Tag Archives: data analytics

Riding the data: How a transportation company used data science to improve decision-making processes

How can a company leverage customer data and turn it into actionable information? This was the challenge one transportation provider faced when its modeling system began underperforming after the company implemented it to predict revenue and passenger traffic. In this article, Milliman consultant Antoine Ly discusses how the firm created a machine-learning model that helps the company analyze various aspects of its ridership, leading to more informed financial decisions.

Here is an excerpt:

Working from a mock-up drafted by the client, the [Milliman] team reproduced the dashboard to the client’s specifications, but it is now supported by newly developed software as well as the client’s data warehouse. The dashboard allows the client’s management team to quire different aspects of passenger usage to gain insight into traffic flows and revenue. Colour-coded symbols, which when clicked on, give managers a concise picture of a train’s revenue and traffic. Managers can also quire the system based on selected features for both past usage and anticipated ridership, and are now able to make more informed decisions about pricing, the need for discounts or adjustments to marketing campaigns.

Because the model can adapt to new situations, deviations from the average error are confined to a much more narrow range. This means managers can have more confidence in the model’s predictive value and increases their ability to manage revenue.

The Data Science Game is back

Registration for the 2017 Data Science Game is officially open. The Data Science Game is a two-phase competition showcasing teams of data science students from universities around the world. An online qualifier will take place on April 15 with the final stage happening in September.

Students from the Moscow Institute of Physics and Technology (MIPT) won last year’s competition. Will your university win this year? To register your team, visit www.datasciencegame.com. The deadline to register is April 9.

Milliman is a sponsor of the 2017 Data Science Game.

Can social media and data analytics expose insurance fraud?

Insurers are now perusing social media networks to detect insurance fraud and avoid paying large claims. Here’s some perspective from Michael Henk’s article “Insurance fraud and social media.”

With the immensely high rate of social media usage, some insurance companies are turning to this large source of public information to help combat insurance fraud. As of April 2016, there are 1.59 billion active Facebook users every month globally, while 400 million people use Instagram every month and 320 million users are active on a Twitter account monthly.5 Social media users account for a substantial portion of the population, and these users are generating a massive amount of data, an insurance company’s best friend.

Each day, 500 million tweets are posted—about 6,000 tweets a second.6 As of May 2013, 4.75 billion pieces of content were shared daily on Facebook. Currently, every 60 seconds on Facebook, 510 comments are posted, 293,000 statuses are updated, and 136,000 photos are uploaded.7 Unless social media users have adjusted the privacy settings on their accounts, all information that is posted is considered public. This means that insurance companies have free access to any non-private posts from their customers (or anyone else for that matter). The insurance industry thrives off of data for business purposes such as underwriting, and now, this social media “data” may help insurance companies succeed in combating insurance fraud.

Searching through these networks can become time-consuming and expensive though. Implementing data analytics in addition can optimize insurers’ time and results by sifting through extensive data and expediting a claims investigation.

While analyzing social media can occasionally result in a big win in the fight against insurance fraud, the costs will not always outweigh the benefits. However, if paired with data analytics, the likelihood of uncovering fraudulent claims can greatly increase for a company. Data analytics gives a company the opportunity to process large amounts of data and quickly identify important outliers or other potential indicators of fraud.

For example, geospatial analysis can be utilized when looking for fraud in wind, hail, or flood damage claims. This statistical analysis helps identify what geographical locations were most affected by a storm and where the company would expect to receive claims. It could also aid in identifying the likely severity of a claim based on the proximity to a storm event. Receiving a claim from outside the affected area or a very large claim from a minimally affected area should trigger the need for further investigation.

Analysts can also examine historical claims to identify typical frequencies and severities of policyholder claims. When a current period’s claims are compared to historical data, one can identify cohorts of policyholders who are either making more frequent claims or claims with higher loss amounts. Investigators may want to look further into these types of policyholders to look for further signs of possible fraud.