Share with your friends










Submit

Analytics Magazine

Viewpoint: The importance of confidence scores

Emma DuckworthBy Emma Duckworth

How confident are you about your modeled data?

If you reply honestly, the answer to the question is likely to be akin to sticking your finger in the air and seeing which way the wind is blowing. The problem with modeled data is its very nature – it’s modeled. Therefore, errors and inaccuracies can creep in, making it at best useless, and, at worst, a dangerous tool in business decision-making. That is why confidence scores are crucial to today’s modeled data attributes.

In order to trust and use data science and modeled data, both the science and the data need to be transparent and explainable. If brands are to make important decisions around pricing, qualification, risk and more using data science, they have to be able to understand how models came to the scores they have and how accurate the models themselves are. It is vital for communicating with customers and regulators alike.

Let’s take the insurance industry as an example. Confidence scored data gives autonomy to insurers to create their own thresholds when making nuanced judgements around pricing or the customer journey. Companies can decide themselves between a more disruptive but thorough customer journey or automated form fill when creating policies. Specialty services can tailor models to these variables with full transparency into the quality of the data and the risk they are facing.

Creating confidence scores can often be just as complex as creating your predictive model. Source: ThinkStock

Creating confidence scores can often be just as complex as creating your predictive model.
Source: ThinkStock

However, there are two main problems in creating accurate confidence scores on modeled insurance data. The first is when there isn’t very much training data available. The second is when there is an abundance of training data available, but it is skewed or not representative of the data to be predicted. If this is the case, there is a significant risk that the model will produce high confidence scores for inaccurate predictions because the scoring population is inconsistent with the training population. It’s like creating a model to identify oranges and using it to predict apples. That the model has good confidence in its ability to predict oranges simply isn’t applicable.

To mitigate the risk of small training data, a good usage of statistical methods/approaches/tests (and distribution assumptions) to select upper and lower confidences reflective of volatile data is key. However, the solution to the second issue is more complex than it might seem at first. To combat it, it is crucial to create a process that ensures the test data is representative of the training data and vice versa. In recent times the flood of data has removed the need to be strict with confidence scores and boundaries, however when modelling on skewed data this discipline is still imperative. Training and test data must be collaborated to remove bias.

Given these challenges, creating confidence scores can often be just as complex as creating your predictive model. It requires judgment, statistics and experience. Moreover, accurate confidence scores are vital when providing data that will underpin business processes and an important part of building trust both with consumers and regulators.

Emma Duckworth is the lead data scientist at Outra.

Analytics data science news articles

Related Posts

  • 82
    With the rise of big data – and the processes and tools related to utilizing and managing large data sets – organizations are recognizing the value of data as a critical business asset to identify trends, patterns and preferences to drive improved customer experiences and competitive advantage. The problem is,…
    Tags: data
  • 79
    The Internet of Things (IoT) is considered to be the next revolution that touches every part of our daily life, from restocking ice cream to warning of pollutants. Analytics professionals understand the importance of data, especially in a complicated field such as healthcare. This article offers a framework on integrating…
    Tags: data
  • 73
    Businesses are greatly expanding the autonomous capabilities of their products, services and manufacturing processes to better optimize their reliability and efficiency. The processing of big data is playing an integral role in developing these prescriptive analytics. As a result, data scientists and engineers should pay attention to the following aspects…
    Tags: data
  • 70
    Frontline Systems releases Analytic Solver V2018 for Excel Frontline Systems, developer of the Solver in Microsoft Excel, recently released Analytic Solver V2018, its full product line of predictive and prescriptive analytics tools that work in Microsoft Excel. The new release includes a visual editor for multi-stage “data science workflows” (also…
    Tags: data
  • 68
    Today, we live in a digital society. Our distinct footprints are in every interaction we make. Data generation is a default – be it from enterprise operational systems, logs from web servers, other applications, social interactions and transactions, research initiatives and connected things (Internet of Things). In fact, according to…
    Tags: data

Headlines

Using machine learning and optimization to improve refugee integration

Andrew C. Trapp, a professor at the Foisie Business School at Worcester Polytechnic Institute (WPI), received a $320,000 National Science Foundation (NSF) grant to develop a computational tool to help humanitarian aid organizations significantly improve refugees’ chances of successfully resettling and integrating into a new country. Built upon ongoing work with an international team of computer scientists and economists, the tool integrates machine learning and optimization algorithms, along with complex computation of data, to match refugees to communities where they will find appropriate resources, including employment opportunities. Read more →

Gartner releases Healthcare Supply Chain Top 25 rankings

Gartner, Inc. has released its 10th annual Healthcare Supply Chain Top 25 ranking. The rankings recognize organizations across the healthcare value chain that demonstrate leadership in improving human life at sustainable costs. “Healthcare supply chains today face a multitude of challenges: increasing cost pressures and patient expectations, as well as the need to keep up with rapid technology advancement, to name just a few,” says Stephen Meyer, senior director at Gartner. Read more →

Meet CIMON, the first AI-powered astronaut assistant

CIMON, the world’s first artificial intelligence-enabled astronaut assistant, made its debut aboard the International Space Station. The ISS’s newest crew member, developed and built in Germany, was called into action on Nov. 15 with the command, “Wake up, CIMON!,” by German ESA astronaut Alexander Gerst, who has been living and working on the ISS since June 8. Read more →

UPCOMING ANALYTICS EVENTS

INFORMS-SPONSORED EVENTS

INFORMS Computing Society Conference
Jan. 6-8, 2019; Knoxville, Tenn.

INFORMS Conference on Business Analytics & Operations Research
April 14-16, 2019; Austin, Texas

INFORMS International Conference
June 9-12, 2019; Cancun, Mexico

INFORMS Marketing Science Conference
June 20-22; Rome, Italy

INFORMS Applied Probability Conference
July 2-4, 2019; Brisbane, Australia

INFORMS Healthcare Conference
July 27-29, 2019; Boston, Mass.

2019 INFORMS Annual Meeting
Oct. 20-23, 2019; Seattle, Wash.

Winter Simulation Conference
Dec. 8-11, 2019: National Harbor, Md.

OTHER EVENTS

Advancing the Analytics-Driven Organization
Jan. 28–31, 2019, 1 p.m.– 5 p.m. (live online)

CAP® EXAM SCHEDULE

CAP® Exam computer-based testing sites are available in 700 locations worldwide. Take the exam close to home and on your schedule:


 
For more information, go to 
https://www.certifiedanalytics.org.