Digital Underwriting Draft 2

source/solutions-library.txt

@@ -107,8 +107,21 @@ kick-start their projects.
             capabilities by integrating MongoDB Atlas Vector Search,
             Superduper.io, and LLMs. 
 
-      .. App-Driven Analytics
-      .. --------------------
+       App-Driven Analytics
+       --------------------
+
+       .. card-group::
+          :columns: 2
+          :style: extra-compact
+
+         .. card::
+            :headline: Automating digital underwriting with Machine Learning
+            :url: https://deploy-preview-218--docs-atlas-architecture.netlify.app/solutions-library/automating-digital-underwriting/
+            :icon: mdb_vector_search
+            :icon-alt: Atlas mdb_vector_search icon
+
+            Leverage Machine Learning with real-time data processing and
+            automate digital underwriting 
 
    .. tab:: Media
       :tabid: media

source/solutions-library/automating-digital-underwriting.txt

@@ -0,0 +1,212 @@
+.. _arch-center-is-digital-underwriting-machinelearning-solution:  
+
+=====================================================  
+Automating Digital Underwriting with Machine Learning  
+=====================================================  
+
+.. facet::  
+   :name: genre  
+   :values: tutorial  
+
+.. contents:: On this page  
+   :local:  
+   :backlinks: none  
+   :depth: 1  
+   :class: singlecol  
+
+Leverage Machine Learning with real-time data processing and automate digital underwriting.  
+
+**Use cases:** `Gen AI <https://www.mongodb.com/use-cases/artificial-intelligence>`__,   
+`Analytics <https://www.mongodb.com/use-cases/analytics>`__  
+
+**Industries:** `Insurance <https://www.mongodb.com/solutions/industries/insurance>`__,   
+`Financial Services <https://www.mongodb.com/industries/financial-services>`__,   
+`Healthcare <https://www.mongodb.com/industries/healthcare>`__  
+
+**Products and tools:** `Time Series <https://www.mongodb.com/time-series>`__,   
+`Atlas App Services <https://www.mongodb.com/atlas/app-services>`__,   
+`Atlas Triggers <https://www.mongodb.com/docs/atlas/app-services/triggers/>`__,   
+`Atlas Functions <https://www.mongodb.com/docs/realm/functions/>`__,   
+`Atlas Charts <https://www.mongodb.com/products/charts>`__,   
+`Spark Connector <https://www.mongodb.com/docs/spark-connector/current/>`__  
+
+**Partners:** `Databricks <https://www.mongodb.com/partners/databricks>`__  
+
+Solution Overview  
+-----------------  
+
+Imagine being able to offer your customers personalized, usage-based
+premiums that take into account their driving habits and behavior. To do
+this, you'll need to gather data from connected vehicles, send it to a
+machine learning platform for analysis, and then use the results to
+create a personalized premium for your customers. You’ll also want to
+visualize the data to identify trends and gain insights. This unique,
+tailored approach will give your customers greater control over their
+insurance costs while helping you to provide more accurate and fair
+pricing.  
+
+In the GitHub repo, you will find detailed, step-by-step instructions on
+how to build the data upload and transformation pipeline leveraging
+MongoDB Atlas platform features, as well as how to generate, send, and
+process events to and from Databricks.  
+
+**By the end of this demo, you’ll have created a data visualization with
+Atlas Charts that tracks the changes of automated insurance premiums in
+near real-time:**  
+
+.. video:: https://www.youtube.com/watch?v=91WlXYEUEkk  
+
+Other Applicable Industries and Use Cases  
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~  
+
+**Financial Services**: Banks and financial institutions must be able to
+make sense of time-stamped financial transactions for trading, fraud
+detection, and more.  
+
+**Retail**: Real-time insights into what’s going on right now.  
+
+**Healthcare**: From the modes of transportation to the packages
+themselves, IoT sensors enable supply chain optimization while
+in-transit and on-site.  
+
+Reference Architecture  
+----------------------  
+
+.. figure:: /includes/images/industry-solutions/Writing Fig1.svg  
+   :figwidth: 1200px  
+   :alt: An illustration shows a reference architecture  
+
+   Figure 1: Reference Architecture With MongoDB  
+
+Data Model Approach  
+-------------------  
+
+A basic example data model to support this use case would include
+customers, the trips they take, the policies they purchase, and the
+vehicles insured by those policies.  
+
+This example builds out three MongoDB collections, as well two
+materialized views. The full Hackloade data model which defines all the
+MongoDB objects within this example can be found on GitHub.  
+
+.. figure:: /includes/images/industry-solutions/Writing Fig2.svg  
+   :figwidth: 1200px  
+   :alt: An illustration shows the MongoDB Data model approach  
+
+   Figure 2: MongoDB Data model approach  
+
+Building the Solution  
+---------------------  
+
+A dataset including the total distance driven in car journeys is loaded
+into MongoDB. A daily cron job runs every day at midnight, summarizing
+the daily trips and compiling them into a document stored in a new
+collection called "CustomerTripDaily." A monthly cron job runs on the
+25th day of each month, aggregating the daily documents and creating a
+new collection called "CustomerTripMonthly." Every time a new monthly
+summary is created, an Atlas function posts the total distance for the
+month and baseline premium to Databricks for ML prediction. The ML
+prediction is then sent back to MongoDB and added to the
+"CustomerTripMonthly" document. As a final step, you can visualize all
+of your data with MongoDB Charts.  
+
+.. procedure::  
+   :style: normal  
+
+   .. step:: Creating a data processing pipeline with a materialized view  
+
+      The data processing pipeline component of this example consists of
+      sample data, a daily materialized view, and a monthly materialized
+      view. A sample dataset of IoT vehicle telemetry data represents
+      the motor vehicle trips taken by customers. It’s loaded into the
+      collection named 'customerTripRaw'. The dataset can be found on
+      GitHub and can be loaded via MongoImport or other methods. To
+      create a materialized view, a scheduled trigger executes a
+      function that runs an aggregation pipeline, generating a daily
+      summary of the raw IoT data and placing it in a materialized view
+      collection named 'customerTripDaily'. Similarly, for a monthly
+      materialized view, a scheduled trigger executes a function that
+      runs an aggregation pipeline, summarizing the information in the
+      'customerTripDaily' collection on a monthly basis and placing it
+      in a materialized view collection named 'customerTripMonthly'.  
+
+      See the following GitHub repos to create the data processing pipeline:  
+
+      - Step 1: `Load the sample data <https://github.com/mongodb-industry-solutions/Digital-Underwriting-Usage-Based-Insurance/blob/main/src/LoadingtheSampleData.md>`__  
+      - Step 2: `Setup a daily cron job <https://github.com/mongodb-industry-solutions/Digital-Underwriting-Usage-Based-Insurance/blob/main/src/DailyCronJob.md>`__  
+      - Step 3: `Setup a monthly cron job <https://github.com/mongodb-industry-solutions/Digital-Underwriting-Usage-Based-Insurance/blob/main/src/MonthlyCronJob.md>`__  
+
+      .. figure:: /includes/images/industry-solutions/Writing Fig3.svg  
+         :figwidth: 1200px  
+         :alt: An illustration shows how to create a data processing pipeline  
+
+      Figure 3: Creating a data processing pipeline  
+
+   .. step:: Automating insurance premium calculations with a machine learning model  
+
+      The decision-processing component of this example consists of a
+      scheduled trigger that collects the necessary data and posts the
+      payload to a Databricks ML Flow API endpoint. (The model was
+      previously trained using the MongoDB Spark Connector on
+      Databricks.) It waits for the model to respond with a calculated
+      premium based on the miles driven by a given customer in a month.
+      Then the scheduled trigger updates the 'customerPolicy' collection
+      to append a new monthly premium calculation as a new subdocument
+      within the 'monthlyPremium' array.  
+
+      See the following GitHub repos to create the data processing pipeline:  
+
+      - Step 4: `Setup a calculate premium trigger <https://github.com/mongodb-industry-solutions/Digital-Underwriting-Usage-Based-Insurance/blob/main/src/CalculatePremiumTrigger.md>`__  
+      - Step 5: `Setup the Databricks connection <https://github.com/mongodb-industry-solutions/Digital-Underwriting-Usage-Based-Insurance/blob/main/src/DatabricksConfiguration.md>`__  
+      - Step 6: `Write the machine learning model prediction to MongoDB <https://github.com/mongodb-industry-solutions/Digital-Underwriting-Usage-Based-Insurance/blob/main/src/Prediction.md>`__  
+
+      .. figure:: /includes/images/industry-solutions/Writing Fig4.svg  
+         :figwidth: 1200px  
+         :alt: Automating Calculations with Machine Learning Model  
+
+      Figure 4: Automating Calculations with Machine Learning Model  
+
+   .. step:: Near-real-time insights of insurance premium changes over time  
+
+      Once the monthly premium calculations have been appended, it’s
+      easy to set up Atlas Charts to visualize your newly calculated
+      usage-based premiums. Configure different charts to see how
+      premiums have changed over time to discover patterns.  
+
+Technologies and Products Used  
+------------------------------  
+
+MongoDB modern, multi-cloud database platform:  
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~  
+
+- `Atlas Database <https://www.mongodb.com/atlas/database>`__  
+- `Aggregation Pipelines <https://www.mongodb.com/docs/v7.0/core/aggregation-pipeline/>`__  
+- `Materialized Views <https://www.mongodb.com/docs/v5.0/core/materialized-views/>`__  
+- `Time Series <https://www.mongodb.com/time-series>`__  
+- `MongoDB Spark Connector <https://www.mongodb.com/products/spark-connector>`__  
+- `Atlas Charts <https://www.mongodb.com/products/charts>`__  
+- `Atlas App Services <https://www.mongodb.com/atlas/app-services>`__  
+  - `Triggers <https://www.mongodb.com/docs/atlas/app-services/triggers/>`__  
+  - `Functions <https://www.mongodb.com/docs/atlas/app-services/functions/>`__  
+
+Partner technologies:  
+~~~~~~~~~~~~~~~~~~~~~  
+
+- `Databricks <https://www.mongodb.com/partners/databricks>`__  
+
+Key Considerations  
+------------------  
+
+- Building materialized views on time series data: refer to steps 1-3 in the GitHub repo.  
+- Leveraging aggregation pipelines for cron expressions: refer to steps 2 or 3 in the GitHub repo.  
+- Serving machine learning models with MongoDB Atlas data: refer to step 4 in the GitHub repo.  
+- Writing a machine learning model prediction to an Atlas database: refer to step in the GitHub repo.  
+- Visualizing near-real-time insights of continuously changing model results: refer to the Bonus step in the GitHub repo.  
+
+Author  
+------  
+
+- Jeff Needham, MongoDB  
+- Ainhoa Múgica, MongoDB  
+- Luca Napoli, MongoDB  
+- Karolina Ruiz Rogelj, MongoDB  

source/solutions-library/insurance-app-driven-analytics.txt

@@ -1,2 +1,4 @@
 .. toctree::
-   :titlesonly:
+   :titlesonly:
+
+   Digital Underwriting <solutions-library/automating-digital-underwriting>

source/solutions-library/insurance.txt

@@ -1,6 +1,6 @@
 .. toctree::
    :titlesonly:
 
+   App-Driven Analytics <solutions-library/insurance-app-driven-analytics>
    Gen AI <solutions-library/insurance-gen-ai>
 
-.. App-Driven Analytics <solutions-library/insurance-app-driven-analytics>

source/solutions-library/retail.txt

@@ -2,6 +2,5 @@
    :titlesonly:
 
    Catalog <solutions-library/retail-catalog>
-   Gen AI <solutions-library/retail-gen-ai>
-
-.. Personalization <solutions-library/retail-personalization>
+   Personalization <solutions-library/retail-personalization>
+.. Gen AI <solutions-library/retail-gen-ai>