Historically, insurers have relied on data to assess and price risk. But without the capability to determine and maintain data veracity, they are vulnerable to significant risk.

Last week, I discussed Citizen AI, one of the trends of Accenture Technology Vision for Insurance 2018. While AI is conventionally seen as a way to improve efficiency, Accenture looks at the more nuanced application across the enterprise to augment human ingenuity—we call it “applied intelligence”—to solve complex challenges, develop new products and services, and break into or create new markets.

But AI is only as good as the data it is provided. And as insurers draw on an increasing number of external sources, the threat of unverified data creates a new vulnerability that could place the entire enterprise at risk.

In order to bring the intelligent enterprise to life, we need to first address issues of trust. The ramifications of not ensuring trust will be felt both internally and externally. Within the organization, operational and strategic decisions will be affected; on the outside, regulatory fines or sanction in the court of public opinion are significant threats.

Data veracity: Systematizing trust

Even the most advanced analytics capabilities are only as good as the data that it crunches. Insurers have a unique opportunity to transform their enterprise with AI, but it requires an equal and intentional focus on data veracity. This is especially salient as insurers push toward fully autonomous decision-making. These decisions aren’t just a question of how much an insurance policy costs, and why—they have implications for business and society as a whole.

Consider that Accenture Technology Vision for Insurance 2018 found that 80 percent of insurance organizations are increasingly using data to drive critical and automated decision-making at scale. And yet, a study reported in the Harvard Business Review found that 97 percent of business decisions are made using data that the company’s own managers consider of unacceptable quality.

In addition to addressing data security (how it’s stored) and data ethics (how it’s used), insurers must have data grading capabilities. This means understanding the behavior around data creation, such as the data trail created by a person driving a telematics-equipped vehicle or the sensor network for an industrial system. Establishing the baseline of expected behavior around data is crucial to being able to record, use and maintain the data—and to being able to detect data tampering that can lead to poor decisions.

The issue of data veracity takes on even more importance as insurers explore partnerships to extend their value proposition and better engage customers. Insurers may no longer own the data themselves; they may have to plug into another partner’s system in order to access it. Even if they own it, the nature of the ecosystem will likely entail sharing data among the partners, all of whom have an obligation to use and secure the information properly. If for this reason only, insurers should choose their ecosystems—and ecosystem partners—wisely. As recent high-profile incidents have shown, organizations that do not safeguard and use their customers’ data ethically may find themselves under scrutiny from regulators and customers alike.

Technologies like blockchain and APIs can make it easier to partner with and trust other players at scale, especially when the insurer doesn’t own the data. I’ll discuss this in more detail next week. Learn more about the importance of data veracity in Accenture Technology Vision for Insurance 2018, and get in touch to discuss how Accenture can help you systematize trust.

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