Advanced AI features in Power BI reports. Use cases for health insurance

AI and information processing

Fei-Fei Li is a Chinese American academic who specialises in artificial intelligence, particularly computer vision, and teaches computer science at Stanford University, where she set up the Vision Lab.

She is also a co-founder and President of Supervisory Board at AI4ALL, an organisation that promotes diversity and inclusion in the field of artificial intelligence.

Fei-Fei Li said:

“Artificial intelligence has the potential not only to automate routine tasks, but also to increase our creativity and decision-making abilities. By using AI for more efficient information processing, we can focus on more valuable aspects of our work, leading to greater productivity and innovation”

Keeping this in mind, let us look at the AI features offered by Power BI. How can they support complex Power BI reporting?

What is Power BI?

Power BI is a powerful data analytics and visualisation platform developed by Microsoft; it allows users to collect, combine, transform and analyse data from various sources and then present data insights in an accessible format via interactive dashboards and panels.

Thanks to its built-in tools for data visualisation, reporting and data interactivity, Power BI enables you to take quick and informed business decisions in real time.

The platform also supports advanced AI features, which further improve data analytics and trends forecasting.

How is Power BI different from Excel?

Compared to Excel, Power BI offers more advanced data visualisation features; it is integrated with the cloud and also allows the creation of easy interactive management dashboards.

For more information, click here: Data Analytics: Excel vs Power BI tools.

Its greatest advantage over Excel, however, lies in its ability to support large data sets, fast real-time analytics and advanced AI features, which make it a more effective tool for complex business analysis.

Power BI is flexible and scalable, and thus responds equally well to the needs of small companies and large enterprises.

AI features in Power BI

Power BI offers a range of AI features that enable advanced data analytics and more accurate forecasting. Below are some of the main AI features available in Power BI:

Automated Pattern Recognition

Pattern Recognition is a feature that allows the computer to detect and analyse recurring patterns in data. Such patterns may involve different objects, such as number sequences, specific relations between data, and many other regularities.

Thanks to this feature, Power BI is able to quickly identify recurrent patterns, giving you better insight into your data. It is as if we were teaching the computer to look at data and understand what they contain without any manual searching or rule definition.

Automated Machine Learning Models (AutoML)

Automated Machine Learning Models is a feature that allows you to create machine learning models without the need for advanced programming skills. This approach is also known as AutoML (Automated Machine Learning).

How does it work? Once you have imported your data (on customers, transactions or any other information you need to analyse), simply choose the data columns you want to use for forecasting or analysis.

Next, activate the AutoML feature, which will automatically search through different ML algorithms and adapt them to your data.

Power BI will create several parallel AutoML models and evaluate them with metrics such as precision or accuracy. Based on this assessment, you can choose the model that best matches your data.

Next, you can use this model to forecast future data based on new information. The advantage of the Power BI AutoML feature is that even people without any specialist knowledge of machine learning can use its advanced analytics techniques.

A user-friendly tool that allows you to experiment with different models, it can be particularly helpful if you’re not an expert in artificial intelligence.

Questions and Answers (Q&A)

The Q&A feature allows users to ask questions in a natural language, which are then analysed by Power BI with the aid of natural language processing (NLP) models to generate accurate data visualisations that help you understand the information.

What-If Analysis

What-If Analysis allows users to model various scenarios and preview their impact on business outcomes.

You can experiment with different values and parameters; for instance,. you can change product prices or sales volumes, and then observe the impact on business data. The feature gives you a better insight into potential outcomes under a wide range of conditions and helps you take more finely calibrated business decisions.

Clustering

Clustering in Power BI allows you to group data based on their similarities to identify natural structures within data and understand their essence.

Suppose you want to group your customers by age, income or previous illnesses.

Choose the data columns you want to include in the clustering process. Next, activate the clustering feature, which will automatically regroup the data based on similar features.

Power BI relies on algorithms to identify natural groups in data. After clustering is finished, you can analyse how data were divided into clusters (each will consist of elements with similar features).

Clustering helps you identify structures within data, even if they are not known beforehand, and shows which elements are the most alike.

Clusters may be used for business decisions, e.g. marketing or offer customisation.

Image Recognition (Vision)

Image Recognition allows Power BI to analyse and understand images and their content. It can analyse graphics and identify objects, which is particularly useful for visual data used, e.g. in marketing or ad campaign performance analysis.

Power BI uses artificial intelligence technologies such as computer vision, which is a feature capable of recognising objects and elements (e.g. people or specific things) within images and assigning them to different classes. For example, it can tell you if an image shows a car or a person.

Image recognition can be used for decision-making, e.g. in marketing analysis, image content identification or automated tagging.

Text Analytics

Text Analytics allows Power BI to process and understand textual data. Once you have imported such data, you can activate the Text Analytics feature for automated analysis.

To understand text, Power BI employs natural language processing (NLP) techniques and other algorithms.

The feature may recognise elements such as important keywords, tone and mood (positive, negative or neutral), and other information, such as language or expressed emotion.

Based on the analysis, you can formulate important conclusions, such as trends, customer opinions or message tone. Power BI also allows you to visualise your conclusions so that information on text content can be more easily shared, for example, through graphs or reports.

The feature can help you understand and extract information from large volumes of text. It can be used in different areas, such as customer opinion analysis, social media monitoring or document classification.

Use Case: AI features in the insurance sector

AI features were added to Power BI in response to growing data-processing needs on the market. The volumes of data currently collected are immense, but also completely inconsequential if we don’t know how to use them in the first place.

Huge data sets are difficult to analyse and the sorting process is quite time-intensive. And since time is a commodity always in short supply, AI features can take over some of our tasks so we can allocate more time to more creative endeavours. To better visualise the potential of AI, let us quote a few simple examples.

Insurance: Text Analytics

Imagine that you want to simplify your communications with customers and set up a single e-mail address where they can send all their messages. Payment queries, complaints, policy change requests – they all now end up in the same inbox.

An insurance company working with Power BI can use the Text Analytics feature to process e-mail content and identify its key elements (such as topic, questions, information about products or services, other keywords).

Based on such analytics, the system will then assign e-mails to different categories.

For example: e-mails that contain payment queries can be forwarded directly to the financial department, while those about products can go to the customer service team.

Text Analytics also uses selected keywords to tell you whether a message is positive, negative or neutral in tone. And that’s not all. Based on text analytics, the system will add tags and categories to your e-mails so they can be more easily sorted.

Insurance: Power Automate

Using other tools, such as Power Automate, you can also configure automated e-mail management rules that will use the TA tags and categories to forward e-mails to the right inbox.

Once they are forwarded to dedicated departments, you can set up customised alerts to notify selected employees of new customer queries or reports.

In this way, an automated process will further accelerate message categorisation and deliver a lot of pre-processed data for further analysis (e.g. the number of filed complaints).

Insurance: Clustering

Once you have used the TA feature, you can go a step further and activate Clustering to look for less obvious patterns.

Your e-mails are now categorised in terms of mood and content, so you can turn your attention to other data in the database and create subgroups divided by age, gender, place of residence or recent medical appointments, etc.

The results might surprise you! For instance, it may turn out that the largest number of complaints is filed in the financial department by people aged 50-55, with a history of at least one heart attack and long waiting times to see a cardiologist.

Finding such patterns will help you identify areas that might require improvement. This may inspire you to create, e.g. dedicated marketing campaigns devoted to heart attack prevention for 50 year olds.

Insurance: What-If Analysis

If your database includes historical data on, e.g. how many policies were purchased by people of a given age or with a specific number of kids, you will also have data that show, for instance, how much money the group spent on insurance, how long they held the policy, which services they preferred, why they decided to give it up, as well as what complaints and extra claims they made.

Based on such data, the What-If Analysis feature helps you create predicted outcome models for different campaigns. In the models, you can experiment with your customers’ age, number of kids or insurance scope and then analyse the results to best match your offer to actual market demand.

Social Media Campaign

Most companies run marketing campaigns on social media sites like Facebook, Instagram, TikTok, LinkedIn, Twitter, Snapchat or WhatsApp. Their objectives may vary; they may be looking for new talent, searching for suppliers or trying to attract new customers.

Integrating artificial intelligence (AI) with data analytics tools, including Power BI, can significantly boost such campaigns by delivering more accurate analytics to better understand user reactions.

In the insurance sector, Power BI’s advanced AI features may be incredibly helpful in social media marketing campaigns.

They allow you to:

• Judge mood: AI will tell you whether customer comments on your social media platforms are mostly positive, negative or neutral, which is very important for monitoring customer opinion on your insurance offers;
• Segment customers: Thanks to AI and Power BI, you can identify different customer groups based on their online behaviour so as to customise your communications and insurance offers. Gen Z’ers on TikTok won’t be shown the same ads as Millennials on Facebook. It may even turn out your target group is mostly on Instagram!
• Customise content: ML algorithms can help you deliver more personalised ads, and Power BI allows you to track which are the most effective. Using Power BI for data analytics and forecasting, you can also predict future trends in the insurance industry based on customer reactions on social media;
• Report results: Automated Power BI reporting features make it easier to track social media campaign performance and quickly respond to changes in customer behaviour;
•  Identify influencers: AI data analytics can help you identify customers who shape the opinions of others, which is important for building your insurance brand;
• Automate Campaigns: Using ML algorithms for automated ad campaign adjustment helps reach specific customer groups more effectively.

The AI features of Power BI are becoming a potent tool for insurance companies, helping them better understand their customers, customise their offer and run better social media marketing campaigns.

AI in Power BI

To recap, we can ask ourselves: is AI necessary in Power BI at all? Or maybe it’s just a redundant extra feature that any experienced data analyst can easily do without?

Artificial intelligence has already penetrated our private and professional lives. It can be looked at from many angles, but for me, it is an incredible tool that supports us and spurs our growth. When it comes to AI, I agree with the words of Satya Nadella, CEO of Microsoft:

“Artificial intelligence may be a tool not just to automate routine tasks but also to inspire people to take a more creative and focused approach to their work. Working alongside human intelligence, AI may be a key partner, supporting us in our efforts and empowering us to achieve higher levels of performance and innovation.”