Optimizing Batch AML Screening with Didit and AWS Batch

Cost-Effective ComplianceLeverage AWS Batch's serverless computing model to pay only for the resources consumed during large-scale AML screening operations, drastically reducing infrastructure overhead.

Enhanced Performance and ScalabilityProcess millions of AML checks efficiently by distributing tasks across a highly scalable and fault-tolerant AWS Batch environment, ensuring rapid turnaround times.

Seamless Asynchronous ProcessingUtilize Didit's asynchronous AML Screening API to submit large batches of data without waiting for individual responses, optimizing API call efficiency and system throughput.

AI-Native, Modular SolutionsDidit provides an AI-native, modular AML Screening solution that integrates effortlessly with existing compliance workflows, offering real-time risk detection against 1300+ global watchlists and configurable risk thresholds.

The Challenge of Batched AML Screening

For many businesses, particularly those in financial services, e-commerce, or online gaming, the need to perform Anti-Money Laundering (AML) screening isn't just about real-time onboarding. There's often a significant requirement for batch screening existing customer bases, periodic re-screening, or processing large historical datasets. This can be a daunting task, as traditional synchronous API calls for millions of records can be slow, resource-intensive, and incredibly expensive. The core challenge lies in balancing the need for comprehensive compliance with the demand for cost-efficiency and high performance. Manual processes are out of the question for such volumes, and even automated systems can struggle without the right architecture. Businesses need a solution that can handle massive loads without breaking the bank or causing significant operational delays.

Introducing Didit's Asynchronous AML API

Didit, as an AI-native, developer-first identity platform, understands these challenges. Our AML Screening product is designed not just for real-time checks but also for efficient batch processing. A key component of this efficiency is our asynchronous API. Unlike synchronous calls, where your system waits for a response for each individual screening request, an asynchronous approach allows you to submit a large volume of requests and receive notifications or retrieve results later. This is crucial for batch operations, as it prevents your application from being bottlenecked by API latency. Didit's AML Screening screens users against 1300+ global sanctions, PEP, and watchlist databases, providing a two-score risk system with configurable compliance thresholds. This means you can submit an entire list of users for screening, and Didit will process them in the background, providing detailed reports that include hit details, risk scores, match scores, PEP matches, sanctions data, and adverse media intelligence.

Leveraging AWS Batch for Scalable Processing

To truly unlock the potential of Didit's asynchronous AML API for large batches, combining it with a powerful, scalable compute service is essential. This is where AWS Batch comes in. AWS Batch is a fully managed service that enables developers, scientists, and engineers to easily run hundreds of thousands of computing jobs on AWS. It dynamically provisions the optimal quantity and type of compute resources (e.g., CPU or memory optimized instances) based on the volume and specific resource requirements of your batch jobs. By using AWS Batch, you can:

• Automate Resource Provisioning: AWS Batch handles all the heavy lifting of managing your compute infrastructure, from EC2 instances to containers, scaling up and down as needed.

• Maximize Cost Efficiency: Pay only for the compute resources consumed by your jobs. AWS Batch can leverage Spot Instances, offering significant cost savings for fault-tolerant workloads like batch AML screening.

• Enhance Reliability: Distribute your workload across multiple compute instances, ensuring that even if one instance fails, your overall batch job continues to progress.

• Simplify Job Management: Define your jobs as Docker containers, making them portable and easy to manage. AWS Batch takes care of scheduling, monitoring, and retrying failed tasks.

Imagine having a list of a million customers to re-screen. Instead of sending one request at a time and waiting, you package your data, submit it to an AWS Batch job, which then uses Didit's asynchronous API to process it concurrently across many workers. This dramatically reduces the total processing time and the operational burden.

Implementing a Cost-Optimized Batch AML Workflow

Here's a high-level overview of how you can implement an optimized batch AML screening workflow using Didit and AWS Batch:

1. Prepare Your Data: Consolidate your customer data into a structured format (e.g., CSV, JSON) suitable for batch processing. Ensure it contains necessary fields like full name, date of birth, and country for optimal AML screening by Didit.

2. Develop a Batch Job Application: Create a simple application (e.g., a Python script) that reads a chunk of your prepared data, calls Didit's asynchronous AML Screening API for each record, and stores the session IDs or initial responses. This application will be containerized using Docker.

3. Configure AWS Batch: Define your compute environment (e.g., using EC2 Spot Instances for cost savings), job queues, and job definitions in AWS Batch. Your job definition will point to your Docker image.

4. Submit Batch Jobs: Break your large dataset into smaller, manageable chunks. For each chunk, submit a job to AWS Batch. AWS Batch will then launch container instances, run your application, and process the data.

5. Retrieve Results: Your batch job application can be designed to store the Didit AML screening results (or references to them) in a central location like an S3 bucket or a database. You can then poll Didit's API using the session IDs to retrieve the final, detailed AML reports once processing is complete. Didit's comprehensive AML report includes match information, scoring details (match score and risk score), and adverse media details, crucial for compliance officers.

6. Automate Actions: Based on the AML screening warnings and scores (e.g., POSSIBLE_MATCH_FOUND or high risk scores), automate subsequent actions such as flagging users for manual review or declining transactions, leveraging Didit's configurable verification settings.

This approach transforms a potentially slow and expensive operation into a fast, reliable, and highly cost-effective one, allowing your compliance teams to focus on reviewing actual high-risk cases rather than managing infrastructure.

How Didit Helps

Didit is at the forefront of enabling efficient and compliant identity verification, including robust AML screening. Our platform is designed with a modular architecture, meaning you can easily integrate our powerful AML Screening product into your existing systems and workflows. Didit's AI-native capabilities ensure highly accurate risk assessment and match scoring, minimizing false positives and focusing your resources on true risks. We offer AML Screening that screens against over 1300 global sanctions, PEP, and watchlist databases in real-time, delivering a two-score risk system (match score and risk score) with fully configurable compliance thresholds. Furthermore, Didit stands out by offering Free Core KYC, allowing businesses to get started without upfront investment, and operates with no setup fees. Our developer-first approach, complete with an instant sandbox and clean APIs, makes integration with services like AWS Batch seamless. Whether it's for initial onboarding or ongoing monitoring, Didit provides the tools to automate trust and orchestrate risk globally and at scale.

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