DOCSP-51702-fraud-detection-accelerator-AWS (#13618)

* DOCSP-51702-fraud-detection-accelerator-AWS

* edits

* edits

* edits

* edits

* last edits

Author: gmiller-mdb

content/atlas-architecture/current/source/solutions-library/fraud-detection-accelerator.txt

@@ -14,82 +14,67 @@ Fraud Detection Accelerator Using AWS SageMaker
    :depth: 1
    :class: singlecol
 
-Revolutionize fraud detection in finance with MongoDB Atlas and Amazon
-SageMaker Canvas. Leverage real-time data and AI for stronger defenses
-against cybercrime.
-
 - **Use cases:** `Gen AI <https://www.mongodb.com/use-cases/artificial-intelligence>`__,
   `Fraud Prevention <https://www.mongodb.com/industries/financial-services/fraud-prevention>`__
 
 - **Industries:** `Financial Services <https://www.mongodb.com/industries/financial-services>`__, 
   `Insurance <https://www.mongodb.com/industries/insurance>`__
 
-- **Products and tools:** `Atlas <https://www.mongodb.com/atlas/database>`__, 
-  `Atlas Charts <https://www.mongodb.com/products/charts>`__, 
-  `Data Federation <https://www.mongodb.com/products/platform/atlas-data-federation>`__
+- **Products and tools:** `MongoDB Atlas <https://www.mongodb.com/atlas/database>`__, 
+  `MongoDB Atlas Charts <https://www.mongodb.com/products/charts>`__, 
+  `MongoDB Data Federation <https://www.mongodb.com/products/platform/atlas-data-federation>`__
 
 - **Partners:** `Amazon S3 <https://aws.amazon.com/s3/>`__, 
   `Amazon SageMaker Canvas <https://aws.amazon.com/pm/sagemaker/>`__
 
 Solutions Overview
 ------------------
-Financial services organizations face growing risks from cybercriminals. High-profile
-hacks and fraudulent transactions undermine faith in the industry. As technology evolves,
-so do the techniques employed by these perpetrators, making the battle against fraud a
-perpetual challenge. Existing fraud detection systems often grapple with a critical
-limitation: relying on stale data. The newest tactics often can be seen in the data.
-That's where the power of operational data comes into play.
-
-By harnessing `real-time data
-<https://www.mongodb.com/basics/real-time-analytics-examples>`__, fraud detection models
-can be trained on the most accurate and relevant clues available. |service-fullname|, a highly
-scalable and flexible developer data platform, coupled with Amazon SageMaker Canvas, an
-advanced `machine learning <https://www.mongodb.com/basics/machine-learning>`__ tool,
-presents a groundbreaking opportunity to revolutionize fraud detection. By harnessing
-operational data and leveraging the power of real-time insights, financial institutions
-can fortify their defenses against cybercriminals who seek to exploit vulnerabilities for
-illicit gains. |service-fullname| proves its strength as an operational data store,
-accommodating high-volume transactional data with exceptional performance and flexibility.
-Meanwhile, Amazon SageMaker Canvas empowers business analysts to leverage AI/ML solutions
-effortlessly, providing a no-code platform that brings the power of advanced analytics to
-their fingertips.
-
-Challenges with Legacy Fraud Systems 
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-- **Incomplete data visibility from legacy systems:** Lack of access to relevant data
-  sources hampers fraud pattern detection. 
-
-- **Latency issues in fraud prevention systems:** Legacy systems lack real-time
-  processing, causing delays in fraud detection.
-
-- **Difficulty in adapting legacy systems:** Inflexibility hinders the adoption of
-  advanced fraud prevention technologies. 
-
-- **Weak security protocols in legacy systems:** Outdated security exposes vulnerabilities
-  to cyber attacks. 
-
-- **Operational challenges due to technical sprawl:** Diverse technologies complicate
-  maintenance and updates. 
-
-- **Lack of collaboration between teams:** Siloed approach leads to delayed solutions and
-  higher overhead.
+
+Financial institutions face growing risks from cybercriminals, including high-profile
+hacks and fraudulent transactions. Cyber incidents undermine customer trust and 
+can result in significant financial losses for companies. Companies struggle to 
+implement secure systems, due to the limitations of legacy fraud systems, which 
+include: 
+
+- **Incomplete data visibility:** Lack of access to relevant data sources for pattern
+  detection.
+
+- **Latency within fraud systems:** Lack of real-time processing capabilities 
+  that causes fraud detection delays.
+
+- **Weak security protocols:** Outdated security that exposes vulnerabilities to
+  cyber attacks.
+
+- **Technical sprawl:** Diverse technologies that complicate maintenance and updates.
+
+- **Poor team collaboration:** Siloed approaches that lead to delayed responses.
+
+To overcome these challenges, financial companies can use
+`real-time analytics <https://www.mongodb.com/basics/real-time-analytics-examples>`__
+solutions powered by MongoDB Atlas and Amazon SageMaker Canvas. These tools deliver
+strong fraud detection systems that use the most accurate data available for 
+their operations.
+
+In this system, MongoDB Atlas stores the operational data and processes 
+high-volume transactions. While, Amazon SageMaker Canvas uses sophisticated AI 
+and `machine learning <https://www.mongodb.com/basics/machine-learning>`__ (ML) 
+tools to power advanced analytics for fraud detection.
 
 Reference Architectures
 -----------------------
-Below, you will find the architecture used to build this fraud solution. The architecture
-includes an end-to-end solution for detecting different types of fraud in the banking
-sector, including card fraud detection, identity theft detection, account takeover
-detection, money laundering detection, consumer fraud detection, insider fraud detection,
-and mobile banking fraud detection to name a few.
+Below is the architecture used to build this fraud detection solution.
+The architecture includes an end-to-end solution for detecting different types 
+of fraud in the banking sector, including card fraud detection, identity theft 
+detection, and consumer fraud detection.
 
 The architecture diagram illustrates model training and near real-time inference. The
 operational data stored in `MongoDB Atlas <https://www.mongodb.com/atlas/database>`__ is
-written to the `Amazon S3 <https://aws.amazon.com/s3/>`__ bucket using the Triggers
-feature in {+atlas-app-services+}. Thus stored, data is used to create and train the
-model in `Amazon SageMaker Canvas <https://aws.amazon.com/pm/sagemaker/>`__. The SageMaker
-Canvas stores the metadata for the model in the |s3| bucket and exposes the model endpoint
-for inference.
+written to the `Amazon S3 <https://aws.amazon.com/s3/>`__ bucket using 
+`MongoDB Atlas Triggers <https://www.mongodb.com/docs/atlas/atlas-ui/triggers/>`__.
+Thus stored, the data is used to create and train the model in 
+`Amazon SageMaker Canvas <https://aws.amazon.com/pm/sagemaker/>`__. The SageMaker
+Canvas stores the metadata for the model in the |s3| bucket and exposes the model
+endpoint for inference.
 
 .. figure:: /includes/images/industry-solutions/fraud-prevention-architecture.png
    :figwidth: 750px
@@ -99,12 +84,16 @@ for inference.
 
 Data Model Approach
 -------------------
-The data is divided into two separate files: one containing identity information and the
-other containing transaction data. These files are connected through the TransactionID.
-It's important to note that not every transaction includes associated identity details.
+The data is divided into two separate files: 
+
+- Transaction
+- Identity
 
-Based on the above two datasets, we prepare a test join on the TransactionID, adding the
-target column as Fraud.
+These files are connected through the ``TransactionID``.
+However, not every transaction includes associated identity details.
+
+Based on the above two datasets, prepare a test join on the ``TransactionID``, 
+adding the target column as Fraud.
 
 *Data courtesy of* `Kaggle <https://www.kaggle.com/c/ieee-fraud-detection/data>`__.
 
@@ -143,76 +132,51 @@ target column as Fraud.
    TransactionAmt,
    isFraud
 
-Building the Solution
----------------------
-The detailed step-by-step guide to build this solution can be found in this `Github repo
+Build the Solution
+------------------
+The detailed step-by-step guide to build this solution is available on this `Github repo
 <https://github.com/mongodb-partners/Frauddetection_with_MongoDBAtlas_and_SageMakerCanvas/blob/main/README.md>`__.
-Below you will find an overview of those steps taken:
+Below is an overview of those steps taken:
 
 1. Set up the `S3 bucket
    <https://docs.aws.amazon.com/AmazonS3/latest/userguide/create-bucket-overview.html>`__
    to which the |service-fullname| data needs to be exported.
 2. `Set up
    <https://www.mongodb.com/basics/clusters/mongodb-cluster-setup#:~:text=about%20storage%20capacity.-,Creating,-a%20MongoDB%20Cluster>`__
    an |service-fullname| Cluster.
-3. Set up {+atlas-app-services+}.
+3. Set up `MongoDB Atlas Triggers and Functions <https://www.mongodb.com/docs/atlas/atlas-ui/triggers/>`__.
 4. Set up the `Amazon SageMaker
    <https://docs.aws.amazon.com/sagemaker/latest/dg/onboard-quick-start.html>`__ domain.
 
-MongoDB Atlas as the Operational Data Store
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-The MongoDB Atlas developer data platform is an integrated suite of data services centered
-on a `cloud database <https://www.mongodb.com/cloud-database>`__ designed to accelerate
-and simplify how developers build with data. Its ability to handle large amounts of data
-in a flexible schema empowers financial institutions to effortlessly capture, store, and
-process high-volume transactional data in real-time. This means that every transaction,
-every interaction, and every piece of operational data can be seamlessly integrated into
-the fraud detection pipeline, ensuring that the models are continuously trained on the
-most current and relevant information available. With MongoDB Atlas, financial
-institutions gain an unrivaled advantage in their fight against fraud, unleashing the full
-potential of operational data to create a robust and proactive defense system.
-
-Amazon SageMaker Canvas as an AI/ML Solution
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-Amazon SageMaker Canvas revolutionizes the way business analysts leverage AI/ML solutions
-by offering a powerful no-code platform. Traditionally, implementing AI/ML models required
-specialized technical expertise, making it inaccessible for many business analysts.
-However, SageMaker Canvas eliminates this barrier by providing a visual point-and-click
-interface to generate accurate ML predictions for classification, regression, forecasting,
-natural language processing (NLP), and computer vision (CV). SageMaker Canvas empowers
-business analysts to unlock valuable insights, make data-driven decisions, and harness the
-power of AI without being hindered by technical complexities. It boosts collaboration
-between business analysts and data scientists by sharing, reviewing, and updating ML
-models across tools. It brings the realm of AI/ML within reach, allowing analysts to
-explore new frontiers and drive innovation within their organizations.
-
 Key Learnings
 -------------
-- Understand the use of Atlas Application Services and Atlas Charts to build products at
-  scale.
-- How MongoDB integrates natively with external services (such as AWS SageMaker, AWS S3)
-  to provide even more powerful applications.
 
-Technologies and Products Used
-------------------------------
+- **Develop real-time fraud detection solutions:** MongoDB Atlas handles large 
+  amounts of data in a flexible schema empowering financial institutions to capture,
+  store, and process high-volume transactional data in real-time.
 
-MongoDB Developer Data Platform 
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+- **Update fraud detection models:** Real-time processing with MongoDB's aggregation
+  pipeline ensures that models are continuously trained with the most current and
+  relevant information available. This capacity provides financial institution a
+  powerful tool to create a robust fraud detection system.
 
-- `Atlas Database <https://www.mongodb.com/atlas/database>`__ 
+- **Integrate sophisticated AI and ML tools:** MongoDB integrates with 
+  external services, such as Amazon SageMaker, which offers AI 
+  and ML solutions in a no-code platform. This friendly-user interface makes models
+  accessible to analysts, enabling them to easily generate accurate ML predictions
+  for classification, regression, forecasting, natural language processing (NLP),
+  and computer vision (CV). 
 
-- `Atlas Charts <https://www.mongodb.com/products/charts>`__ 
-
-- `Atlas Data Federation <https://www.mongodb.com/products/platform/atlas-data-federation>`__
+Authors
+-------
+- Babu Srinivasan, Partner Solutions Architect at MongoDB
+- Igor Alekseev, Partner Solutions Architect at AWS
 
-Partner Technologies
-~~~~~~~~~~~~~~~~~~~~ 
+Learn More
+----------
 
-- `AWS S3 <https://aws.amazon.com/s3/>`__ 
+- :ref:`arch-center-hasura-fintech-services`
 
-- `AWS SageMaker Canvas <https://aws.amazon.com/pm/sagemaker/>`__
+- :ref:`arch-center-is-payments-solution`
 
-Authors
--------
-- Babu Srinivasan, Partner Solutions Architect at MongoDB
-- Igor Alekseev, Partner Solutions Architect at AWS
+- :ref:`arch-center-is-card-fraud-solution`