Digital underwriting ml

source/solutions-library.txt

@@ -112,7 +112,23 @@ kick-start their projects.
 
    .. tab:: Media
       :tabid: media
+
+      App-Driven Analytics
+      --------------------
+      .. card-group::
+         :columns: 2
+         :style: extra-compact
+
+         .. card::
+            :headline: Automating digital underwriting with Machine Learning
+            :url: https://deploy-preview-202--docs-atlas-architecture.netlify.app/solutions-library/Streamline-global-gaming-management/
+            :icon: mdb_custom_aggregation
+            :icon-alt: Atlas mdb_custom_aggregation icon
 
+            Leverage Machine Learning with real-time data processing and
+            automate digital underwriting.
+
+
       Gen AI
       ------
 
@@ -130,11 +146,7 @@ kick-start their projects.
             user experiences by using Atlas Database, 
             Atlas Vector Search, and Pymongo. 
 
-
-
-      .. App-Driven Analytics
-      .. --------------------
-
+
 .. .. tab:: Retail
    .. :tabid: retail
 

source/solutions-library/audio-based-AI-diagnostics.txt

@@ -83,7 +83,7 @@ maintenance needs, minimize disruptions, and improve fleet management.
 equipment to identify potential issues early, ensuring patient safety, and
 optimizing healthcare operations.
 
-.. video:: https://youtu.be/-FCyBZlA2_8
+.. video:: https://youtu.be/91WlXYEUEkk
 
 A fun and quick demo of MongoDB's Vector Search capabilities for anomaly
 detection through sound input, using a basic handheld fan as our makeshift wind

source/solutions-library/digital-underwriting.txt

@@ -0,0 +1,230 @@
+.. _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/industries/manufacturing>`__,`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/deployment-hierarchy.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 and a daily cron job is run every day at midnight that
+summarizes the daily trips and compiles them into a document stored in a
+new collection called “CustomerTripDaily.” A monthly cron job is run on
+the 25th day of each month, aggregating the daily documents and creating
+a new collection called “Customer Trip Monthly.” 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 “Customer
+Trip Monthly” 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’ (1). 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. This then generates a daily summary of the raw IoT
+      data and places it in a materialized view collection named
+      ‘customerTripDaily’ (2). Similarly for a monthly materialized view, a
+      scheduled trigger executes a function that runs an aggregation pipeline
+      that summarizes the information in the ‘customerTripDaily’ collection on
+      a monthly basis and places it in a materialized view collection named
+      ‘customerTripMonthly’ (3).
+
+      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/deployment-hierarchy.svg
+         :figwidth: 1200px
+         :alt: An illustration shows on 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 then 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/deployment-hierarchy.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 view 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/digital-underwriting>

source/solutions-library/insurance-gen-ai.txt

@@ -2,4 +2,5 @@
    :titlesonly:
 
    PDF Search </solutions-library/pdf-search>
-   Claim Management <solutions-library/claim-management>
+   Claim Management <solutions-library/claim-management>
+   Digital Underwriting </solutions-library/digital-underwriting>

source/solutions-library/manufacturing-iot.txt

@@ -1,4 +1,5 @@
 .. toctree::
    :titlesonly:
 
-   Audio-Based AI Diagnostics <solutions-library/audio-based-AI-diagnostics>
+   Audio-Based AI Diagnostics <solutions-library/audio-based-AI-diagnostics>
+   Digital Underwriting </solutions-library/digital-underwriting>