Read views of Infosys experts on how blockchain technology offers an unprecedented opportunity to transform the transactions of the future, how its adoption will create newer value propositions and what is required for its integration into larger IT systems.

September 19, 2019

Unblocked, Unchained

In Quentin Tarantino's 2013 classic 'Django Unchained', Leonardo Di CAprio's colorful character Calvin Candie famously utters: "Gentlemen, you had my curiosity, but now you have my attention."

Well, that's what blockchain enthusiasts die to hear during most client conversations. However, given that blockchain or DLT (Distributed Ledger Technologies, as it is famously referred now) belongs to a pack of 'new age digital technologies', it gets to go through the pain of being regarded as any other technology in its pack & thus, routinely faces the question - Is it different, or is it really different?

Let us try a bit to understand this world of new-age digitech that is often looked upon as a beautiful rainbow, that promises to fill color into the otherwise bland world of technologies of the recent past, that by the way, operate wonderfully well inside of the enterprises already. So, what does this rainbow promise, really? Well, each color promises its unique merits over-the-top of existing IT landscape of an enterprise. They range from Artificial Intelligence (AI), Machine Learning (ML), Augmented Reality (AR), Virtual Reality (VR), Blockchain (/DLT), Internet-of-things (IoT) so-on.

However, past few years of digital evolution have amply shown that true value of digitech to an enterprise will emerge only when those technologies are mixed in suitable proportions, considering the underlying business problems as the core basis in the first place, instead of force-fitting technologies into the existing IT ecosystem.

 In order to understand this better, one must observe the nature of new age digitech. They essentially fall into two broad categories: Inside-out technologies & Outside-in technologies. Inside-out digitech could be defined as ones that provide wonderful over-the-top usability to the existing (read: boring) end-user experience of ERP, MIS, Core systems of records of past three decades. These technologies make it possible for organizational users to fall in love with their existing systems once more, all over again!!! Analytics-driven actionable dashboards powered by AI/ML, multi-device AR/VR driven interfaces - are typical manifestations of such technologies. Basically, they allow for organizational users as well as end-customers of an enterprise to access and perceive their existing systems better.

But, some things are not fully sorted out even after that 'digital over-the-top makeover' done intrinsically. Those things are to do with the 'external nature of an enterprise' i.e. the manner in which an enterprise is perceived in its value chain, that consists of a larger group of business stakeholders like vendors, distributors, bankers, regulators, shippers, insurers et al. Those things are to do with the 'Outside-In' nature of the business, and thus, related to a connected world - a world of coopetition, driven by an innate collective need of a value chain to democratize itself. Thus, the second kind of digitech are the 'Outside-In' technologies, with Blockchain or DLT as their flag bearer.

Over the last 4-5 years, from the time the industry started to take note of DLT's seriously enough, they have been put through initial experimentation, albeit with the hangover of "Inside-out' technologies, with trends like - find the most convenient business processes that are internal in nature, that are least on risk, that don't involve external (read: difficult-to-influence stakeholders), that are not necessarily core to the business & therefore conduct quick "proof of concepts" around them, and let them die slow death ie show them the door. This 'POC-driven phase of frivolous use cases' that went on over last 2 years, for use cases that were internal, least-risk & non-core in nature, has been the primary reason for many analysts to have blocked (read: pan) this technology in its initial days, instead of panning the way in which the technology was deployed in the first place.

Blockchain is not an 'over-the-top' technology. It is in fact an 'under-the-floor' invisible technology that provides an exclusive conduit for not just a singular enterprise, but to its entire value chain. All stakeholders in a given value chain will push something selective & useful into blockchain, and retrieve something relevant from the blockchain, thereby slowly moving to the idea of 'value chain driven' networked approach of doing business. 

* That, in essence, is the power of blockchain. It literally chains the enterprises seamlessly, who are otherwise connected by the purpose of their existence, but disconnected by way of their respective technology choices of the past. So, looking at blockchain as an 'inside-out' technology is of no use beyond the initial-stage POC's. If an enterprise truly wants to benefit from blockchain, they must mobilize their entire value chain to experiment, question and suitably accept or reject the technology after conducting multi-enterprise networked experimentation that is 'outside-in' in nature.

* That 'multi-enterprise business network' is a true manifestation of 'outside-in' technologies such as blockchain. But even that is not enough...not just for this blog, but in general! In our experience thus far, no single technology is complete in itself. When the genuine and relevant business problem becomes the core of any technological consideration, then it is often noted that one needs a medley of such technologies instead of any one specific technology. In this context, Blockchain, when mixed with other digitech like IoT, provides a viable basis for connected enterprises to observe collective value in such initiatives.

That's when the enterprise world will shine brighter in the emerging white light. This is what Newton proved during 1700's through his famous colored disk experiment, that when all colors in the rainbow are mixed, all of them fade out to provide bright white color. Infosys stands for creating and facilitating such experiences...more on this, in the next blog in this series...

June 13, 2019

Factory of Future: An 'A-C-T-I-O-N' Oriented Approach

Manufacturing sector is about to change and change for better. This industry has seen three Industrial Revolutions (IR) and next is just around the corner. Some call it Industry 4.0 or 4th Industrial Revolution while some simply summarize it as digital revolution. While most of today's factories have adopted computers and automation in some form, there is still significant manual intervention required resulting in lower productivity and reduced efficiencies.

Despite automation, there is no real-time view into health of assembly line or inventory condition. Some examples are:

  • The maintenance of machines is still done on the basis of age-old standard servicing calendar. This approach forgoes predictive maintenance that sometimes leads to costly production halt.
  • The factory is fitted with multiple cameras but, the video feeds are still monitored manually which many a times is ineffective to prevent any accident or to avoid non-compliance.
  • While the physical supply chains work across organization boundaries, the digital fragmentation results in costly duplicate data entries, reconciliation issues and subsequent delays.

Research in computing, both on software and hardware fronts, has led to some promising technologies such as Artificial Intelligence, Blockchain, Cognitive Bots, Mixed Reality and Internet-of-Things. Factories of the future can leverage these next-gen technologies to make them Automated, Connected, Transparent, Intelligent, Omni-Channel and Nimble (A-C-T-I-O-N).   

Automated: The next-gen robots, often called as Cognitive Bots, are capable to handle multiple scenarios and would aid in higher level of automation. For example, an incident tracking 'ticketBot' can automatically classify incidents based on text or image and also take appropriate action to close the ticket.

Connected: Machines and Assembly Line act like nervous system of the factory. Keeping it running and in good health is of paramount importance to any manufacturer. The IoT sensors embedded in machines and assembly line provide real-time status of the factory line helping take appropriate actions and prevent any unforeseen breakdown.

Transparent: Manufacturers have wide and deep supplier and distributer network but, they lack end-to-end visibility into the supply chain. A Blockchain like network can bring multiple supply chain participants on a single network providing complete product and parts traceability, thereby enabling faster procure-to-pay cycle. Smart Contracts can help increase automation level by codifying business clauses between supply chain partners on Blockchain and reducing manual intervention and costly errors.

Intelligent: Artificial Intelligence can process IoT and other sources of data for various business scenarios such as predictive maintenance of machines and assembly line. AI-OCR techniques can process hand written documents automatically. AI-Computer Vision can process camera feeds in real- time to detect anomalies such as a worker not wearing safety gear or unusual cracks on metal structures within factory.

Omni-channel: The devices that staff and workers use can vary from traditional desktops and phones to smart watch and smart phones. Omni-channel strategy will make data accessible anytime, anywhere from any device. For example, the training that new worker underwent using desktop can be re-attended or continued using a smart phone or AR/VR devices.

Nimble: In this era of fierce global competition where only the fittest survive, enterprises need to adopt an agile approach. All the benefits of being connected, intelligent and transparent would be lost, if organizations are not nimble to take actions.

Infosys Intelligent Apps Platform (iIAP) can help manufacturers create factory of the future in an A-C-T-I-O-N oriented approach. iIAP has a generic IoT framework which has easy device configuration, IoT gateway and rule chain like features for complex event processing. The AI and Big Data Analytics framework helps in getting real-time insights and automates bots to take appropriate actions. The Blockchain framework provides services for inter-org data sharing over various open source distributed ledger platforms. iIAP can be deployed either on open internet cloud or on-premise as per individual manufacturer requirement.

May 27, 2019

Reimagine Trade Finance with Emerging Technologies


In the previous blog, I had discussed how blockchain can help in bringing in various stakeholders of trade finance ecosystem on a single network and how it will benefit financial services companies to efficiently carry out their transactions. We also discussed the benefits of keeping various documents hashes on blockchain to reduce incidents of fake document submissions and furthermore tracking status of the trade between all players with blockchain acting as single source of truth. The channel or sub-ledger capabilities offered by various blockchain platform will improve data privacy and only the relevant stakeholders of the trade will be able to view data on blockchain.


While we have only seen benefits of only one emerging technology, we can also look at role of other emerging technologies such as Artificial Intelligence (AI) and Internet-of-Things (IoT) in conjunction with Blockchain to gain higher benefits. At its core, AI is the capability of machine or computer to carry out tasks in same way that a human being does and requires human intelligence. Typically, AI is applied for tasks that take inputs in the form of text or documents, natural language speech or vision, process the input and produce the output without human intervention. IoT is another emerging technology that connects physical objects with internet. There are various sensors that provide continuous measures of physical object health such as temperature, humidity, pressure, weight etc. With IoT, these measures can be monitored from remote location in real-time and decision can be taken.


The AI-IoT-Blockchain technology combination can help us to reimagine trade finance process. At the initiation and signing of contract between importer and exporter, the blockchain based identity framework can be useful to verify the claims of each party. The verification of exporter claiming that he or she is compliant with an international regulation, then that certificate or license can be verified through blockchain based credential management platform. Once the verification is done, the digital contract copy can be kept on blockchain for future reference purpose. This would help in quick custom clearance and dispute resolutions, if any.


During the issuance of letter of credit and afterword during verification of documents such as invoices, bill of lading, insurance policy etc, AI algorithms can help in automatic content extraction of various documents. This would reduce huge manual effort that goes in currently at financial services organizations. The AI process can also bring out any anomalies or any critical misses from the document to assist decision makers on approvals and rejections. One of the largest international bank has claimed productivity improvement benefits upto 50% by using AI techniques. Similarly, IoT tags on goods can send real-time location and their condition data to all participants of that trade via blockchain thereby further reducing risk of payments for damaged or missing goods.


In summary, Trade finance domain is ripe for innovation to bring in more efficiency and reduce risks by using emerging technologies mentioned above. Infosys Intelligent Application Platform brings convergence of these technologies and help accelerate innovation in various domains. One can start with small proof of concept to validate the ideas and then deploy the innovation at scale for complete business process which can then be rolled out for multiple geographies. Please connect with us in case you are further interested. 

Role of AI, IoT and Blockchain in a Smart City

Smart City means different things to different people and no universal single definition exists for this new urban phenomenon. Depending on various factors such as development level, reform agenda, citizen ambitions and available resources, the concept of Smart City changes from place to place and nation to nation. Meaning of a Smart City may be different in one country than other parts of the world. According to Smart Cities Mission of India, a Smart City is a city that meets the needs and aspirations of its citizens in terms of infrastructure and various services delivery. This would require town planners to develop city that stands on social, economic, physical and institutional pillars for a comprehensive development.

The role of technology is crucial in achieving the desired objectives of better urban quality of life and economic growth in the local city areas. The ICT applications generate very high volume of data which will further needs to be stored, processed and analyzed in a smart city infrastructure and service delivery improvements. Such development is expected to create more jobs, improve quality of life and raise income levels of all, together termed as inclusive development. Also, for inclusive development, citizens of the smart cities need to actively engage in proposed changes and governance aspects constructively for their own aspirational smart city realization.

AI refers to machines which are able to learn, think, perceive and solve problems similar in a way human beings do. AI has evolved to much broader concept which can potentially benefit vast number of tasks resulting in improved productivity. Internet-of-Things (IoT) term was first coined by Kevin Ashton for embedding sensors to objects in supply chain context. Objects with sensors can provide their status and location details in real-time to other remote systems over the internet. Blockchain, also known as distributed ledger, is a technology in which exact same time-stamped data is shared across all participants on a network and verified using consensus mechanism without the need of any central intermediary.

Smart Cities will be enabled by electronic sensors, IoT devices and cameras to continuously monitor infrastructure. This will lead to large amount of data being created which will require data collection, data processing and advanced data analytics technologies that will transition the city from urban city to smart city or intelligent city. Citizen services many a times are deteriorated due to government departments operating in silo mode and store different versions of truths. Grievance resolution process in most of the government departments is plagued by poor standards of communication and age old physical paper based practices. Many times, records are manipulated to show compliance.

AI technology is evolving and expected to deliver large competitive advantage to wide array of processes by offering huge incremental values such as automation, 24×7 service delivery and predictive maintenance. The sophistication of AI powered software solutions can lead to improved efficiencies in Smart Cities and can lead to completely new urban paradigms that transform processes related to traffic management, energy and water supply distribution, and make cities more sustainable. AI can adapt intelligently to changing conditions reported from IoT sensors generated big data and user behavior. Without analyzing the continuously generated data from sensors, smart city planners would lose a good opportunity presented by smart city's big data and thereby making it harder to achieve smart city objectives.

Similar to AI, Blockchain is another revolutionary evolving technology that can help in improving overall service delivery through its various features such as transparency, network based trust and traceability. The tracking of various government procurement using Blockchain can potentially make it very difficult for counterfeiting and frauds. Experts believe Blockchain will transform electronic transactions management for many different areas such as banking, insurance, supply chain, healthcare, telecom, education and energy. E-Government services are no exceptions to it. E-Government services currently lack secure, unalterable, real-time sharable and trustable information management across all departments. These needs are pushing governments to look at Blockchain technology for a possible solution for redefining service delivery and information management framework.

While technology is evolving rapidly, it is also important to see how citizens will adopt to such solutions. Challenges in terms of data privacy, trust in technology, social influence and any other risks needs to be overcome before widespread implementation. As with any novel technology based solutions, starting small and improve as it progresses in agile manner can be a good way to initiate journey towards smart city.

Continue reading "Role of AI, IoT and Blockchain in a Smart City" »

March 25, 2019

Blockchain Capacity Building

Gartner predicts that the business value-add of blockchain is set to exceed $3.1 trillion by 2030. There is soaring demand for talented blockchain experts' familiar who can help demystify and explore potential use cases but 53% of hiring managers surveyed by Upwork cite access to skills as biggest hiring challenge. Infact, hiring start-up Belong reports that for every 10 jobs in blockchain development, there are only five people eligible to take up the job.

Although, blockchain developers are some of the top paid roles in software development earning between $150,000 and $175,000 in annual salaries on average there is an acute shortage of blockchain experts. Firstly, the fringe status of blockchain, where developers don't consider blockchain in the same esteem as Python and C++ has affected the availability of talent in the industry. Secondly, blockchain developers are scared of working with legal and security experts which is an essential part of blockchain development in the financial industry. Another major reason for shortage of blockchain developers is that developers understand that blockchain is not a universal solution and standards are still evolving.

Organization have adopted multiple strategies to combat the industry wide shortage. Organization like IBM are developing their talent in-house by creating training centers and providing personalized online training for its employees who are interested in blockchain. Similarly, blockchain platform IOHK provides free blockchain training to computer science graduates to create job-ready employees it can hire. Secondly, some companies are outsourcing to agencies and freelancers that specialize in blockchain development. Another strategy that companies have adopted is to hire "now-collar jobs", a term indicating a job class that does not require a college degree but has some training.

However, courses on blockchain by platforms such as Coursera help develop the talent required by the industry while CodementorX allow companies to hire freelance developers on their platform. Also, the development of the blockchain community has helped companies find recommendations or referrals for blockchain developers. Blockchain has proved that it is here to stay and companies are planning multiple blockchain projects, the recognition of blockchain's capabilities have encouraged more and more developers to invest in their blockchain skills.

In conclusion, the evolution of blockchain has made it imperative for organizations to innovatively incentivize and hire talent. The hype surrounding blockchain has reduced and the technology is finding applications in multiple industries and generating revenue while reducing costs. The industry is set to soar and create trust in a trustless world.


November 9, 2018

Business Rationale for "Paper Boat" Projects in Blockchain

(Blueprint for a Decentralized Future)

I often find myself in conversation with prospective clients about potential engagement opportunities that I call- "Paper Boat Trials", aimed towards testing the waters before diving. Notably, these prospects haven't necessarily graduated from the "Blockchain tourism" phase but they have certainly become more discerning travelers. That is to say- they now have a fair understanding of the technology and more rational expectations; they don't want to revolutionize or disrupt the world nor do they want to have a Live implementation by the end of the year. They want to explore the "art of possible". They want to gain insights by setting up a demo network; they want to start building technical capabilities, start gathering the requirements and start exploring potential business implications; and more importantly they want to do so without creating too much mess (financial and operational).

What I call Paper Boat Trials (you may very well call them Proof-of-Concepts), are low cost, low risk engagements (most often in partnership with systems integrators and platform operators) to try out first hand and gather experience. While these projects/trials vary greatly in scope and execution methods, they do exhibit certain salient characteristics.

They are Business led, and have a fixed duration and a fixed budget. Applications rarely get connected to any Live legacy systems; no existing system ever gets switched off; user interfaces remain very basic and the data is often fictitious (dummy data to demonstrate a functionality).

What good are they? You may say- that these are pet projects to quench a thirst of curiosity or to spend the innovation budget for the quarter.

But there are other reasons why it makes sense.

Understanding the Dynamics of Network Participation

The value of a Blockchain network relies on the strength of the network effect (collaboration among the players of the value chain). A decentralized ecosystem for doing business will redraw the boundaries of operational silos (in the value chain); there will be new decision rules and power shifts.

Hegemony and control of some existing power players will wane, and they will have to be incentivized. Others will see clear benefits. Players with less clout or with significantly less stake in the game may be indifferent as long as the switching cost remains low.

Engaging stakeholders in early in the process can drive participation and provide an opportunity for the project team to assess the potential barriers to network-building and gain a knowledge competency that can't be replicated easily. The project can be extended to include Design Thinking Workshops with multiple stakeholders (the eager ones) under the same roof.

Isolated teams can get so immersed in solving technical issues or formulating frameworks that they fail to listen to the other side (the critical partners of the Business network). The barriers/inhibitions for each stakeholders are unique and has to be acknowledged before any adoption framework or strategy can be formulated. There has to be broad consensus or agreement among parties to collaborate on the first place- a shared vision (a charter); to reach such a broad consensus, project teams may have to incorporate principles of Behavioral Economics and Organization Design and invent new strategies for networking, incentives and collaboration. Without these collaborations (without this network) there can be no successful Blockchain.  Paper Boat Trials establishes that starting point to think about the dynamics at play.

Avoiding Costly Mess

Executives, although keen about innovation are often a little hesitant to spend too on them, especially if the exploration involves deep diving too much into the unknown. There could be many reasons for this hesitation. Not having a clear goal or an ROI makes decision making difficult to be justified. Exploratory or Innovation projects may lose track or relevance very quickly; it is not easy to determine when stop funding a project and extremely difficult to cut funds from a bleeding project after much investment had already been made.

Blockchain (or DLT), a foundational technology like the internet will have to wait its turn to take off in the everyday world.

There is wisdom in not going full throttle yet; in most cases there is after all no strategic urgency.

Starting early can help, and it is beneficial have the learnings (data) first hand, but early failures shouldn't swamp business as usual.  Paper Boat Trial is less risky with little impact on cash (usually the innovation budget comes from cash in hand not expensive debts). A cap on the downside risk makes it easier for executives to sponsor such projects. The business case for these projects apart from innovation could be around- Market sensing, prioritizing use cases and assessing potential impact on business, building Basic competency, exploring opportunities, market signaling etc.

Playground for the Decentralized Future

Firms may consider these Trials/projects as playgrounds to develop the necessary motor skills before they can jump into more real-life business implementations. Paper Boat Projects are unique opportunities to set up a basic infrastructure (technology and organizational) that can be leveraged by the firm to jumpstart future projects quickly.

These projects are laboratories of Business innovation. Business impact can't be assessed and strategic priorities can't be determined on spreadsheets alone. Business leaders (now more than ever) need to develop a taste of technology in order to make sense of the forces that is already shaping the world.

There could be a business opportunity lurking in plain sight to be discovered by the lucky or the competent.

Continue reading "Business Rationale for "Paper Boat" Projects in Blockchain" »

October 13, 2018

Analytics on Distributed Ledger (Reducing noise in the Value Chain)

I was reading a 2016 HBR article from Daniel Kahneman (et al) about the hidden cost of inconsistent decision making (refer notes for details), on my flight back to India last week, and wondered whether Blockchain could reduce disparity of view-points held by different stakeholders within a firm and across the entire value chain in which it operates. Firms are operating in an increasingly connected ecosystem with shared value; having consistency of view-points or decisions may have a big impact on driving multiparty collaboration.

One of the reasons why different stakeholders of the same value-chain come up with different outlook about the same situation is that, they are referring to different data sets for their analysis to start with.

Take for instance a re-insurance case in which the insurer has spread a particular risk across three different re-insurers. Now, each of these parties- insurer and re-insurers maintain their own ledger summarizing- claims, premiums, tax etc.

Each of these parties maybe using different tools to summarize their financial position, but more importantly each of them may also be potentially using different datasets to generate their financial summary, leading to inconsistent status for the same risk.

There could be several reasons for having multiple versions datasets to start with-

They may have been generated for different time periods; may have been extracted with different filter conditions; may have been consolidated differently by using different cleaning, imputation and aggregation techniques; or could be that one party had a more recent information than anybody else (transient information asymmetry). In short, the cause is largely on the ETL (extraction transformation and Loading) side.

Here is the case for transient information asymmetry- the insurer, being in direct contact with the insured will always have firsthand information about any new claim; this information may take time to permeate down to the re-insurers. In-the-mean-time their claims summary report will not tally.



At t1 a new claim comesàInsurer updates its ledger. But all re-insurers disagree. (Information asymmetry)

At t2 Re-insurer 1 and Reinsurer 2 gets updated information, but Reinsurer 3 is still having the old version. (Information asymmetry)

At t3 Reinsurer 3 updates its ledger and everyone is in sync.

Similarly, two Bank agents working on different versions of a customer's data will come up with different credit score or compute different applicable interest rate for the same customer. This discrepancy will cause severe damage to the trust and loyalty bestowed by the customer.

In global supply chain, a customer may get different status of goods in-transit from the supplier and other multi-modal logistics partners. This discrepancy may eventually get resolved but not without significant reconciliation effort- administrative overheads and payment delays (often leading to detrimental cash-flow pressures).

Having a source of data that gives a consistent view to all the participating value chain players is key to consistent decision-making. Enter Blockchain (or for that matter any DLT)...

In this Blog I have used Corda to demonstrate this idea of consistent reports for all value chain partners (but you can use any framework or platform).

Corda allows point-to-point messaging- only those members, who are party to the same flow have access to the shared ledger. This shared ledger forms the basis for Business Intelligence reports and Analytics that are uniform across participating members of the Value chain. [It is not necessary to comprehend the architecture of a corda node to appreciate the essence of reporting and analytics on Blockchain/DLT. Feel free to skip this section.]

A node (could be a computer maintained by individual participants of the network) is a collection of processes, a Vault, which contains the output state relevant to a party and a Transaction Storage that has key-value store for attachments, transactions, and serialized state machines (SSM).



The figure (modified from Corda documentation) depicts the Corda node and where BI reports can be plugged.

Re-insurers and the insurance company sharing the same Risk will have a common ledger on which they will query.

The nodes can choose to put this shared information (for a given point in time) in its Vault. An API can expose this vault data to the outside world to be consumed by BI or Analytics tools such as Tableau, R or Python.


The figure depicts a sample Tableau report created from the Data extracted from Corda node. As the report is generated on the shared ledger, every participant (with the privilege to view the data) will always have matching numbers about status of claims on common policies.

Here is a graphical representation of the flow of materials from a Supplier to a Customer through a multi-modal logistic channel. The nodes/circles represent the participants; and the weights of the connected arrows represent the volume of goods passing through them (or handled by them). These kind of visualizations can easily be made using tools such as R/Python.

Stakeholders can use these insights to determine critical transport partners and formulate a incentive strategy or evaluate integration options.


The above figure has been drawn using power point for illustration purpose only; similar visualizations can be easily created in R.

As pointed out in the HBR article- noise may be difficult to identify and may be observable much later when very little can be done. For instance a re-insurance company may only come to know about a particular risk (that it didn't want to keep in the first place) only after a large claim comes knocking at the door; or a Bank may come to know about an inconstant credit risk calculation only after the furious prospect calls customer service.


Having a blockchain alone is not enough unless managers can extract insights from it.

A self-auditing network, with codified autonomous business rules may reduce the need for noise audits (alluding to the article again) and improve the overall quality of decisions made by managers; amplifying efficiency in the entire value chain. That makes a case for Reporting and Analytics on Blockchain.


Daniel Kahneman is psychologist who was awarded the Nobel prize in economics in 2002 for his contribution in behavioral economics. 

HBR article referred in this Blog is, "Inconsistent decision making is a huge hidden cost for many companies. Here is how to overcome what we call Noise" by Daniel Kahnemal, Andrew M. Roenfield, Linnea Gandhi and Tom Blaser, published in HBR October 2016 edition.


Continue reading "Analytics on Distributed Ledger (Reducing noise in the Value Chain)" »

September 14, 2018

How Machine Learning and Blockchain Can Improve Supply Chain Management?


What is Blockchain

I am quite sure that you are aware of Bitcoin, but how many of you are familiar with its basis - The Blockchain? What is blockchain? The Blockchain is a math-based process that enables you to make "anything computerized" record by keeping it in a succession or history in an inviolable way - thus guaranteeing the whole history of the transactional data. Blockchain can be public or private; it allows users to send data and its control in a "community oriented"/ distributed way (it wipes out the requirement for an entity that ensures data trust). A September 2015 World Economic Forum report anticipated that, by 2025, 10% of GDP will be accumulated in innovations closely related to Blockchain.

The blockchain first introduced in Satoshi Nakamoto's white paper is a decentralized distributed ledger that contains a temper-proof record of every single past transaction.

The Blockchain is developed particularly to quicken and streamline the procedure of how transactions are recorded. This implies any kind of asset can be transparently transacted utilizing this totally decentralized network.

What is Machine Learning

Machine Learning (ML) is an innovation that has really been around quite a while, with a large number of its modern approaches originated in the 1990s when papers on SVMs and Recurrent Neural Networks were published. However, Machine Learning truly has taken off in the last decade or so with the massive enhancement in both computation power and information storing capabilities making way for "deep learning". In other words, this implies using a whole lot of data and high computation power to address a problem until it is solved. This can be applied to issues in data analysis, autonomous vehicles, image recognition systems and so on.

ML can be thought of as programming that provides systems the ability to learn and enhance from experience or from training data without being explicitly programmed.

An interesting case of how ML works is spam detection where the program continuously enhances its own capacity to recognize garbage emails after some time. It does this using supervised learning which allows a system to learn a model from labelled training data and thus allowing the system to make predictions about the ever changing spam patterns.

How Machine Learning can improve Blockchain?

The use of machine learning procedures to track transactions on a blockchain may enhance the productivity and adequacy of numerous enterprises, especially the Supply Chain Management. Also, it is set to enter that stage where execution moves rapidly.

Now the question is why does the Supply chain require improvement at all? There are three noteworthy issues with our present supply chains that blockchain can settle.

1. Visibility of Data

Our first real issue is with information visibility. You've likely found out about big data and the advantages of storing and analysing the immense amount of data created by a supply chain network. However, at this moment, that information is siloed in private cloud databases. When the information is divided, the advantages of having it shirnk. In any case, blockchain stores information on a solitary, unified information sheet.

By utilizing ML and AI to control the chain, there is a possibility to enhance security. Further, as ML can work with a great deal of information, it makes a chance to construct better models by exploiting the decentralized idea of blockchains (that empowers information sharing).

"This phenomenon will be driven by quality information"

The Blockchain has genuine potential since it will work with quality information. At this moment, data science needs to manage a considerable measure of issues with bad information or bad data.

2. Optimization of Processes

The procedures that make up our present supply chains aren't as proficient as they could be. The solution lies in blockchain and its capacity to utilize smart contracts.

Blockchain innovation empowers a group of participants to keep up a protected, perpetual, and carefully designed ledger of transactions without a central authority. In blockchain, transactions are not recorded in centralized manner. Rather, each participant keeps a duplicate copy of the ledger. The ledger is a chain of blocks, each containing the set of processed transactions. Transactions are communicated and recorded by every member in the blockchain network. At the point when another block is proposed, the members in the network validates that this is the legitimate duplicate of this block as per the smart contracts. Once a block is validated and committed to the network, it is impossible to temper with or remove it from the network. Henceforth, a blockchain can be viewed as a de-centralized temper-proof information store, which can supplant a centralized storage of transactions administer by a regulated authority. Blockchain frameworks, for example, Ethereum additionally allows execution of scripts on top of a blockchain. These alleged smart contracts enable entities to encode business processes on the blockchain in a way that acquires from its temper-proofness.


3. Demand Management

The third issue we have to deal with is demand Management. This relates back to the information trust. At the point when our information is divided, we can't use the genuine intensity of big data and machine learning. Rather, we end up making forecasts and writing programs that calculate the user demands in a reactive way. We're utilizing past information and assumptions to figure out the user demand, and it is not as effective as it can be.

a machine-learning algorithm can help organizers by helping them in forecasting unusual product demand just by burrowing through verifiable information. To determine the correct correlation, the ML algorithm categorizes items by using comparative and statistical mechanisms, helping retailers hit the correct coordination with suppliers to guarantee that giant foam finger gets to its goal on time.

By incorporating ML algorithms with a decision making system, prescriptive data analysis gives owners and managers convenient suggestions of which item or product types to target for the upcoming market, empowering the predictive analysis by removing "what-if" from supply chain market.


"For the supply chain professional, utilizing ML to help with demand planning and forecasting, and exploit openings like the Super Bowl, this is their "I'm going to Disneyland!" minute."

Continue reading "How Machine Learning and Blockchain Can Improve Supply Chain Management?" »

June 28, 2018

Can Blockchain provide your Chatbot the inventory for enhanced query handling?

Bob, an impatient customer who has recently approached your Bank for a loan, logs in to the self-service portal and starts interacting with Qbot (your pet chatbot that queries your Loan processing Blockchain/DLT). 

He wants to know the typical SLA for getting a Loan approval. No problem says Qbot- "it takes about 3 to 5 days on an average".

His colleagues have warned him, that for a loan amount as high as his (he needs the money to buy a posh apartment at the city centre) it will take longer than a week; after all the credit departments will have to check his credit score, determine the valuation of the property through independent appraisers and scrutinise his income and liabilities more extensively. 

"Tell me Qbot, whether the credit department's independent valuation of the identified property matched my Loan amount?", Bob asks.

Qbot, "Apparently the evaluators had submitted their appraisal report yesterday, and the records don't suggest any discrepancy"

Blockhain/DLT provides a consistent version of truth across disparate network participants, involved at different stages of an end-to-end process (in this example the Loan Origination Process).

A Blockchain and AI based Query handling enables timely and accurate information dissemination, ensures data privacy and protects against potential security compliance lapse. It drives customer's acceptance of the self-service channel, thus reducing an Institution's customer handling cost and enhances customer experience, positively impacting the Net Promoter Score (NPS).


May 23, 2018

Blockchain Next: Artificial Intelligence

Artificial Intelligence can be defined as the field of computer science dedicated to solving cognitive problems commonly associated with human intelligence, such as learning and pattern recognition. AI has been a fascinating concept of science fiction for decades but technological advancement has enabled organizations to leverage AI capabilities such as machine learning, deep learning, natural language processing, etc. for a variety of applications.

Technology giants such as Google, Facebook & IBM are developing AI technology to help people in all aspects of their lives from translations to healthcare. Facebook is developing AI Tech to enable people to communicate better while IBM is developing scalable AI models and tools such as natural language processing, speech and image recognition and reasoning.

The technology is set to change our lives and how we relate to technology but it still has some hurdles to overcome before we can realize its full potential.

Data is a key ingredient for AI training and this need has amplified the need for qualitative data. The perennial problem of getting access to quality data for training AI based models has significantly hampered the advancement of AI across multiple industries. The unavailability of quality data is either due to the fact that data is locked up in siloed databases or data owners have no incentive to trade data.

Blockchain's ability to guarantee the accuracy of data makes it useful for a number of AI applications, both for feeding data into AI systems and for recording the results of AI logic. Blockchain opens up the possibility for data owners to share de-personalized data using smart contracts to define the rules of the transaction. The model also opens up valuable data locked up in siloed databases for the advancement of AI/ML algorithms making this a unique win-win proposition for the data producer as well as the data consumer.

Blockchain also enables organization to comply with regulations by creating an audit trail of AI logic for research and prevention of catastrophes due to the autonomous and evolving nature of AI.

With the convergence of the two technologies, AI is able to address many security concerns associated with layers interfacing with blockchain networks. Advanced deep learning based cyber security algorithms enable prevention of data fraud as well as help organization optimize their energy consumption for data mining on the blockchain.

AI and blockchain enables the processing of encrypted data ensuring the secure transfer and processing of data further enabling the creation of a network for monetization of anonymized data.

The convergence of AI and blockchain stands to revolutionize organizations and our day to day lives as the world around us slowly transforms into our favorite science fiction movies filled with autonomous robots, cars and homes enabled by the access to vast amounts of data and security provided by these two technologies.


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