Financial services firms have long trafficked largely in intangibles, from counterparty risk and online bill payment to assets that were previously tangible but increasingly are not, such as stock certificates and even money itself. So the Internet of Things (IoT)—a suite of technologies and applications that use embedded sensors in physical items to generate customer, operational, and other data that can be aggregated and analyzed for valuable insights—might not seem directly relevant to the way financial services institutions (FSIs) do business.
But IoT may, in fact, transform financial services. Most pieces of information have roots in the physical world—for instance, a logistics firm’s stock price may depend on the number of packages shipped, while wheat futures may change based on rainfall levels. Already, many FSIs are using sensor datato improve operational performance, customer experience, and product pricing. Perhaps the most mature example involves the development of usage-based insurance, in which sensors in automobiles or, increasingly, smartphone apps automatically provide insurance carriers with information on vehicle driving history and driver performance.¹ Another example is in commercial real estate, where sensors in commercial buildings can help companies better manage energy usage, environmental comfort, and security.
To gain some insight into future IoT scenarios in this industry, the Deloitte Center for Financial Services engaged with a group of academics, analysts, and entrepreneurs with expertise in financial services and technology to imagine how IoT technologies might generate new use cases for various FSI segments in the future.
Physical, performance, and behavioral data generated from biometric and positional sensors for individuals, as well as shipping and manufacturing control sensors for businesses, could provide new opportunities for credit underwriting, especially for customers lacking a credit history. A challenge would involve developing an understanding of which data points best predict an individual’s creditworthiness.
In addition, given that banks finance the lease or purchase of many physical items, there may be opportunities to tap into data from sensors monitoring the condition of these goods. For example, lenders could partner with electronics or household appliance manufacturers to proactively make credit offers to individuals if their purchased items begin to show noticeable wear or face imminent failure. Leasing companies, too, could monitor the condition of leased assets in order to determine a more precise residual value of assets at lease expiration.
Firms on both the “buy” and “sell” sides of trading and investing activities could benefit from enhancing their capacity and capability to gather, store, and analyze huge amounts of real-time, IoT-generated data, especially when combined with continued acceleration in algorithmic trading. By removing the human element and obtaining more comprehensive real-time data flows, for instance, from sensors that monitor manufacturing plant activity or foot traffic in retail stores, capital market analysts might develop analytics that could better evaluate suspected market bubbles. However, it is also possible that algorithms might be unable to account for shifts in consumer demand or geopolitical events, leading to faulty conclusions that could actually create bubbles.
The longer-term impact of the adoption of automotive sensors is one of the more interesting IoT scenarios for insurance carriers. Already, the industry is grappling with the strategic implications of self-driving cars, suggesting a shift from automobile casualty insurance, where the driver is at fault, to product liability insurance, where the manufacturer may be held liable.⁴ With IoT, insurers may also gain better information on potential product defects, allowing them to more accurately price coverage. However, these changes in traditional coverage models, as well as the potential for fewer accidents, could result in insurers seeing decreased income from premiums.
In addition, usage-based insurance may lead policyholders to request more on-demand coverage to reduce their costs. For example, in personal life and injury insurance, all manner of risks are covered under a single policy, but with the development of more fine-grained data about personal behaviors, firms could tailor and essentially unbundle coverage to potentially add or eliminate certain risks. This would make underwriting and pricing a more complex undertaking for insurers, but could also yield improved customer satisfaction.
In commercial insurance, deploying sensors on shipping containers and transport vehicles could provide insurers with the opportunity to enhance shipping insurance coverage. The ability to better detect and model risks due to theft or damage could move the pricing of these products from an actuarial exercise to one that better assesses risks and losses in real time.