The Internet of Things – Making sense of the data

As more devices become connected to the Internet of Things, ever growing quantities of data are produced. Ville Sointu, Head of Emerging Technologies at Nordea, explains how this data has the potential to transform financing models and business decisions for companies and individuals alike.

The term Internet of Things (IoT) is used to describe any device, machine or appliance that is connected to the internet. This expands the scope of the internet beyond computers and mobiles as devices and everyday objects that were not traditionally connected to the web come online.

For Ville Sointu, the definition of IoT is a straightforward one: “Broadly speaking, we consider IoT as all of the different connected devices with sensors that are producing data. Within that scope could be everything from your connected car that is communicating with navigation systems, right through to industrial internet applications like production machines and even ships and airplanes transmitting a lot of data.”

Internet of every(things)

Over recent years, the growth of connected devices has been exponential and according to a prediction by Gartner will reach over 20 billion units worldwide by next year.

Ville Sointu says: “There are plenty of connected devices out there already. The smart usage of data in connected cars is a good indicator of what is going on. For example, if you plug in a device which is connected to your insurance company, you might receive a discount on your insurance premium if the data shows that you don’t have the habit of doing excessive acceleration or braking. They will see you are a safe driver and therefore could give you a discount. On the industrial side of IoT, the most obvious example is an airplane; a highly complex connected device that produces terabytes of data every hour. Today, this flight and airplane data is used by manufactures and operators for optimising the maintenance and up keep of these airplanes.”

The airplane industry also provides a good example of where IoT could be used to develop more tailored financing models in the future.

“One of the common things for airplanes is they are extremely expensive and almost always financed in one way or the other. The question is, could we actually utilise the usage data of connected industrial devices like airplanes and create more innovative financing models based on the actual usage and depreciation periods of those parts? An airplane engine’s actual usage and lifetime is the underlying point of interest in the context of finance for example.”

Choosing where to go

With potentially unlimited streams of data produced by billions of connected devices, deciding which project to pursue is always a question of finding the one most relevant to customers.
“The first step as always with anything we do is that we actually need to talk to our customers as there is no way we can come up with a new system, product or solution and then go to our customers and start offering it to them out of the blue. By listening to our customers and understanding their actual needs and the IoT data that they produce, we will be able to create data driven services that directly add value to our customers,” explains Ville Sointu.

“For corporate customers, this conversation is more focused on trying to find innovative financing models for the services they offer. For individual customers of ours, the discussion is around ‘are you willing as an individual to share some data about you and your life in order for us to make better consumer credit decisions?’ That is a valid question that can be related to things like life insurance and all sorts of risk associated financing products like mortgages. If you are happy to share your health data with us, for example, that allows us to make a better risk decision on your behalf for a long-term mortgage application. It is always a balance of using the parts of the data that you are willing to share and then ultimately for us, the ability to consume this data and make actionable decisions on it.”

Understanding the data

Leveraging already well established processes for co-innovation with customers, the task now on is to develop ways of using the extra data streams available to uncover new sources of value adding services.

“Currently, I am trying to bring the IoT element into our talks with customers to try to make them understand the value of the data that they are producing and then offering them the ability to monetise that data. If we look online today, the data people are producing is generally being used unilaterally by companies for sales and marketing purposes where there is only a one-way relationship of monetising people’s data,” notes Ville Sointu.

“As a bank, the question we want to ask is, ‘would you be interested in creating a more transparent relationship with that data?’ If we are able to put a monetary value on your data, then we go on to say, ‘you will gain this actual benefit based on these attributes.’ The basic model is to give our customers the ability to have informed consent on the data they are sharing and present them with highly personalised financial services options as a reward for doing that. I think that is a better model where everything is built around the concept of informed consent. The customer’s data sovereignty is important to us.”

IoT generated transparency

Ultimately the increased data generated by IoT will give both companies and personal customers increased transparency around their business and daily lives. This will enable them to make some more informed decisions that will hopefully lead to beneficial outcomes such as discounts.

“If you look at the business of banking in general, many of the reasons we price the products and offerings the way we do are related to managing risk. Effectively we place a risk margin on the things we do and traditionally these risk margins are built on a very broad set of assumptions. Taking a large spectrum of cases, the average outcome is then applied to the individual case,” adds Ville Sointu.

“Now what we want to do, especially if we have verified and trusted IoT data that we can utilise, is to take decisions down to an individual customer-by-customer level. The only way to be able to do that in a cost-effective way is by automating this process to the extent that allows us to be efficient with the usage of the data. Therefore, creating tailored services based on the exact individual, company or business case situation and building a real-time economy based on this data exchange.”

Data sources

The multiple streams of data sources offered by IoT, create the possibility for a bank to develop contractual frameworks based on the data streams. These can then result in an agreement either between a bank and a customer or two customers of the same bank with the bank acting as the broker of that agreement with the chance of being able to insert its own products into the equation.

Ville Sointu explains: “Let’s say the bank has a customer that produces doll making machines. The bank has another customer that wants to produce dolls but can’t afford the machine to produce them. They therefore cannot make the upfront payment to buy those machines from the machine manufacturer and start producing those dolls. The wannabe doll producer might already have an order in place to sell 20,000 dolls but they don’t have the ability to produce them.”

“As it happens now, the customer who wants to produce the dolls would come to the bank and ask for a loan to buy the production machines, showing that they already have a potential order for the dolls. Following that, a long negotiation about the loan would then take place.”

“The way that IoT could hypothetically unlock this situation is very interesting. There would be the possibility of creating a ‘machine as a service agreement’ between the machine producer, the doll manufacturer, a credit institution and ultimately, the buyer of the dolls. An electronic contract, agreed by all parties and brokered by the bank could be created where the machines producing the dolls produce data via IoT that triggers payments based on actual machine usage, creating doll production as a service. At the very baseline of that equation it just becomes an automated contract. Ultimately, it’s either the machine producer or a credit institution that has to foot the bill in actually producing those machines in the first place but now that there is full transparency on the data flow and the electronic agreement, this becomes a far more transparent data set to enable a credit decision to be based upon. Now the bank or the credit institution no longer has to make a decision completely based on the trustworthiness of the doll producer and their business plan; we can base the decision on a very transparent set of data and are subsequently able to automate large parts of this credit decision.”

Of course, as with any new system, ensuring security and trust are critical. Ville continues: “A lot of work remains to be done to make this digital value chain secure and trusted. The security problems in the current IoT space are well known and need to be thoroughly addressed before ecosystems like these can properly function but since the benefits of moving into a real time connected economy like this are well established, the goal is realistic.”

Quantifying value chains

In the end, IoT promises to make all sorts of value chains more transparent, accessible, and quantifiable from a risk-decision perspective.

Ville Sointu concludes: “I think that is where the opportunity lies and if you look at that model in general, it is not very dissimilar to what we are doing on the blockchain side of things with the we.trade trade finance platform for example. There we are doing the exact same thing; we are digitising the trade agreement between the SMEs and then inserting our own financial services based on that smart contract. In reality we are providing invoice financing and bank payment guarantees based on the mere fact that we have so much transparency with the data. Now in the IoT space we can actually have these connected devices producing that trusted data as part of this equation, but ultimately on a high level it looks like the same thing – it’s about data transparency and trust.”

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