Part 2 in a 2-part series. Read Part 1.
In the first blog in this series, we examined the way that real-time compliance is changing the entire landscape of regulatory reporting. Here, we’ll look at how the Internet of Things, blockchain, and artificial intelligence will help capture and employ the real-time data required to make this a reality.
The Internet of Things (IoT)
In the IoT, devices in the field read real-time data and make simple decisions. For example, when a temperature sensor detects higher temperatures than the prescribed threshold, it triggers the shutdown of the equipment automatically to maintain safety. This kind of computing, where decisions are made in decentralized nodes rather than on a centralized server (such as the cloud) is called edge computing. We could probably call decisions taken on such edge devices “edge decisions.”
In the oil and gas, chemical, and hazardous materials industries, filing reports to the Environmental Protection Agency (EPA) and similar regulators is very common. Today these reports can be autogenerated by extracting data from the IoT sensors in real time. Very soon, we will see regulations where the EPA defines edge decisions instead of just mandating filed reports. One could term these “edge regulations.” Regulations followed and filed in real time would transform the entire face of the regulation industry and the technology behind it.
If we look at the Bernie Madoff case, for example, billions of investor dollars could have been saved if real-time data was available. Another example of bringing change to the regulations landscape is real-time tax calculation integrated with IoT.
This is already happening in Amazon stores. Amazon Go is an early implementation of taking home what is in the basket, where offers, discounts, and taxes are calculated in real time, on the go. In conjunction with e-payment wallets, this business model will only grow, and governments will be challenged to cope with new regulations that support consumers in real-time situations. In addition to providing data, IoT sensors can also provide provenance of this data.
Blockchain is a simple way of using a ledger to capture a transaction, where it assumes that the parties involved in the transactions do not trust each other and have different systems of record-keeping. This means that any blockchain transaction is immutable and decentralized. Since the basic nature of blockchain is to be immutable, this gives it a unique attribute, which is the provenance of the transaction. It can be argued that the sensors could be hacked to falsify information. How can we prevent this from happening?
In the example of the Volkswagen-EPA emission test scandal: If we can authenticate and ensure the provenance of each sensor that feeds data, we see the possibility of tracking the entire chain of events. This concept is already gaining popularity. Know Your Customer (KYC) is a standard way of authenticating a blockchained individual or company so that data is immutable and secure. The next generation is Know Your Device (KYD), to authenticate the provenance of a device and the data it sends.
As we move into a sensor-equipped, real-time-data-based world, the approach of blockchain, provenance, and security becomes crucial. Governments are already adopting blockchain-based systems rapidly. This is especially true in the case of developing countries such as India and the UAE, where the national digital agenda is still getting formulated. These countries are leapfrogging from “going digital” to “going blockchain.”
Smart contracts (event-triggered contracts in blockchain) are already being used in many countries for taxes, charges, and tendering processes to ensure safety and transparency in businesses. Governments now look to implement provenance in supply chains for ensuring quality in materials and last-mile reaches. The scale and scope to be a part of this network is huge, and this also leads to the question of how an institution can detect fraud knowing that data is secure, immutable, and real-time.
We see many applications of AI, from identifying faces in photos and videos to high-frequency trading. Using the power of AI, governments and businesses can detect patterns of transactions, especially in real-time data. Fraud and outlier patterns are much easier to detect by training algorithms for recognizing them. Early detection of these deviations can prevent scams of monumental proportions.
In the industrial age, generation of information and access to information presented challenges. In contrast, in the current age, we are going to drink information out of a firehose. Regulation changes are inevitable, and how technology companies adopt and support the ecosystem determines the future of “disruptions in the regulatory landscape.”
This thought is summarized very well by Rajeev Soota, senior general manager of IT at Usha International Ltd, commenting on the massive regulatory changes that were ushered in India in July 2017:
“With GST bringing in real-time e-way bills and tax reconciliation at every invoice, reporting is no longer periodic. Being real-time makes it imperative for companies to stay compliant.”
For more on how emerging technology is transforming traditional business models, see Overcoming Big Data Challenges With Real-Time Computing.