Why is Infrastructure testing important for decentralized applications built on any Blockchain or DLT

According to a recent forecast by Gartner, “by 2025, the business value added by blockchain will grow to slightly more than $176 billion, then surge to exceed $3.1 trillion by 2030.” Right from the voting process to the transfer of data for mission-critical projects, blockchain-based technology would be an integral part of the social, economic, and political setup the world over. 

There are many exciting components/features that make it possible for blockchain platforms to provide a secure decentralized architecture for activities ranging from processing transactions to storing data that is immutable. We have briefly discussed these in our earlier blog. The blog identified how various services and components make infrastructure testing a matter of utmost significance/consequence- while at the same time testing the developer’s or application team’s core competence.

Considering its immense impact in the days to come in all aspects of human life, it has become essential that clients investing in blockchain ensure that the nature of transactions is inviolable. 

To ensure this inviolability, the infrastructure of the blockchain must work seamlessly. Hence the need for infrastructure-testing of blockchain to verify if all the constituent elements are operating as desired. 

What comprises infrastructure testing for Blockchain/Distributed ledger platform 

In simple terms, infrastructure-testing of blockchain networks translates into verifying whether the end-to-end blockchain core network and its constituent elements are operating as desired. It is critical as it determines the reliability of a product, which depends entirely on nodes spread across the globe.

When it comes to decentralized applications built on the blockchain or distributed ledger platforms due to the nature of operations where each constituent element is highly reliant or linked with the other, any shortcoming or failure could jeopardize operations. Hence, to ensure continuity, reliability, and stability in services, infrastructure testing should be carried out with high focus.      

Defining the constituent elements of a Blockchain
  • All distributed ledger platforms, including blockchain, have a dedicated service responsible for establishing communication between the nodes utilizing peer-to-peer networking or any other networking algorithm. 
  • There is also a component or service that makes the network of such applications fault-tolerant using consensus algorithms. 
  • Another critical aspect of blockchain platforms is making consensus on the state and transactional data to process, followed by persisting of the manipulated data. 
  • When it comes to private networks, also known as consortium networks, there are many ways to achieve permission for each node to provide a secure and isolated medium among the participants. 

For confirming production usage for application builds over these platforms, infrastructure testing has similar importance as any other supported functionality. Without verifying functionality, none of the applications can be deployed to production. Similarly, decentralized applications built over various platforms can be deployed to production only after the reliability of infrastructure has been verified with all nodes’ probable numbers. 

What makes the entire exercise demanding are the following factors: 

  • Peer- to- Peer networking(P2P) 
  • Consensus algorithms
  • Role-based nodes along with permission for each node (meant only for private networks)
  • State and transactional data consistency under high loads along with resilience test of nodes

Another vital characteristic to be considered is the number of nodes itself.  Considering that such applications’ functionalities are dependent on the number of nodes, this is a key requirement. The number of nodes can vary depending upon:

  • Which service or component is to be tested 
  • How all the factors mentioned above impact the service or component
Importance of testing various components of Blockchain Infrastructure
Reliability testing

Reliability of infrastructure by far is the most challenging phase for any blockchain developer or application team. Here, confirming whether an application can run on targeted infrastructure or not is explored. Defining application reliability for multiple machines (a.k.a. nodes or servers or participants etc.) increases complexity exponentially due to the permutation and combinations of failures. 

Hence, wherever multiple machines are involved, it is the natural course of action for developers and application teams to measure application reliability on the infrastructure on which such applications will run. All the factors enlisted earlier attest that infrastructure testing is of prime consequence for decentralized applications built on all available platforms. 

Peer-to-Peer networking

If there is any flaw in peer-to-peer networking, then nodes will not communicate with each other. If nodes cannot establish connections with each other, then nodes will not be able to process the transaction with the same state. If nodes are not in the same state, then there will not be any new data manipulated and created to persist. In the case of blockchain, there will not be any new blocks. For the distributed ledger, there will not be any new data appended to the ledger. This may lead to chain forking or a messy state of data across the nodes that will eventually result in the network reaching a dead-end or getting stuck. 

Improper peer-to-peer network implementation can also lead to data exposure to unintended nodes that do not have permission to see the data. To overcome this risk of unintentional data exposure, proper testing must be performed. That will ensure that the expected number of participants and expected numbers of new participants can participate within the network as appropriate communication is established between nodes based on each node’s role and permissions. 

Consensus algorithms: 

Consensus algorithms have two critical functions: 

  • Drive consensus by ensuring that a majority of nodes are processing new data with the same state
  • Provide fault tolerance for network

Consensus algorithms must be verified with all possible types of nodes and all probable permissions that can be defined for each node. To verify the consensus algorithms, multiple network topologies are needed. Improper verification will result in the network getting stuck. It would also result in sharing of data with nodes that were not supposed to get the data. 

Any flaw in consensus will result in a “stuck network” and cause the forking of data. Worse, data can be manipulated with fraudulent nodes. Depending upon which consensus algorithm is used, the network topology can be created and verified with all expected features claimed to be working. 

Role-based nodes, along with their permission 

Each platform supports different roles for each node to ensure that nodes get only the intended information based on the defined permissions. Depending upon the different kinds of roles and their respective permissions, various network topologies are created to perform all required verifications. In case there is any missing verification, sensitive data is exposed to unintended nodes. The way data is shared between nodes is governed by consensus algorithms based on defined permission. 

Any flaw in the permission control mechanism can lead to sensitive data leakage. Data leakage is catastrophic, more so for private networks. The importance of accuracy cannot be emphasized more in this case, and it can only be achieved by ensuring a proper testing mechanism is being utilized.

State and transactional data consistency 

As there can be any number of nodes in real-time, it is highly critical to verify that each node has the same state and transactional data. All complicated transactions must be performed with an adequately defined load to ensure that all nodes have the same state and transactional data. 

Resiliency-based verification must be performed so that all nodes can get to the same state and transactional data, even when a fault is intentionally introduced to randomly selected nodes with a running network. 

Conclusion

To conclude, infrastructure testing should not be substituted with any traditional functional testing process. Furthermore, as this is a niche area, infrastructure testing must be entrusted to a partner with industry-wide experience and capable resources having a sound understanding of all the factors underlined above. A real-time experience in establishing testing processes for such platforms is a highly desired prerequisite.  Without infrastructure testing, it is perilous to launch a product in the market. 

Magic FinServ has delivered multiple frameworks designed for all the above factors. With an in-depth knowledge of multiple blockchain platforms, we are in an enviable position to provide exactly what the client needs while ensuring the highest level of accuracy and running all frameworks following industry standards and timelines. As each customer has their own specific way of developing such platforms and choosing different algorithms for each factor, choosing an experienced team is undoubtedly the best option to establish an infrastructure testing process and automate end-to-end infrastructure testing.

To explore infrastructure testing for your Blockchain/DLT applications, write to us at mail@magicfinserv.com

Revolutionizing the Investment Research Process with AI

Introduction

Investment research and analysis is beginning to look very different from what it did five years ago. While five years ago, the data deluge could have confounded asset management leaders, they now have a choice on how things could be done differently, thanks to AI and advanced analytics. Advanced analytics helps create value by eliminating biased decisions, enabling automatic processing of big data, and using alternative data sources to generate alpha. 

With multiple sources of data and emerging AI applications heralding a paradigm shift in the industry, portfolio managers and analysts who earlier used to manually sift through large volumes of unstructured data for investment research can now leverage the power of AI tools such as natural language processing and abstraction to simplify their task. Gathering insights from press releases, filing reports, financial statements, pitches and presentations, CSR disclosures, etc., is a herculean effort and consumes a significant amount of time. However, with AI-powered data extraction tools such as Magic DeepSight™, quick processing of large-scale data is possible and practical.

A tool like Magic DeepSight™  extracts relevant insights from existing data in a fraction of the time and capital compared to manual processing. However, the real value it delivers is by supplementing human intelligence with powerful insights, allowing analysts to direct their efforts towards high-value engagements.

Processing Unstructured Data Is Tough

There are multiple sources of information that front office analysts process daily, which are critical to developing an informed investment recommendation. Drawing insights from these sources of structured and unstructured data are challenging and complex. These include 10-K reports, the reasonably new ESG reports, investor reports, and various other company documents such as internal presentations and several PDFs. SEC EDGAR database makes it easy to access some of this data, but extracting this data from SEC EDGAR and identifying and then compiling relevant insights is still a tedious task. Unearthing insights from other unstructured documents also takes stupendous manual efforts due to the lack of any automation. 

10-K Analysis using AI

More detailed than a company’s annual report, the 10-K is a veritable powerhouse of information. Therefore, accurate analysis of a 10-K report would lead to a sounder understanding of the company. There are five clear-cut sections of a 10-K report – business, risk factors, selected financial data, management discussion and analysis (MD&A), financial statements, and supplementary data, all of which are packed with value for analysts investors alike. Due to the breadth and scope of this information, handling it is inevitably time-consuming. However, two sections that usually require more attention than the others to analyze due to the complexity and existence of possible hidden anomalies are the “Risk Factors” and the “MD&A”. The “Risk Factors” section outlines all current and potential risks posed to the company, usually in the order of importance. In contrast, the   “Management’s Discussion and Analysis Of Financial Condition And Results Of Operations” (MD&A) section is the company management’s perspective of the previous fiscal and future business plans’ performance.

As front-office analysts sift through multiple 10-K reports and other documents in a day, inconsistencies in analysis can inadvertently creep in. 

They can miss important information, especially in the MD&A and Risk Factors sections, as they have to analyze many areas to study and more reports in the queue. Even after extracting key insights, it takes time to compare the metrics in the disclosures to a company’s previous filings and against industry benchmarks. 

Second, there is the risk of human bias and error, where relevant information may be overlooked.  Invariably, even the best fund managers would succumb to the emotional and cognitive biases inherent in all of us, whether confirmation bias, bandwagon effect, loss aversion, or various other biases that behavioral psychologists have formally defined. Failure to consider these issues will lead to suboptimal decisions on asset-allocation and often does. 

Using AI to analyze the textual information in the disclosures made within 10-Ks can considerably cut through this lengthy process. Data extraction tools can parse through these chunks of texts to retrieve relevant insights. And a tool or platform custom-built for your enterprise and trained in the scope of your domain can deliver this information to your business applications directly. More documents can be processed in a shorter time frame, and armed with new insights, analysts can use their time to take a more in-depth learning’s untapped potential look into the company in question. Implementing an automated AI-Based system omits the human errors,  allowing investment strategies to be chosen that are significantly more objective, in both their formulation and execution. 

Analysing ESG Reports

Most public and some private companies today are rated on their environmental, social and governance (ESG) performance. Companies usually communicate their key ESG initiatives yearly on their websites as a PDF document. Stakeholders are studying ESG reports to assess a company’s ESG conduct. Investment decisions and brand perception can hinge on these ratings, and hence care has to be taken to process information carefully. In general, higher ESG ratings are positively correlated with valuation and profitability while negatively correlated with volatility. An increased preference for socially responsible investments is most prevalent in Gen Z and Millennial demographics. As they are set to make-up 72% of the global workforce by 2029, they are also exhibiting greater concern about organizations’ and employers’ stance on environmental and social issues. This is bringing under scrutiny a company’s value creation with respect to ethical obligations that impact the society it operates in.

Although, ESG reports are significant when it comes to a company’s evaluation by asset managers, investors, and analysts, as these reports and ratings are made available by third-party providers there is little to no uniformity in ESG reports unlike SEC filings. Providers tend to have their own methodology to determine the ratings. The format of an ESG report varies from provider to provider, making the process of interpreting and analyzing these reports complicated. For example, Bloomberg, a leading ESG data provider, covers 120 ESG indicators– from carbon emissions and climate change effects to executive compensation and rights of shareholders. Analysts spend research hours reading reports and managing complex analysis rubrics to evaluate these metrics, before making informed investment decisions.

However AI can make the entire process of extracting relevant insights easy. AI-powered data cleansing and Natural Language Processing (NLP) tools can extract concise information, such as key ESG initiatives from PDF documents and greatly reduce the text to learn from. NLP can also help consolidate reports into well defined bits of information which can then be plugged into analytical models including market risk assessments, as well as other information fields. 

How Technology Aids The Process

A data extraction tool like Magic DeepSight™ can quickly process large-scale data, and also parse through unstructured content and alternate data sources like web search trends, social media data, and website traffic. Magic DeepSight™ deploys cognitive technologies like NLP, NLG, and machine learning for this. Another advantage is its ability to plug the extracted information into relevant business applications, without  human intervention. 

About NLP and NLG

Natural Language Processing (NLP) understands and contextualises unstructured text into structured data. And Natural Language Generation (NLG) analyses this structured data and transforms it into legible and accessible text. Both processes are powered by machine learning and allow computers to generate text reports in natural human language. The result is comprehensive, machine-generated with insights that were previously invisible. But how reliable are they?

The machine learning approach that includes deep learning, builds intelligence from a vast number of corrective iterations. It is based on a self-correcting algorithm which is a continuous learning loop that gets more relevant and accurate the more it is implemented. NLP and AI-driven tools, when trained in the language of a specific business ecosystem, like asset management, can deliver valuable insights for every stakeholder across multiple software environments, and in appropriate fields.

Benefits of Using Magic DeepSight™ for Investment Research

  1. Reduced personnel effort

Magic DeepSight™ extracts, processes, and delivers relevant data directly into your business applications, saving analysts’ time and enterprises’ capital.

  1. Better decision-making

By freeing up upto 70% of the time invested in data extraction, tagging, and management, Magic DeepSight™ recasts the analysis process. It also supplements decision-making processes with ready insights. 

  1. Improved data-accuracy

Magic DeepSight™ validates the data at source. In doing so, it prevents errors and inefficiencies from  creeping downstream to other systems. 

  1. More revenue opportunities

With reduced manual workload and emergence of new insights, teams can focus on revenue generation and use the knowledge generated to build efficient and strategic frameworks. 

In Conclusion

Application of AI to the assiduous task of investment research can help analysts and portfolio managers assess metrics quickly, save time, energy and money and make better-informed decisions in due course. The time consumed by manual investment research, especially 10-K analysis, is a legacy problem for financial institutions. Coupled with emerging alternative data sources, such as ESG reports, investment research is more complicated today. After completing research, analysts are left with only a small percentage of their time for actual analysis and decision-making. 

A tool like Magic DeepSight™ facilitates the research process, improves predictions, investment decision-making, and creativity. It could effectively save about 46 hours of effort and speed up data extraction, tagging, and management by 70%. In doing so, it brings unique business value and supports better-informed investment decisions. However, despite AI’s transformative potential, relatively few investment professionals are currently using AI/big data techniques in their investment processes. While portfolio managers continue to rely on Excel and other necessary market data tools, the ability to harness AI’s untapped potential might just be the biggest differentiator for enterprises in the coming decade. 

To explore Magic DeepSight™ for your organization, write to us mail@magicfinserv.com or Request a Demo