Machine Learning and its Possible Contributions to Secure Demat Transactions

Machine learning is defined as the stream of artificial intelligence (AI), by which machines ‘learn’ or grasp the execution of certain tasks without the need for prior programming. Machine learning is an emerging and fast developing technology, with scientists all over the world embracing it for its many applications. It can be well utilized in industrial applications, chemical, and medical industries, and even contribute to the current fintech revolution.

The technology of machine learning is derived from specific rules called ‘algorithms’, which aid computers to predict and execute an action on data sets. What makes machine learning relevant in the field of finance, is its application in forecasting information based on past data. It combines the subject knowledge of mathematics, optimization models, predictive analytics, statistics, and other theories. Machine learning can contribute to a great extent to the current dematerialised trading environment. For, this the computers need to understand what is demat account, what are its features, interface, and functions.

With the aid of AI and machine learning, computer systems can improve the speed of transactions and also predict strategies for future trades. Traders can also benefit from these advanced technologies for higher returns on investments. Security of the trades would also be better as machine learning can predict a breach of security as well. Traders can contact broking agencies for more information on AI-based tools and how to utilize them for earning better returns than the market average. Machine learning has been used in developed countries for various applications. However, in developing economies, it is yet to get commercialized. Here are some of the possible contributions of machine learning to secure demat transactions:

1. Encryption of Data:

Machine learning can be adapted to understand data encryption and apply it to demat account transactions. Data entered by an investor regarding buying and selling shares is confidential, and security tools such as encryption and digital signatures are necessary to keep this data private.

2. Access Security:

Building secure systems include secure access. The demat account should only be accessible by unique demat ID code and authentic passwords. Machine learning can aid in strengthening access security by additional tools and preventing unsafe or untrusted access at any point in time.

3. Random Audits:

Computer systems can undertake random checks and audits on a demat account data with machine learning technologies. This audit can identify security issues, and even operational issues, note them in internal memory, and also observe trends. Algorithms would enable computers to perform random checks, for better security systems.

4. Secure Data Transfer:

The demat account works in tandem with the investor’s trading account and bank account to effect a trading transaction. Secure transfer of data, funds and share to and from different accounts can be improved upon by machine learning and analytics. Transfer of data over the open networks can pose a risk of loss of privacy.

5. Clustering:

Cluster analysis is a subset of machine learning where similar scenarios are clustered together to form specific observations regarding that cluster. The demat account has various aspects which machine learning can cluster upon- security, operations, data transfer, fund transfer, the privacy of data etc.

6. Forecasting data breach:

Machine learning has the unique feature of forecasting after understanding past trends. The technology can be well utilized to forecast possible data breaches and identify trends where past breaches or leaks in data had occurred. Once the forecast is made, the computer systems can be strengthened for security by deploying additional software for protection.

7. Simulation of cyber attacks:

Cyber attacks such as hacker attacks, online phishing, and compromised firewalls can affect traders using the demat account. Machine learning can be deployed to analyze trends and simulate these attacks and find corrective solutions to prevent loss of security for traders. Sensitive information like bank information, cards etc. can be protected through this.

8. Detecting frauds:

Similar to simulating cyber attacks, machine learning can be used to detect frauds and data breaches in real time. Traders can be protected from getting trapped in fraudulent transactions, with programs developed by machine learning technologies and the underlying algorithms.

Once the machine learning systems understand what is demat account, they can devise softwares to improve its operations and strengthen security systems. Cyber crimes like fraud, identity theft, phishing, scams, and hacking can be prevented with analytics and machine learning. The systems can be designed to analyze these in real time, and find solutions to simulate and study the effects of the same.

Once the simulations are done, the machine learning softwares can compute methods of forecasting these attacks and preventing the effects of the same on sensitive information. Maintaining the privacy of data is one of the top priorities in the financial sector. Machine learning can also be utilised to develop advanced security features like firewalls and encryption softwares. It is a tool which will further develop in the near future.


A Simple Guide to Machine Learning and Artificial Intelligence

Technology, despite the revolutionary progress that it has enabled over the millennia, has never been short of doubting Thomas’s. Computers, for example, have become so closely integrated into our lives that we use them in our homes and we carry them around in our pockets. That has never stopped people from fearing computers and predicting doomsday scenarios. The anxiety rose to fever pitch as Artificial Intelligence (AI) and Machine learning (ML) became the talk of the town.

These new age technologies are poised to impact us in ways we can’t imagine. The topic fascinates and worries us in equal measure. That’s why people, even the least technology-savvy among us, need to understand what AI and ML are and how these technologies are poised to change our lives dramatically.

We requested IT experts from FirewallTechnical to break down the technical complexities, shed the jargon, and present a simplified guide to AI and ML so that a layperson could gain a better understanding of what these technologies are capable of doing. We share these valuable insights:

What Is Machine Learning and Artificial Intelligence?

It could be the weather, a population group, an industrial process, or something as mundane as an economic impact theory or cultural event. We study these events and gather an enormous amount of data. This data is minutely analyzed. When we analyze data, we focus on patterns of behavior that keep recurring. Then we apply advanced principles of computer science to analyze the behavior and predict what may happen in the future. The end result is a “statistical model” capable of predictive analysis. Artificial Intelligence and Machine learning are the technological breakthroughs that create statistical models and make predictive analysis possible.

Artificial Intelligence and Machine Learning: How Are They Different?

Artificial Intelligence

The word artificial means it was created by humans. Intelligence implies that the computer is programmed to ‘think’ on its own almost like (but not superior to) a human. AI is a complex cluster of algorithms that simulate human-like behavior in a computer. It’s a way of programming a computer to think on its own (mimic human intelligence) without human guidance at every step.

Machine Learning

Machine learning builds on the foundation of AI to develop programs that teach computers to gather and analyze data on their own and predict future behavior. In such a scenario, the machine is not merely programmed to do a defined task. The machine is actively ‘learning’ and changing its responses with experience. You can ask ten people for solutions to a problem and probably receive ten different answers. ML will analyze thousands of variables and predict the best outcome.

If AI infuses a machine with human-like intelligence, ML ensures that the machine thinks on its own and learns from experience to predict outcomes.

If Artificial Intelligence was a tree, ML applications would be the low hanging fruit. Both tree and fruit are mutually dependent and one can’t exist without the other. AI is the foundational technology, ML is its application. AI is the architect and strategic planner, ML is the builder and project manager.

Three Foundational Tasks That ML Is Geared to Perform

Having acquired a bird’s eye view of what these new technologies are, we move on to the three basic tasks that AI and ML are programmed to do in real life situations – Classification, Regression, and Ranking.

ML Classification:

Take facial recognition, for example. The observed image is matched with an existing database and quickly identified. Facebook is using this to tag pics of people known to you.

ML Regression:

This makes it possible to correlate numerical values with factors that tend to vary. For example, in real estate, ML can help you predict the value of your home after considering related factors such as market value of residential property, the age of the home, and depreciation.

ML Ranking:

This is a way of predicting an outcome within a certain set of parameters. For example, the likelihood of the New York Yankees topping the charts in Major League Baseball. Another example is Google’s search engine that ranks the most relevant result to a searcher’s query.

How ML Uses Statistical Models to Analyze Data and Predict Outcomes

We’ve already seen that statistical data gathering creates a machine learning model of how events pan out in the world. When data analysis gives you patterns of behavior, the same can be studied and what the future holds in store can be predicted. ML does this using datasets and functions.

  • A dataset is a graph with rows and columns showing how variables relate to each other. For example, a row may show age-groups (young, middle-aged, senior, super-senior) within a targeted population, and the column indicates their preference for an herbal product.
  • A function is a sequence of code that instructs a machine to perform specified tasks. The same functions can be used to manipulate data sets to yield a particular result.

In the population study, you could use functions (codes) to teach the machine to recognize patterns such as more middle-aged folks opting for herbal products as opposed to younger people. The machine “learns” the pattern and recognizes it when it is repeated elsewhere.

Like Human Learning, Machine Learning Follows Different Pathways

The statistical model lies at the heart of machine learning. Using these models, the ML application follows a steep learning curve imbibing “lessons” on the way to predicting behavior. Humans are said to follow at least seven learning styles – video, audio, language, and physical among others. The same is mimicked by AI and ML powered systems:

● Supervised Learning:

This is the ML way of linking a programming input to an output. For example, you can teach a program to learn the difference between good food and bad food. The computer can’t taste the food, so you explain through examples that certain foods are good, some are bad to enable it to recognize the difference. The Microsoft Virtual assistant, Cortana that trains a computer device to recognize speech commands is an example of supervised learning.

● Unsupervised Learning:

If it is cumbersome to modulate the machine ’s decision-making ability with endless examples, unsupervised learning takes over. Without referring to any known classification or category of data, the machine studies patterns and comes to its own conclusion. This is very useful when large data sets are being analyzed, and it becomes manually impossible to program responses covering every possible variation. NASA, for example, uses unsupervised learning to discover new stars that do not figure in an existing database.

How AI/ML principles impact us in daily life

Google’s search engine switched to AI and ML, creating a new algorithm called RankBrain. Previously, Google would deliver web results relevant to the keywords used by customers in web searches. Powered by AI and ML, Google’s new search engine studies the context in which the keyword has been used compares it with the customer’s past searches, studies the customer’s choices (rejection/acceptance), and delivers a result that satisfies the searcher’s intent.

The major difference is that the search engine adapts search results in the way the customer responds and offers improved results as the search intensifies. Google search ‘learns’ through experience to become more accurate at predicting the customer’s intent to give a better and personalized web experience.

Agreed, AI and ML Are a Big Deal, but How Do They Affect Me?

Social media and e-commerce giants need not engage you in long intimate conversations to get to know you better. Your web browsing habits, personal preferences, and product choices are already known to them. All they do is hand over the data to AI and ML to analyze. Patterns of behavior emerge that prompt company to reorient and personalize marketing strategies suiting individual preferences.

Machine learning is about to change the job landscape. The World Economic Forum predicts Data Analytics, AI and Machine learning specialists, and Operations managers as the top three in-demand job vacancies by 2020. Data entry, accounting and bookkeeping, and administrative roles are the top three jobs destined to become obsolete. Financial services will be worst affected because no human capability can match AI and Machine learning capacity to crunch big data and predict more accurate results.


Let’s put it in a nutshell. If you’re a developer, AI and ML unleash your potential to go where no man has gone before. If you’re a layperson, understanding and integrating the new age technologies into your lifestyle will mean the difference between survival and extinction. Agreed, AI can’t offer solutions for everything, but it is better to be aware of how AI inspired changes are reshaping our universe.

We should respond positively when a new technology promises to change the globe as we know it. Forget doomsday. Already, AI and IoT (Internet of Things) is merging with blockchain to save the environment. The same technology mergers are revolutionizing supply chain management in industry. The benefits of AI and ML are so awesome we ignore this revolution only to our disadvantage.