Car insurance and more could become cheaper (and a lot fairer) thanks to artificial intelligence

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TomTom partners with AI insurance startup Loop to provide coverage that balances driving habits over demographics.

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TomTom on Wednesday unveiled a multi-year partnership with Loop, an Austin, Texas-based insurance technology startup, to provide better, more transparent auto insurance pricing, the companies said.

According to the announcement, Loop will provide machine learning data and TomTom, the Netherlands-based mapmaker and location technology specialist, will provide Loop with things like speed profiles, traffic statistics and map services, all in the context of optimizing Loop’s AI capabilities to improve driver safety to help lower insurance rates.

Retaining car coverage and how the rate is calculated can be frustrating and all too often unfair to consumers. When potential customers inquire about a rate quote for coverage, the most popular legacy insurance companies typically use information such as an individual’s annual income, credit score, education level, and even marital status. And as a result, according to Loop, such stats hinder low-income drivers with too high rates and insufficient coverage.

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Loop, which considers itself a Community trade mark, hopes to change such circumstances by removing the bias it believes is embedded in such standards through a data-driven insurance approach. In particular, it wants to use AI and telematics – a system where data transfer from the vehicle to a company keeps costs down. How well and where you drive is critical – not a fare that isn’t too high because the driver doesn’t have a master’s degree or an impeccable FICO score.

In addition, TomTom said in a press release that it aims to provide Loop AI assistance for analyzing road and driver data, which in turn will help the startup better understand driving behavior and also encourage policyholders to take safer routes. The reward would reduce insurance risk and costs for drivers.

This data-driven approach to provide consumers with a more level playing field is naturally finding its way into a range of business models. For example, according to Vidya Phalke, the chief innovation officer at risk-resilient company MetricStream, “artificial intelligence is helping financial services companies when it comes to GRC (governance, risk and compliance) – especially for empowering their frontline employees – who are often the prime target of cyber-attacks.”

β€œAI enables companies to break down barriers and silos,” he adds, β€œby creating a comprehensive view across departments and introducing cognitive search capabilities so financial institutions can locate data and sort and analyze any risk while it evolves. When searches speed up, first and second-tier users can reduce redundancy and move to tighter security and smoother compliance.”

Some experts warn that AI alone cannot remove the intrinsic bias. According to AI expert Roman Yampolskiy, a professor of computer science and engineering at the University of Louisville, it is unfeasible to create machine learning systems that are 100% impartial. “Researchers usually try to downsize their AI,” he said, “but there are proven mathematical results showing that a certain bias is impossible to remove.”

Yet there is an argument that the use of AI can nevertheless provide financial inclusion for more disadvantaged individuals. For example, Moutusi Sau, VP of research at Gartner, states that within the banking services sector, “the increasing use of AI models in lending would help increase financial inclusion in underserved populations.” In particular, he argues in his research that more fairness can be created in the lending process by using AI to explain the terms of agreements and help banks evaluate a broader population.

β€œFrom a data and model perspective, explainability helps financial institutions identify and retrain any systemic biases in model output if necessary,” he says. While he adds, “While not a perfect solution, explainability is a prerequisite to increase diversity and inclusion. To achieve the desired outcome, responsibility must be intrinsically incorporated into the design of the algorithm.”

And back to the auto insurance front, Ali Salhi, the chief technology officer at Loop, says AI-powered data only benefits drivers β€” if they’re committed to safe driving.

For Salhi, the use of TomTom’s mapping technology and comprehensive location data will enable his company to “insure and assess risks with pinpoint precision never before seen in private passenger car insurance.” The way Loop sees it, through algorithms, there is a way to spark a move that is shaking up an industry that brings in $254 billion annually.

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