Eight ways machine learning is transforming insurance software - Blog - artificial.

Eight ways machine learning is transforming insurance software

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Collecting data is vital to the insurance industry, allowing firms to calculate risk and develop personalised rating assessments.

Through machine learning, the insurance industry can gain countless benefits, making the quoting and issuing process for insurers faster, safer and completed with fewer errors. Technology has the ability to increase the efficiency of insurance businesses whilst also improving customer experience.

It is undoubtedly the future of the insurance industry, transforming its practices in many valuable ways.

What is machine learning?

Machine learning is an application of Artificial Intelligence (AI) that enables computer systems to use algorithms to automatically learn and improve through experience, as opposed to being entirely programmed.

These algorithms build mathematical models that become 'training data,' allowing systems to make predictions and decisions.

Machine learning is fast becoming an invaluable tool across many industries, and this technology has a huge potential benefit for insurance firms.

Here are eight ways that machine learning is rapidly changing insurance software:

1. Collecting data has never been easier

Data capture is a time-consuming task. With machine learning it only needs to be captured once, meaning you no longer have to rekey information into other platforms.

Paper-based submissions can even be digitalised through Optical Character Recognition (OCR) and advanced machine-learning techniques, enabling data entry that is accurate and efficient. This will allow your team to spend more time focusing on valuable customer interactions rather than performing simple administrative tasks.

2. Better flow and synchronisation of data

Insurance has always involved a huge amount of data, be it in the form of mountains of paperwork or on databases. With the digital revolution, it has never been easier to store and manage data flow.

However, businesses still encounter information silos when transferring data across different systems. Machine learning tackles these issues, allowing insurers to create an environment that ensures business and customer interactions can move seamlessly between departments and platforms without encountering any breaks in the chain.

All information collected can be stored in one place and can be integrated with other apps easily. This makes it easier for task management across the business whilst also increasing the quality of end-to-end information management systems.

3. Automation improves the efficiency of claims processing

Insurance is driven by policy and legal requirements and claims must meet certain criteria throughout the process cycle. Dealing with the thousands of claims and customer queries is a daunting, time-consuming task.

Machine learning can greatly improve these processes moving claims through the initial report, analysis and to contact with the customer much more efficiently and effectively.

This is another huge time-saving application, freeing employees to focus on more complex claims and direct customer contact.

4. Automated support for insurance customers

As well as processing claims, machine learning can also enable elements of claim support to be conducted through AI. AI chatbots can review claims, check policy details and perform fraud detection, before sending instructions to the bank for the claim settlement payments.

This gives customers a quick and efficient response, as opposed to waiting on phone lines for long periods. The journey of individual claims can also be immediately updated as the chatbot responds, ensuring the continued flow of information as it is processed.

This greatly reduces the use of business resources and offers instant customer service, creating a positive experience for all parties.

Through chatbots, machine learning can reduce the impact of fraudulent claims and eliminate human errors and inaccuracies, which are identified through data patterns.

5. Improved underwriting

Effective underwriting requires the processing of huge amounts of data. Machine learning tools can help underwriting by combining insights from a wide variety of analysed data, allowing for a more precise, data-driven approach to calculating premiums.

The healthcare and car insurance sector stand to benefit significantly from machine learning analytics, as advanced algorithms can explore a customer's lifestyle, risk factors, medical records, financial stability and previous insurance claims to create a dynamic and accurate profile.

The data gathered can be scrutinised strictly, reducing the risk of fraud and delivering accurate and safe premiums for customers.

6. Be proactive with predictive analytics

Machine learning tools can use predictive algorithms of past claim activities and data to identify future risk. This can be particularly useful for healthcare, auto insurance and workplace safety insurance.

The information can be used by companies to minimise health risks and protect their resources before any claim event may occur.

7. Understand your customer's value

By using machine learning technology, we can better understand customer lifetime value (CLV). The potential profitability of a customer can be calculated more effectively with AI tools, quickly assessing data to establish revenue vs. expenses of each customer.

Machine learning models can be used to forecast future profitability and customer retention, helping to determine the policies offered.

8. Make marketing easier

Insurance is a very competitive market, and an effective marketing strategy is vital to ensure continued business growth. Using machine learning solutions, insurance firms can gain access to the full profile of prospective customers.

This allows for sophisticated and personalised marketing tactics to be used, resulting in more accurate predictions of customer preferences in order to tailor the products to suit the individual customer. Machine learning enabled insurance advice and quotes can then be offered once the insurer has captured the customer's interest.

Consumers are open to this digital transformation, with 74% of people saying they'd be happy to get computer-generated insurance advice, according to an Accenture poll.

At Artificial, we understand the incredible capabilities that machine learning offers for the insurance industry. We want to enable insurers and customers to overcome the challenges that the traditional life cycle of insurance claims present, instead offering software solutions that provide a holistic improvement to user experience.

Artificial is working with some of the world's largest brokers and underwriters in the industry and we are proud to be a driving force in the future of insurance practice.

by Martin Watts
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