Blockchain and AI will transform the driving experience

Renting a car can be a time-consuming process. You have to go to the rental agency, wait for the attendant to fill out the leasing forms, which you must then check and sign before even getting sight of the keys. It’s a hassle and that’s even when you remember to bring along your license! We’re so used to going through this process that it’s hard to imagine there’s a better, more convenient way. But there is.

Imagine being able to walk up to a car and lease it with a micro-loan that would approve your use of it for, say, an afternoon. Attached to this micro-loan would be insurance contracts, along with a history of the car’s drivers, services, MOTs and other relevant events. Once you’ve successfully rented the car, a log of your journey – including interactions with toll gates and CCTV cameras, to name a few scenarios – is automatically added to the car’s audit trail.

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This scenario may sound futuristic, but it is all already possible today. And it may surprise you to hear that blockchain technology is the enabler. Car rentals and auto financing are just one potential application of blockchain, but by following this example we can get a sense of how transformative blockchain can be for financial services.

Recording time chains of events

When you hear ‘blockchain’ you probably immediately think ‘Bitcoin.’ While it’s true that blockchain is the technology that underlies Bitcoin, its potential applications are far broader. In fact, worldwide spending on blockchain solutions is set to hit $2.1 billion this year, more than double the amount spent in 2017.

As well as supporting cryptocurrencies, blockchain technology can also be used to record ‘time chains’ of events in relation to contracts, interactions and occurrences, like in the car rental scenario described above. These time chains encrypt the identities of the people and items they interact with, as the distributed blockchain creates a single, unquestionable source of truth.

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The range of possibilities for these auditable time chains is vast. Imagine, for example, applying them to tracking faulty components from their source to every affected vehicle. This could transform the management of safety flaws and put an end to today’s inefficient mass recalls.

Data chains and AI

But it isn’t just timed events that can be tracked in a blockchain. Data can be, too. A car, for example, generates thousands of interactions per year with different drivers, other cars, petrol stations, traffic lights and cameras. From these interactions, the car builds data, along with its understanding of what’s routine, and what’s not.

When you add artificial intelligence (AI) to the mix, it’s possible to draw insight from the complex webs of relationships that exist between these data event chains. For example, it becomes possible to identify periods of poor driving, minor collisions, and even opportunities to service or repair a car before certain parts break.

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Finally, attaching scoring to these webs, based on shifting chains and graphs, delivers remarkable predictive power. This opens up a world of possible applications:

Used cars: Using blockchain and AI to create relationship epochs that inform a buyer’s understanding of a car’s ‘past life’ would take much of the uncertainty out of buying a used vehicle. It would be much easier to understand, for example, if the car had been in an accident, the style with which previous owners had driven it, and if it had been serviced correctly.
Defensive driving: The rise of driverless cars raises a question: How much can you trust the car in front of you? Should you take another route, or change lanes? Blockchains will help drivers make smarter, more informed road safety decisions.
Insurance risk: Blockchain will help insurance companies assess and score drivers’ safety habits and aggregate risk levels.

Together, blockchain technology and AI have the power to transform the driving experience of the future, as well as opening up new vistas of intelligent process management in financial services.


Sourced by Scott Zoldi, chief analytics officer, FICO