Other parts of this series:
Data has always been the lifeblood of the insurance business. The digital age has made it more important than ever, and many of the technologies and trends described in the [marketo-rtp-id id=”rtp-form-id” image=”” description=”” title=”2018 Accenture Tech Vision for Insurance” registration_page_link=”https://financialservices.accenture.com/ins-technology-vision-2018-rp.html”]suggest a future where data’s relevance for insurers increases even more.
Insurers interested in deploying AI, for example, need access to huge amounts of data to “train” the AI for its intended functions. As their need for data grows, so will their access to it. Common sources of real-time data across the industry now include:
- Sensor data from policy holders’ cars, homes and workplaces
- Data from drones and satellite imaging
- External data sources like government databases and social media
Monetizing this data, along with its associated platforms and algorithms, is a substantial opportunity for the industry. Accenture estimates it could be worth $28 billion in the next five years.
Yet an ever-growing reliance on data means insurers must also manage a new form of risk: data veracity. Inaccurate, biased, or manipulated information threatens to compromise the insights insurers use to plan, operate and grow. Insurers are learning to manage this risk as they explore new use cases for data.
Accurate, real-time data enables insurers to offer highly personalized products based on real-time assessment of contextualized data rather than on historical data, general needs and averaged pricing. Insurers are also leveraging data to incentivize customers to reduce their exposure to risk and to help them avoid incurring losses in the first place.
For example, StrongArm is an insurtech that uses sensors worn by industrial workers to collect real-time data about workplace activities and environmental conditions. StrongArm uses cloud-based artificial intelligence to analyze this data for insights on how employers and insurers can reduce workforce injury risk. StrongArm can also send real-time alerts to workers at risk of injury.
Another example comes from Habit Analytics, which uses real-time data from smartphones and connected devices in homes to create behavioural profiles of insurance customers. Insurers can use Habit information to monitor changes in a home’s risk and customize services to suit specific customers.
Incumbent insurers have been active in this space as well. For example, Travelers recently announced a partnership with Amazon to offer consumers smart home kits, home insurance quotes, and risk management guidance through the digital retail giant’s storefront. The kits include cameras, water sensors, motion detectors and a smart home hub. Eligible customers receive a discount on their home insurance with the purchase of a kit.
Some carriers are also using data to create disruptive business models. For instance, ZhongAn, a Chinese internet-only insurer, has established a big data alliance with Sinosafe Property Insurance and Urtrust Insurance. Through this alliance, ZhongAn collects data related to more than three million vehicles, which it used to launch Data Cube, a big data platform for ZhongAn’s auto industry partners, in early 2018. .
Yet for all these opportunities, data also exposes insurers to new risks. Eighty percent of the insurance executives surveyed for Tech Vision 2018 reported that their organizations increasingly use data to drive critical and automated decision-making at scale. But a recent study estimated that 97 percent of business decisions are made using data that the company’s own managers consider to be of unacceptable quality.
Clearly, insurers will need to do more to ensure the veracity of the data they use. They should start by building confidence in three data-focused tenets:
- Provenance—verifying the history of data from its origin throughout its life cycle
- Context—considering the circumstances around its use
- Integrity—securing and maintaining data
To establish these principles throughout the business, every insurer should build a ‘data intelligence’ function, drawing from existing data science and cybersecurity capabilities to grade the truth within the data they use. The foundational elements of such a practice will involve ramping up existing efforts. Insurers can start by embedding and enforcing data security throughout their organizations, and by adapting existing investments in cybersecurity and data science to address data veracity.
Ultimately, insurers will need to be vigilant about uncovering and addressing inaccurate data—some of which may be willfully manipulated by bad actors. Insurers will need to be able to track behavior around how the data is recorded, used and maintained.
For more about how insurers should address the looming issue of data veracity, check out the [marketo-rtp-id id=”rtp-form-id” image=”” description=”” title=”full 2018 Tech Vision for Insurance here.” registration_page_link=”https://financialservices.accenture.com/ins-technology-vision-2018-rp.html”] Or, come back after the holidays for a look at another trend: frictionless business.