensions

The bank planned to expand automation to include KYC/AML screening for smaller business customers and to integrate the onboarding hub with credit‑decisioning models. It also considered using generative AI to summarise due‑diligence findings for relationship managers.

Tools and Architectures Highlighted

This case illustrates the use of adverse‑media screening APIs, configurable risk‑scoring engines, cloud‑based processing, and workflow automation to modernise customer onboarding. Together, these tools provide a secure, auditable and scalable compliance hub.

Disclaimer: The case studies presented here are for illustrative purposes only and are based on publicly available information. They do not represent projects executed by Zynolabs and are intended solely to demonstrate the types of AI solutions and outcomes that could be achieved in comparable scenarios.