Author: Jim Kunce, SVP & Chief Actuary
Company: MedPro Group
Awards Category: Best Use of Server
Describe the problem you needed to solve
MedPro Group, Berkshire Hathaway's dedicated healthcare liability solution, is the nation's highest-rated healthcare liability carrier - according to A.M. Best (A++ as of 5/27/2015). We have been providing professional liability insurance to physicians, dentists and other healthcare providers since 1899. Today, we have insurance operations in all 50 states, the District of Columbia and are growing internationally. With such great size of operations and diversity of insurance products, it is a challenge to connect systems, processes and employees with one another.
Regardless of an insurance carrier's size and scale, its long-term success depends on:
Our challenge was to lay the analytical foundation necessary for an ever-growing insurance company to execute on these three objectives. We identified the following three action items and linked them to the drivers of long-term success.
Describe the working solution
"Fuel new business growth by centralizing processes & remove system silos, link manual processes together."
This solution has three parts to it.
Today, from anywhere in the country, our sales personnel can request a report for a customer they are prospecting and receive a consistent, reliable recommendation in a matter of seconds with little manual intervention.
"Drive consistent pricing and risk-evaluation: Remove data supply bottle-necks & empower business analysts to self-serve."
In an insurance company, actuaries and underwriters are responsible for pricing insurance policies and evaluating insurance risks of applicants. These complex decisions rely on many data inputs - some of which are internally available, but in other cases come from external sources (e.g. government websites, third party resources).
Today, we have been able to significantly reduce the data supply bottle-necks by configuring the Alteryx server to be the bridge between the data sources and our actuaries and underwriters. Each person along the pricing and risk evaluation process now gets “analysis-ready†data consistently and timely from the private gallery, a virtual buffet of self-serve apps for all data needs.
"Unify internal operations: accelerate modernization -- facilitate enterprise-wide legacy system integration."
In 2015, MedPro Group decided to scale up investments in modernizing legacy systems to a new web-based system. The challenge was to move data from our legacy systems into the new web-based system and vice versa. Additionally, the software solution needed to have a short learning curve and be flexible and transparent enough that key business leaders managing this modernization would be able to perform the data migration tasks.
Alteryx was a great fit in this case. Not only were business leaders able to program processes in Alteryx in a relatively short timeframe, we scaled up with ease and accelerated modernization by deploying on the private server for analysts to use in a self-serve, reliable environment.
Describe the benefits you have achieved
"We have connected systems, processes and employees to one another and made the benefits of that interconnectivity available to every employee."
We have been using the private gallery and server since July, 2015. What started as a proof of concept and experiment is now a fully functional production-grade experience. The list of systems that have been connected, processes that have been automated and employees who are finding value out of our private gallery and server is growing rapidly.
Here's a view into some of the measurable benefits we have achieved in just nine months -
And our goal? Move that needle to 100% with Alteryx in the months to come!
Author: Michael Barone, Data Scientist
Company: Paychex Inc
Awards Category: Best Use of Predictive
Describe the problem you needed to solve
Each month, we run two-dozen predictive models on our client base (600,000 clients). These models include various up-sell, cross-sell, retention, and credit risk models. For each model, we generally group clients into various buckets that identify how likely they are to buy a product/leave us/default on payment, etc. Getting these results into the hands of the end-users who will then make decisions is an arduous task, as there are many different end-users, and each end-user can have specific criteria they are focused on (clients in a certain zone, clients with a certain number of employees, clients in a certain industry, etc.).
Describe the working solution
I have a prototype app deployed via Alteryx Server that allows the end-user to “self-service” their modeling and client criteria needs. This is not in Production as of yet, but potentially provides great accessibility to the end-user without the need of a “go-between” (my department) to filter and distribute massive client lists.
Step 1: ETL
This results in several YXDBs that are used in the models. Not all YXDBs are used in all models. This creates a central repository for all YXDBs, from which each specific model can pull in what is needed.
Once all the YXDBs and CYDBs are created, we then run our models. Here is just one of our 24 models:
The individual model scores are stored in CYDB format, to make the app run fast (since the end-user will be querying against millions and millions of records). Client information is also stored in this format, for this same reason.
Step 2: App
Step 3: Gallery
And then they can select the various client criteria:
Once done running (takes anywhere between 10 – 30 seconds), they can download their results to CSV:
Describe the benefits you have achieved
Not having to send out two dozen lists to the end-users, and the end users not having to wait for me to send them (can get them on their own). More efficient and streamlined giving them a self-service tool.
Author: Mandy Luo, Chief Actuary and Head of Data Analytics
Company: ReMark International
Awards Category: Best Use of Predictive
As a trained Statistician, I understand why "70% data, 30% model" is not an exaggeration. Therefore, before applying any regression models, I always make sure that input data are fully reviewed and understood. I use various data preparation tools to explore, filter, select, sample or join up data sources. I also utilize the data investigation tools to conduct or validate any statistical evaluation. Next, I would usually choose 3-5 predictive modeling candidates depending on the modeling objective and data size. I often include one machine learning methods in order to at least benchmark other models. After the modeling candidates finish running, I would select the best model based on both art (whether the coefficients look reasonable based on my understanding of the data and business) and science (statistical criteria's like the goodness of fit, P-value and cumulative lift etc.). I am also often using the render function for model presentation and scoring/sorting function for model validation and application.
Describe the problem you needed to solve
ReMark is not only an early adopter in predictive modeling for life insurance, but also a true action taker on customer centricity by focusing on customer lifetime analytics (instead of focusing on 'buying' only). In this context, we need to 'join up' our predictive models on customer response, conversion and lapse in order to understand the most powerful predictors that drive customer activities across pre and post sales cycle. We believe the industry understand that it is insufficient to only focus on any single customer activity, but is still exploring how this can be improved through modeling and analytics, which is where we can add value.
Describe the working solution
Our working solution goes with the following steps:
Describe the benefits you have achieved