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.
