How Financial Institutions can use AWS to Address Regulatory Reporting
AWS Architecture BlogHow Financial Institutions can use AWS to Address Regulatory Reportingby Jiwan Panjiker, Brian Tang, and Raviteja Yelamanchili | on29 JUN 2021| inAmazon EC2 Container Registry,Amazon SageMaker,Architecture,AWS CodeCommit,AWS CodePipeline,AWS Fargate,Compliance,Financial Services,Intermediate (200)|Permalink|ShareSince the 2008 financial crisis, banking supervisory institutions such as theBasel Committee on Banking Supervision (BCBS)have strengthened regulations. There is now increased oversight over the financial services industry. For banks, making the necessary changes to comply with these rules is a challenging, multi-year effort.Basel IV, a massive update to existing rules, is due for implementation in January 2023. Basel IVstandardizes the approach to calculating credit risk, increases the impact ofrisk-weighted assets (RWAs)and emphasizesdata transparency.Given the complexity of data, modeling, and numerous assumptions that have to be made, compliance under Basel IV implementation will be challenging. Standardization omits nuances unique to your business, which can drive up costs, but violating guidelines will result in steep penalties.This post will address these challenges by outlining a mechanism that facilitates a healthy, data-driven dialogue between banks and regulators to better meet regulatory reporting requirements. The reference architecture will focus on enabling fast, iterative releases with the help of serverless AWS services.There are four key actions to take in order to support this mechanism:Automate data managementEstablish a continuous integration/continuous delivery (CI/CD) pipelineEnable fast, point-in-time audit replaysSet up proactive monitoring and notificationsAutomate data managementDue to frequent merger activity, banks are typically comprised of a web of integrated systems and siloed business units, making it difficult to consolidate data. Under Basel IV guidelines, auditors want banks to provide detailed data in a presentable way.You can tackle this first challenge by establishing a data pipeline as shown in Figure 1. Take inventory of each data source as it is incorporated into the pipeline. Identify the critical internal and external data sources that will be used to populate the initial landing area.Amazon Simple Storage Service (S3)is a great choice for this.Figure 1. Data pipeline that cleans, processes, and segments dataAmazon S3 is a highly available, durable service that is a popular data lake solution. S3 offersWORMstorage capabilities likeS3 Glacier VaultandS3 Object Lockto protect the integrity of your archived data in accordance withU.S. SEC and FINRA rules.Basel IV regulations also require banks to use many attributes to develop accurate credit risk models. The attributes can be a mix of datasets such as financial statements, internal balanced scorecards, macro-economic data, and credit ratings. The risk models themselves can also be segmented by portfolio types, industry segments, asset types and much more.You can split data into different domains and designate data owners with separate S3 buckets. Credit risk model developers, analyst, and data scientists can then use the structure of the S3 buckets to pull together relevant datasets. They can then store the outputs into S3 buckets.To support fast, automated data retrieval, store object metadata in a highly scalable, and queryable database. You can set up Amazon S3 so that an event can initiate a function to populateAmazon DynamoDB. Developers can useAWS Lambdato write these functions using popular languages like Python.WithAWS Glue, you can automate Extract/Load/Transform (ETL) processes to clean and move data to the different S3 buckets. AWS Glue can also support data operations by automatically cataloging your various data sources.Taking on a structured approach will simplify data governance and transparency as the business continues to grow and operate.Establish a CI/CD pipelineAdopt tools that machine learning teams can use to build a streamlined CI/CD solution as demonstrated in Figure 2.Figure 2. An end-to-end machine learning development and deployment pipelineUsing tightly integrated AWS services, your teams can minimize time spent managing tools and deployment processes, and instead, focus on tuning the models and analyzing the results.Amazon SageMakerbrings together a powerful set of machine learning capabilities on the AWS Cloud. It helps data scientists and engineers build insightful models. Figure 2 depicts the high-level architecture and shows howAmazon SageMaker Pipelineshelps teamsorchestrate the automation and deploymentprocesses.The core of the pipeline uses a set of AWS deployment services so that your teams can collaborate and review effectively. WithAWS CodeCommit, your teams can set up git-based repository to store and version models for data processing, training, and evaluation. The repository can also store code and configuration files usingAWS CloudFormationfor deployment. You can useAWS CodePipelineandAWS CodeBuildto create and update a model endpoint based on the approved/reviewed changes.Any updates detected in the AWS CodeCommit repository initiate a deployment whenever a new model version is added to the Model Registry. Amazon S3 can be used to store generated model artifacts, historical data, and models.Enable fast, point-in-time audit replaysFigure 3. Containers offer a lightweight, powerful solution to run audits using historical assetsOne of the main themes of Basel IV is transparency. Figure 3 illustrates a solution to build trust with regulators by allowing them to verify and understand modeling activity.A lightweight application is hosted inAWS Fargateand enables auditors to re-run Basel credit risk models under specified conditions. With AWS Fargate, you don’t need to manually manage instances or container orchestration. Configure the CPU or memory specifications at the task level and set guidelines around scalability for your service. Your tasks then scale up and down automatically, based on demand, and will optimize cost efficiency and availability.Figure 3 shows the following:The application takes inputs such as date, release version, and model type.It then queries DynamoDB with this information.The query will return the data necessary to retrieve model artifacts from previous CI/CD deployments and relevant datasets from historical S3 buckets.Using this information, it can spin up as many containers as needed to run the model.It then stores the outputs in a separate S3 bucket.Auditors will have a detailed trace of all the attributes, assumptions, and data that went into the modeling effort. To streamline this process, the app can also compare the outputs of the historical runs to the recent replay and highlight any significant deviations.Though internal models will be de-emphasized under Basel IV, banks will continue to run internal models as a benchmark against the broader standards.Schedule AWS Fargate tasksto run these models regularly to capitalize on highly performant compute services while minimizing costs.Set up proactive monitoring and notificationsFigure 4. Scheduled jobs can send out notifications using Amazon SNS when certain thresholds are breachedThe last principle is based around establishing an early warning system, enabling financial institutions to proactively stay within acceptable thresholds.With automated monitoring and notifications, banks will be able to respond quickly to potential concerns. For instance, there can be a daily scheduled job that launches containers and runs the models against the latest data. If any thresholds are breached, alerts can be sent out via SMS or email. Operational teams can be subscribed to certain message topics usingAmazon Simple Notification Service (SNS). They can then respond before actual compliance issues emerge.ConclusionWith aWell-Architected approach, AWS helps you control your data, deploy new features, and embrace a serverless approach. This frees you to innovate quickly and address regulatory challenges.You can iterate with new AWS services and bring machine learning to bear on various streams of data to identify high impact pools of value. You can get a clearer picture of the data to make it easier to identify areas where you can reduce RWAs. Using Amazon S3, you can turn on AWS analytics services such asAmazon QuickSightandAmazon Athenato visualize the data. This will help address some of the reporting requirements found in regulatory studies likeCCAR,DFAST,CECL, andIFRS9.For more information about establishing a data pipeline, readLake House Formation Architecture. It is a powerful pattern that combines a few concepts that will help bring your data together cohesively. To set up a robust CI/CD pipeline, explore theAWS Serverless CI/CD Reference Architecture.TAGS:financial services complianceJiwan PanjikerJiwan Panjiker is a Solutions Architect at Amazon Web Services, based in the Greater New York City area. He works with AWS enterprise customers, helping them in their cloud journey to solve complex business problems by making effective use of AWS services. Outside of work, he likes to spend time with his friends and family, going for long drives, and exploring local cuisine.Brian TangBrian Tang is an AWS Solutions Architect based out of Boston, MA. He has 8+ years of experience helping enterprise customers across a wide range of industries achieve digital transformations by migrating workloads to the cloud. His core interests include DevOps and serverless-based solutions. Outside of work, he loves rock climbing and playing guitar.Raviteja YelamanchiliRaviteja Yelamanchili is an Enterprise Solutions Architect with Amazon Web Services based in New York. He works with large financial services enterprise customers to design and deploy highly secure, scalable, reliable, and cost-effective applications on the cloud. He brings over 11+ years of risk management, technology consulting, data analytics\, and machine learning experience. When he is not helping customers, he enjoys traveling and playing PS5.
Since the 2008 financial crisis, banking supervisory institutions such as the Basel Committee on Banking Supervision (BCBS) have strengthened regulations. There is now increased oversight over the financial services industry. For banks, making the necessary changes to comply with these rules is a challenging, multi-year effort. Basel IV, a massive update to existing rules, is […]
Jiwan Panjiker
Amazon EC2 Container Registry Amazon SageMaker Architecture AWS CodeCommit AWS CodePipeline AWS Fargate Compliance Financial Services Intermediate (200)