The Role of AI in Auto Insurance
An alternative example is if customers submit photographs of the damaged vehicle online to an insurer rather than wait for a claims appointment, and have an algorithm assess the damage – that way not only does the processing time get reduced, but also the customer service experience would be improved.
Generative AI derives insights from unstructured data to allow insurers to craft more targeted offers and take smarter decisions – a key to transformation and a better customer experience.
Predictive Models
On the one hand, predictive models allow insurers to operate more efficiently. For example, predictions of when a vehicle will be repaired and when a customer is likely to swipe their insurance card at a repair shop can lead to faster repairs by shortening turnaround time in the shop. On the other hand, predictive models can detect fraud by identifying patterns that are characteristic of bad actors.
Through predictive analytics in insurance scoring, insurers are able to offer customers better and more accurate premiums, which can be beneficial, although they are sometimes difficult for consumers to grasp.
Some insurers are beginning to offer reduced rates for cars, based on driving behaviour monitored by ‘black box’ on-board, or telematics or ‘telemetry’, devices that record how often and how hard one slams on the brakes, whether they speed, park dangerously, head into bad areas and so on. This enables policies that are ever more granular and informed by impossibly high levels of predictive analytics.
Automated Decision-Making
Today, however, due to the expansion of computing power, memory capacities, cloud computing and big data technologies, AI systems have developed the ability to manage more complex algorithms and to sort through unstructured data faster than humans can – which in turn has sharpened insurers’ ability to streamline complex processes faster, with lower costs and higher employee satisfaction.
A fraud-detection system in an insurance company could leverage this feature to flag the suspicious claims at once, opening them for review by investigators to declare them valid or invalid – thereby, building trust and loyalty among genuine customers by settling payouts promptly and accurately in an age of false claims that make insurers bear heavy financial losses and lead the premium rates to remain lower – ultimately benefiting genuine policyholders. Also, it helps the insurers avoid unnecessary financial losses, while maintaining the reputation of their market, and keeping the premium rates within control.
Real-Time Monitoring
Insurers then need to collect, and stream, at the highest possible rate, as many vehicle-based data points as they can get their hands on from as many vehicles as possible, to maintain claim credibility and avoid loss of the data that undermines its credibility. To capture these ultra-rich data sets within minutes can’t be easy.
Insurance firms must also make sure their customers travel safely, and so they, too, need to use ADAS technologies and predictive analytics to spot dangerous habits of speeding or forgetting to buckle up. After identification, data needs to be processed securely before it can provide meaningful insights.
UBI car insurers install devices that track the driver’s mileage, the time of day when driving, where the vehicle accelerates and decelerates, and when and how hard it brakes; they then openly advertise that they use this information to assign drivers ‘driving scores’ to determine the premium price. Consumer advocates fear that such devices might be used to discriminate against drivers.
Customized Coverage
With technology innovation serving to revolutionise the way insurers determine price and perils, the winners on the open sea of 2030 will be the insurers who can deploy innovative risk-based products, streamline core processes and reduce costs by tapping into leading-edge technological trends.
AI-enabled automation can help to speed up the claims process which saves insurers from time and cost; but more importantly makes the whole process more compatible with the specialised requirements of various customers and their aspirations for their budgets.
Meanwhile, greater accuracy in claims settlements has translated into higher customer satisfaction, which has ensured better retention rate and enhanced reputation for the brand. For insurers, AI can detect fraud with comparative ease and help significantly reduce fraudulent payouts and losses.
So again, there’s a new possibility for using an AI explanation system to be more transparent in ensuring customer satisfaction or, more specifically, to ward off dissatisfaction or disputation that otherwise would lead to dissatisfaction and attrition.
