The Finance industry has few physical endpoints. Yet despite lacking pumps, engines, actuators, or many of the physical processes commonly associated with IoT technology, banking and financial institutions stand at the forefront of digital transformation. After all, the CEOs of both JP Morgan and Goldman Sachs recently described their organizations as technology companies rather than financial institutions. The goal of this white paper is to highlight use cases that demonstrate the potential of IoT to transform the Banking, Financial Services, and Insurance (BFSI) industry.
The most mature pillar of BFSI regarding IoT innovation is the insurance sector. Auto insurance companies pioneered the use of telematics (technology that communicates safety and operations related data from automobiles to the cloud) to adjust premiums. This is referred to as usage-based insurance (UBI). However, extending insights gained from telematics or equipment data (mobile and fixed equipment) across industries holds a multitude of opportunities for the insurance industry. Any company with a vehicle fleet, multi-modal supply chain, or capital-intensive equipment is likely insured according to actuarial tables and relies on expensive investigations to sort out claims. Instead of (or, more likely, in addition to) actuarial tables, sensors can provide data on this type of equipment in real-time. Insights on critical alarms and maintenance records are also invaluable to dynamically profile risks and exposure. Insurance companies could, by extension, partner with lenders to help extend credit to a customer whose assets are nearing the end of their lifecycle. This is a win for the insurance company, the lender, and the policy-holder who can take advantage of a credit offering for new equipment.
Any company responsible for insuring workers has significant exposure to worker safety risks. In 2016 businesses in the US lost $60B to workplace injuries. Connected worker solutions and resultant data help organizations optimize these provisions by addressing the most significant cause of workplace injury: lapses in employee compliance with safety standards.
Insurance is not the only sector where usage-based business models can transform product offerings. Banks can marry the usage data from IoT sensors with transaction data they already hold to develop entirely new models for asset financing based on the actual use of factory assets, logistics fleets, mining equipment, energy production, and even agriculture. Bankers build a risk profile of customers based on a multitude of factors such as past performance and industry trends. However, in the end the banks will base lending decisions on their assessment of a client’s ability to use the capital to earn enough revenue to pay the loan back. IoT offers bankers and their clients the ability to maintain a complete risk profile in real-time. A banker with access to data from a factory machine that stamps serial numbers on goods knows the exact quantity of each type of good produced in a factory each day. Taking this approach a step further, a bank could marry this data from loading docks at distribution centers and see where and how many of the goods are shipped. This model also works in a proactive approach where insights and data can enable banks to jointly build financing models with manufacturers and service providers. This model applies to financing complex assets such as lines of manufacturing equipment as well as simpler transactions such as financing car leases.
The upstream segment of the energy and natural gas sector provides a dramatic, yet a logical extension of IoT’s relevance in the BFSI sector. The Deepwater Horizon disaster cost BP over $40bn. The sheer dollar value provides underwriters a massive incentive to ensure their clients are taking environmental and safety compliance seriously.5 Using IoT technology to monitor safety and environmental impact is a mature area, but there are also opportunities for financial institutions to monetize this data. When the real-time risk profile of a site is known through tamper-proof data source (such as Blockchain) rather than relying on manual inspections in difficult to access areas, investors will have a complete risk profile as well as information that can be used to assess the yields from an exploration site.
Finally, retail banks are immediate candidates for IoT innovation. The ATM is the most common physical endpoint for any retail bank, and, to many, serves as the only non-human physical interface between regular people and the finance industry. The model is simple: insert a card, enter a PIN, and retrieve the money. One must ask, however, what does a piece of plastic, even one embedded with a chip, do that a mobile device or biometric scanner cannot? Instead of using an easily-spoofed card, why not use a mobile device that can not only verify identify but can also take a short interaction at an ATM and turn it into an opportunity to provide a customer personalized offers from other service lines such as personal loans or different savings vehicles.
The above are both examples of conventional IoT applications. The examples to follow are perhaps more derivative, but also hold even more transformational potential for the BFSI industry.
Banks also have a tremendous opportunity to capitalize on the transaction data held across multiple industries. As information about Wall Street grows more and more transparent, how can IoT data on asset utilization in factories, mines, supermarkets, etc. provide an advantage to investors? By examining purchasing decisions as well as usage trends, a bank would be able to profile customers such as UPS or DHL to monitor delivery activity during the holiday season and make decisions based on real-time data rather than waiting on reports on consumer sentiments.
Commodity traders feast on data: agricultural yields, consumer behavior, weather patterns, etc. Why not connect a trader who specializes in pork belly futures, to feeding trough behavior at a few bell weather hog farms in Iowa? With the proper analytics engine, the trader is no longer waiting on reports but is trading based on the same day feeding behavior of hogs he is buying or selling. This same scenario holds true for any trader relying on any agricultural yields. This value stream could venture well beyond commodity traders to farmers and end consumers. For example, were the type of data above to indicate a demand spike for a product like grass-fed, grain-finished beef, farmers would be able to justify their borrowing to purchase grass-fed calves and grain feed to their lenders.
Commercial realty lending is another field where a tweak to the way IoT is employed holds enormous potential for driving value. Insurance agencies, lenders, and even REIT managers benefit directly from sensors that track foot traffic, consumer behavior, and even energy usage. This information, correctly applied, would provide a valuation based on the real-time behavior or occupants rather than making projections based on comparable properties.
As the IoT industry matures questions change from ‘what can sensors detect?’ to ‘what information will allow you to make better decisions?’ IoT in financial services represents the significant opportunity to figure out how data from tangible assets can inform decision making over electronic flows of capital. Financial institutions need to take the plunge and invest in the expertise, platforms, and data integration that will allow them to derive value from all data available. The data is already at the fingertips of the BFSI industry; it is time to grasp it and monetize it.
