A guide to the artificialOS platform - Blog - artificial.

A guide to the artificialOS platform

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A few years ago, it would have been impossible to predict where the world is today. There has been unprecedented disruption to the global landscape and many are facing challenges to the way they operate.

When we first started using AI and machine learning to build our insurance technology, we wanted to make insurance processes frictionless, even in complex commercial lines. As the world changes, our objective stays the same.

Location shouldn't be a boundary to providing an excellent insurance product, so this step-by-step guide to the artificialOS platform intends to show how we help our clients to improve their workflow and increase profits, wherever they may be working and on whichever device.

The artificialOS platform

Our digital, cloud-based platform allows insurers to digitise and control every aspect of the quote, bind and issue process in four ways:

1. Capture data from customer or client

2. Enrich data with internal and external sources

3. Score and triage risks to provide insight

4. Build a digital contract with electronic signoff

This is not a one-size-fits-all product. It is designed as a series of building blocks that each tackle a different pain point so the insurer can use what they need.

One client may only require a contract negotiation tool, while another may just need a scoring and triage service with the capacity for client data capture. This flexible approach allows each client to really think about their obstacles and work with us for a tailored solution.

ML-powered data capture

The first, and perhaps most important, part of a digitised quote, bind and issue process is data capture. Data - that is to say all of the information that could be used to score a risk - is precious, and informs almost all other aspects of the business.

We enable insurers to collect and structure data so it can be easily accessed by other systems, analysed effectively and is transparent to the customer. Machine learning digitises the data from unstructured emails, attachments and paper-based submissions and can even extract points from text and image-based documents.

Our applications help insurers optimise their profits by recognising and summarising the content of claims documents and continuously learning patterns to suggest improvements for future decision making.

Another recent addition to our platform is a Claims Bordereaux analysis function, which captures data that already exists in a client's Bordereaux claims files, structuring it to provide insights into business performance, claim origin and potential improvements.

Sophisticated data enrichment

To make the most of the wealth of information that's out there, we enable insurers to seamlessly plug in to third-party data providers to enrich and improve the accuracy of their insurance data once it's been collected.

Underwriters and brokers can therefore make fully-informed decisions without the effort of traditional data collection - and with far more accurate results.

As the Artificial CCO and co-founder David King explained in a recent InsTech London podcast, we can tap into valuable specialist data sets like Opta and Pitney Bowes to enhance the underwriter's understanding of a risk.

The benefits of this speak for themselves; more data means more accurate pricing, lower loss ratios and more informed - and therefore satisfied - customers.

Automated scoring and triaging

Underwriters spend too much of their time saying 'no' to risks. To combat this, our technology says no for them instead, focusing on the business that has the most potential value.

Our automated scoring and triaging tools generate scores from all of the data available. These scores are configured by the insurer and used to demonstrate the quality of a risk and its fit within the current portfolio. Submissions can be automatically rejected, accepted or passed on to the underwriter for further investigation.

As King explains, 'We use a variety of rules-based approaches and machine learning, so by the time it hits an underwriter they've got a very detailed picture that helps them understand what the likelihood is of them writing this business, and is it worth their time?'

This flexible and highly accurate process ultimately means that brokers and underwriters receive an initial analysis of a submission without heaps of paperwork, and ensures that they don't waste their time saying 'no' to low-value risks.

Digital contract negotiation

Our contract building tool is one of the most remote-friendly, adaptable applications in the artificialOS platform.

We provide dynamically generated, white labelled websites and apps to our clients, who can in turn communicate quickly and efficiently with their customers and partners. Cloud-based submission means all parties in the value chain have instant access to documents.

Instead of laborious paper-based contracts with repeated edits, the client can create a fully compliant contract, complete with audit trails and amendment tracking from anywhere in the world. This enables digital, real-time contract negotiation which is completed remotely with electronic sign off.

This tool drastically cuts down the time taken to process a policy and allows customers to quote and buy insurance any time or place at a fraction of the speed it used to take.

What's next?

It's clear that insurance is changing and that big decisions are being made as to how the industry proceeds going forward. We believe that the technology to improve already exists - it's just a case of when, rather than if, it's adopted.

At Artificial, we use this technology to help insurers write better business from wherever they are in the world. Data extraction, digital contracts and automated scoring are just some of the ways we are making insurance frictionless, and our platform will continue to thrive and grow.

To find out more about any of our applications, email us at hello@artificial.io or visit our website.

by Anna Burge
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