2 items tagged "insurance industry"

  • How data analytics is affecting the insurance industry

    How data analytics is affecting the insurance industry

    Data analytics in the insurance industry is transforming the way insurance businesses operate. Here's why that is important.

    Technology has had a profound impact on the insurance industry recently. Insurers are relying heavily on big data as the number of insurance policyholders also grow. Big data analytics can help to solve a lot of data issues that insurance companies face, but the process is a bit daunting. It can be challenging for insurance companies who have not adjusted to this just yet.

    Effect of big data analytics on customer loyalty

    One of the reasons why some insurance companies get more customers as compared to others is because they can provide the things that their customers need. The more that they can give what the customers expect, the more loyalty customers reciprocate in return.

    Instead of just aggregating one policy from their insurer at a time, they may get all of their insurance policies in a single, centric dashboard. Even if people solicit an anonymous car insurance quote from a different company that is lower than others, they would still stick to a company that they are fiercely loyal to. This means that they will need to consider other factors, such as whether they have been unfairly prejudicing customers based on characteristics like gender or race. Big data may be able to help address this.

    Big data analytics can be very useful in acquiring all of the necessary data in a short amount of time. This means that insurance companies will know what their customers want and will offer these wants immediately. Insurance companies will also have the ability to provide personalized plans depending on their customer’s needs.

    Big data analytics in fraud cases

    One of the biggest issues that insurance companies are facing nowadays is fraud. According to industry findings, 1 out of 10 claims is fraudulently filed. This is an alarming rate, especially with the number of policyholders that an insurance company may have. Some consumers filing fraudulent claims have done so sloppily, which makes it easier for the company to seek restitution and prosecute the offenders before they can drive premiums up on other drivers. Some may be meticulously done and people can get away with it.

    With big data analytics, a large amount of data can be checked in a short amount of time. It includes a variety of big data solutions, such as social network analysis and telemetrics. This is the biggest weapon insurers have against insurance fraud.


    A large amount of data that is needed and received for subrogation cases. The data can come from police records, medical records, and even notes regarding cases. Through big data analytics, it will be possible to get phrases that will show that the cases that are being investigated are subrogation cases.

    Settlement cases

    There are a lot of customers who may complain that lawsuit settlements often take a long time, because there is a lot of analysis that needs to be done. With the use of big data analytics, the processes can help settle the needed claims instantly. It will also be possible to check and analyze the history of the claims and the claims history of each customer. This can help reduce labor costs as the employees do not have to put all of their time into checking and finalizing each data regarding the claim. It can also give the payouts to the customer faster which means that customer satisfaction will also greatly increase.

    Checking more complex cases

    There are some people who have acquired anonymous car insurance quote and have gotten insurance in order to file claims to acquire money from the insurance company. Some cases are obvious frauds and the authentic ones can be immediately analyzed with the use of big data analytics. Yet, there are some cases that are just too complex that it would take a lot of checking to see if the data received coincide with what the customer claims. Big data analytics use data mining techniques. These techniques allow the various claims to be categorized and scored depending on their importance. There are even some that will allow the claims to be settled accordingly.

    Some common issues in using big data analytics

    It is always important for insurance companies to consider both the good and the bad details about using analytics. Some of the good things have been tackled above. These are just some concerns that you need to be familiar with:

    • You still need to use multiple tools in order to process the data which can be problematic as data may get lost along the way.
    • Getting too many data analysts when a few will be enough.
    • Not unifying the gathered information.

    Take note of these issues so that they can be avoided.

    With all of the things that big data analytics can do, it is not surprising why a lot of insurance companies would need to start using this soon. This can be integrated little by little so that it will not be too overwhelming for everyone who is involved. The sooner that this can be done, the better. Not only for the customers but for the insurance company as a whole.

    Big data will address countless insurance industry challenges

    The insurance industry is more dependent on big data than many other sectors. Their entire business model is built around actuarial analyses. As a result, they will need to rely on big data to solve many of the challenges that have plagued them for years. Big data will also help them fight fraud and process lawsuit settlements more quickly.

    Author: Diana Hope

     Source: Smart Data Collective

  • Pyramid Analytics' 5 main takeaways from the Insurance AI and Analytics USA conference in Chicago

    Pyramid Analytics' 5 main takeaways from the Insurance AI and Analytics USA conference in Chicago

    Pyramid Analytics was thrilled to participate in the Insurance AI and Analytics USA conference in beautiful Chicago, May 2-3. The goal of the conference was to provide education to insurance leaders looking for ways to use AI and ML to extract more value out of their data. In all of their conversations, the eagerness to do more with data was palpable, but a tinge of frustration could be detected beneath the surface.

    Curious to understand this contradiction, they started most of their conversations with the same basic question: 'What brings you to the show?' Followed by a slightly deeper question: 'Where are you with your AI and ML initiatives?'

    The responses varied. However, a common thread emerged: despite the desire to incorporate AI and ML capabilities into routine business practices, roadblocks remain, regardless of carrier type. Chief among the concerns of the attendees was the ability to access data, it appears that data silos are alive and well. We also heard many express frustrations with the tools used to derive AI and ML insights.

    Here are some observations of the most common reasons for attending the show into five groups, organized by persona:

    1. Data scientists looking for deeper access to data 

    The data scientists seemed to struggle with data access, which is often trapped within departments throughout the organization. To do their jobs effectively, data scientists need to access data so they can unlock trapped business value. They were seeking solutions that would help them bridge the gap between data and analytics.

    2. Executives from traditional organizations trying to understand the way forward

    To varying degrees, the insurance executives had AI and ML programs in place but weren’t satisfied with the results. They attended the conference to learn how they could extract more value from their AI and ML initiatives.

    3. Sophisticated insurers seeking technology to gain an edge on the competition

    This was a general takeaway from indivivuals from newer insurance companies who fit squarely in the “early technology adopter” category. Lacking the constraints of typical insurers (legacy processes and systems), these individuals were seeking information on new technologies and hoping to build partnerships with vendors to achieve further differentiation.

    4. Data and technology vendors looking to build meaningful partnerships

    There were many representatives from data and technology companies seeking out insurance partners looking to advance their businesses at the margins, either by enriching existing data store or by finding new or unique data streams.

    5. Consultants promoting their unique approach to AI and ML initiatives

    It’s clear that AI and ML initiatives require more than just tools, people, and processes. They require strategic direction and a roadmap that builds consistency and accountability. There were a number of consultants making themselves available to insurers.

    Author: Michael Hollenbeck

    Source: Pyramid Analytics

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