Data Accuracy and AI Quality

Looking to maximize the quality of AI throughput? Think Data Accuracy

Working in the insurance industry, we get to deal with a lot of people who’d be a lot happier if they’d just been in a different place 5 seconds ago. “If only I had known before…” is a common sentiment shared by those who’ve met with an unfortunate accident. We understand that very well and while we cannot predict whether an accident will occur or not, we can surely make the insurance process much smoother. Our tools will guide the insurance process through the predictive power of AI.

How are data and AI prediction related?

This is the era of Big Data. Almost every activity you perform online breaks down to a little ‘morsel’ known as data packet. These data packets, once stored in a manageable form, can be manipulated in a variety of ways to make coherent sense.

In the world of AI, data is fed to the intelligent system as a training set. The learning models of AI are broadly categorised as supervised learning and unsupervised learning. The goal is to move from supervised learning (the algorithm is actively engaged in churning out the output from the input) to unsupervised learning (once fed with input, the intelligent system can think on its own and reach an output stage by itself). Once trained with enough precision, AI can now surpass the need for human presence to function. Stage by stage, your AI becomes an expert for the particular domain it was exposed to. A fully trained AI system, often called as expert system, can now start making predictions by bringing to light hidden trends and patterns.

The precursor to this, as it has become clear now, is that the initial data which trained AI must be as error-free as possible.

Measuring the accuracy of data

Now that it is clear the data you collect and feed to your AI system is critical, the next question to answer is: how do you moderate and measure the accuracy of your data? The answer lies in the coupling of the two independent words Data and Accuracy.

Data is the content; it is all facts and numbers. Data will be the one driving all the branching decisions and output stages. Accuracy in this context is less obvious to interpret. When it comes to data accuracy, you must think of it in terms of form and content. Form is the structural composition of content. For proper predictive performance, AI must be trained with a consistent form that is devoid of all ambiguity. (For e.g. you should represent date with a standard date-month-year form throughout the training phase.) It is important to remember that the computer does not understand intention. It will treat the same data point, but with a varying form, as separate data points, which may lead to incorrect results. In this discussion, the underlying assumption is that the data is factually correct. That should be your primary concern: to remove all discrepancies from your data.

Measurable Parameters to Guide You

There are certain distinct parameters that you can use as guidelines to check your data accuracy.

Relevance

While it is your decision whether that you want your AI to go ‘deep’ or ‘wide’, it is generally advisable to keep your domain laser-focused on your business niche. Feed only relevant data to your training set, and periodically evaluate your data sources to see if you may have missed out on a possible avenue for data collection.

Recency

Big data is dynamic. It changes gears at a dizzying speed. Since the primary use of AI is to predict future trends, your intelligent system must successfully capture the current trends. The content streaming service provided by Netflix does a very good job at this. It makes varying suggestions for ‘Things you’d enjoy watching’ based on your recent watching habits and genre preferences. Stale data will most definitely lead to inaccurate results.

Reliability

Three characteristics that indicate reliable data: validity, accuracy (factual correctness) and consistency.

Flexibility

Data should be adaptable to changing business scenarios. It should support a variety of ‘views’, or subsets of the big-picture objective. You need to align your data such that it can morph from a generalized summary form to a more drilled down, specialised report. But keep in mind the first parameter of relevance. Do not opt for flexibility at the cost of a loose focus.

 

Maintaining data accuracy: The Claim Genius Way

We have in our team a dedicated panel of specialists working towards data accuracy. Groups of experienced data scientists, data appraisers and quality control staff members work together like a well-oiled machine to provide only the most refined data to the data analysts so they can interpret it. Our data passes through a rigorous curation process during which it passes through two phases of appraiser feedback. After preliminary inspection, our team enters into QC discussions. We then regulate the data as per predefined parameters and the appraisers can either agree or disagree with the QC team. The team then either finalizes or irons out Disagreements over until the team arrives at a unanimous decision.

We use this polished data to train our Genius AI engine, which then recognizes patterns and provides instant damage assessment of vehicles on the basis of accident photographs. System users can easily upload these photographs using our elegant mobile app. If you’d like to learn more, feel free to write to us. We’d be glad to discuss anything you’d like to know.

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