IMM (Institutional Money Market) is a mutual fund that invests in highly liquid instruments, cash, and cash equivalents. IMM funds are large financial intermediaries that are crucial to financial stability in the US. Due to its criticality, IMM funds are highly regulated under the security laws, notably Rule 2a-7, Which states that during market stress, fund managers can impose a liquidity fee up to 2% or redemption gates (a delay in processing redemption) if the fund’s weekly liquid assets drop below 30% of its total assets. The liquidity fees and gates allow money market funds to stop heavy redemption in times of market volatility.
Traditional banks use legacy systems and rely on monolithic architectures. Typically, data and business logic are tightly coupled on the same mainframe machines. It’s hard for analysts and fund managers to perform self-service and gather real-time analytics from these legacy systems. They work on the previous nightly report and struggle to keep up with market fluctuations. The slightest modification to the reports on these legacy systems involves vast costs, time, and significant dependency on the software development team. Due to these limitations, analysts and fund managers can’t respond effectively to market trends and face a tremendous challenge in adhering to the regulatory requirements of monitoring market volatility.
Over the last few years, many banks have adopted the cloud. Banks have migrated their legacy workloads to reduce cost, improve their competitive advantage, and address competition from FinTech and startups. As part of the cloud strategy, many mainframe applications got re-platformed or re-architected to a more efficient database platform. However, many opportunities exist in modernizing the application. One such option is to enable self-service to run real-time analytics. AWS offers various services that help such use cases. In this post, we demonstrate how to analyze fund performance visually using AWS Glue Studio and QuickSight in a self-service fashion.
The aim of the post is to assist operations analysts and fund managers in self-service their data analysis needs without previous coding experience. This post demonstrates how AWS Glue Studio reduces the software development team’s dependency and helps analysts and fund managers perform near-real-time analytics. This post also illustrates how to build visualizations and quickly get business insights using Amazon QuickSight.
Solution overview
Most banks record their daily trading transactions activity in relational database systems. A relational database keeps the ledger of daily transactions that involves many buys and sells of IMM funds. We use the mock trades data and a simulated Morningstar data feed to demonstrate our use case.
The following sample Amazon Relational Database Service (Amazon RDS) instance records daily IMM trades, and Morningstar market data gets stored in Amazon Simple Storage Service (Amazon S3). With AWS Glue Studio, analysts and fund managers can analyze the IMM trades in near-real time and compare them with market observations from Morningstar. They can then review the data in Amazon Athena, and use QuickSight to visualize and further analyze the trade patterns and market trends.
This near-real time and self-service enable fund managers quickly respond to market volatility and apply fees or gates on IMM funds to comply with Rule 2a-7 regulatory requirements.
The following diagram illustrates the solution architecture.
