By 2020, the Internet of Things (IoT) is expected to have as much as a $15 trillion impact on the world’s economy. Once the current challenges of integration, skill fragmentation, and agility associated with the fusion of IoT, Big Data, and Traditional Data systems are addressed, data omniscience can be achieved, enabling innovative new ways for consumers to access and use financial services. With this new technology, the following scenario can become a reality:
Dan Moody drives to his local bank branch. As he approaches, the bank’s app recognizes that he is approaching the branch, and immediately runs analytics that predicts the reason for Dan’s visit. In this case, the app uses fog and cloud analytics to determine that the customer typically visits the branch twice a month to get documents notarized and to make trust allocation adjustments. Since this requires private banking services, the bank branch automatically places the customer in the private banking queue.
Once Dan passes the front door, a beacon communicates with the Bank’s phone app. The phone app sends an alert to the bank noting that Dan has entered the bank branch. Dan then receives a message on his phone that asks him if he wants private banking services today. He responds “yes” and is immediately directed to a conference room, where “Samantha Williams will meet him in 5 minutes”. Dan goes to the conference room where a beacon confirms that he has entered. This alerts an associate to ask Dan if he wants any water or coffee while he waits.
While entering the bank, cameras performed facial and other biometric analytics on Dan’s face and body structure and verified that the person who entered was likely Dan. To improve this determination, IoT based analytics verified that the customer was driving his usual IoT connected car, was carrying his usual IoT connected smartphone, and was wearing his usual IoT connected smartwatch. Additional analytics on the smartwatch health data determined that the customer was having a bad day. The threshold for authenticating Dan was met and is forwarded to Samantha’s pad computer. She knows that she will be meeting Dan in 4 minutes, he has had a bad day, will likely need notary and trust allocation adjustment services. She also was notified of recent account actions and specific information on the customer’s taste preferences.
This information enables Samantha to enter the conference room, provide better customer services, and then complete the transaction more efficiently. In this case, service was so good that, when offered, Dan accepted the bank’s IoT payment service, that enables his refrigerator to sense things like his household has run out of milk, and then automatically order a fresh carton from the local grocery store. His car is also attached to this payment service, enabling him to leave the parking lot via the express exit.
Speed to data insight and ability to access all traditional and all IoT data all of the time is the key enabler to this kind of next generation banking solution. Implementing the above use case requires the ability to (1) Detect a data event; (2) Determine a Root Cause; and then (3) Respond by changing one or more controls (data, process, or physical). The speed of change is only limited by the change validation and verification required for health, safety, and financial reasons. With this toolset, companies can conduct real time experiments while incorporating traditional data science practices on the expected 100 fold increase in the amount of data collected by financial services companies by 2020. This new data can better help the financial services sector serve customers and provide more innovative product offerings to help the consumer of the future.
In essence this change is:
From Static Data Decisioning – where devices are pre-programmed with a static processing workflows. Once deployed, updating or changing a workflow is a manual process that typically takes weeks to months. Systems assume a constant function throughout their lifespan.
To Dynamic Data Decisioning – where devices are pre-programmed with a set of goals and devices figure out what algorithms and workflows to use to accomplish said goals. Goals can be updated in real time to reflect the ever-changing world. Systems assume a evolving functions throughout their lifespan.
This change is enabled when the integration, skill fragmentation, and agility associated with the fusion of IoT, Big Data, and Traditional Data systems are addressed. Once this happens, the true value of the IoT is unleashed. Prepared Financial institutions will be positioned to benefit immensely.
- A consolidated team collaborates to manage a set of systems
- Most systems are a data silo (even big data systems)
- Most processing is batch, even in the streaming domain
- Long timelines required to respond to changing operational requirements
- Centralized Operations Center, Distributed Data, Responsive Operations, Self Organizing Systems
- A blended team
- Data is shared between systems with minimal duplication
- Blended Fog+Cloud Systems: self-repairing, self-optimizing
- Shared algorithms between systems
- Near-zero timeline required to respond to changing operational requirements
Chris Howard is the Founder and CEO of the Kersplody Corporation.