Category: Financial Services

In this post, we explain why uncontrolled AI agent development on managed copilot platforms is the next generation of EUC risk, why discovery is the prerequisite that most governance frameworks skip, and how a four-tier classification model can help financial institutions capture the productivity gains of citizen AI without repeating the remediation cycles of the past.

In this post, we explore the security layers you can apply to protect your AWS Payment Cryptography deployment. These layers complement our existing Security Reference Architecture, which provides broader security guidelines for a multi-account environment. This post assumes you have already implemented those guidelines, including AWS IAM Identity Center.

In this post, we show how to design a system of domain specialized AI agents, each mirroring a role in the financial services three lines of defense model. This is a risk governance model most financial institutions run on: the first line (business and technology teams) owns and manages risk day to day, the second line (risk and compliance) sets policy and independently oversees that risk, and the third line (internal audit) provides independent assurance that both are working.

This post documents a working system. Five KYC/KYB checks, each with its own agent architecture, prompt methodology, and failure modes. We built it, ran it, broke it, fixed it, and are publishing the full method so you can build your own.

This post demonstrates on Massive Multitask Language Understanding (MMLU) [Hendrycks et al., 2021], an open-source multiple-choice benchmark accessible via Hugging Face Hub, selecting four business-relevant subjects.

In this blog, we provide a practitioner-focused guide for enabling a new AWS Region in a financial services enterprise AWS environment. We walk through each workstream, from initial governance approval through networking, security, operations, and workload readiness, highlighting the considerations, dependencies, and common pitfalls that teams encounter.

In this post, you’ll learn how financial institutions can use Amazon Bedrock AgentCore and Strands Agents SDK — an open source framework for building intelligent, model-driven AI agents — to automate their complaint management process through a near real-time agentic solution that classifies complaints with defensible rationale, captures evidence at each decision point, tracks SLA compliance, assigns clear remediation ownership, and improves mean time to resolution.

When AWS released Amazon S3 Tables, this calculus changed. In this post, you will learn how Amica reduced their ETL job runtimes by 80% by building a data lake for core insurance data using S3 Tables.

In this post, we present a deployable reference architecture that addresses both challenges simultaneously. We show how Amazon Bedrock AgentCore orchestrates a single AI agent that reasons across unstructured documents in Amazon S3 and structured data in Snowflake.

Learn how through its strategic partnership with Nasdaq and AWS, BYMA (Bolsas y Mercados Argentinos) went live on Nasdaq’s Eqlipse Clearing platform, hosted on AWS — becoming one of the first FMIs in Latin America to run core clearing and settlement workloads in the cloud.