The research conducted by Harish Kumar suggests the automation that the artificial intelligence of safe transactions
In recent years, digital financial transactions have become much more common and faster. While this is a development that affects the need, the scene of the threat surrounding these transactions has also grown increasingly complicated. From theft of identity and defrauding the payment of artificial financial crimes and data violations, the guarantee of transaction security is now a top priority all over the world. Unfortunately, these emerging threats often remain uncovered while using the rules -based traditional systems.
Harish Kumar Sardam, a famous expert in safe -paying treatment, assessing credit risks, preventing identity theft, and marketing automation, suggested a frame driven by artificial intelligence that re -visiting transactions security through the search paper titled “Automation Automation that depends on AI in integrated payment solutions: converting financial transactions with notes highlighted by the nervous network.” Take advantage of nerve networks, childish artificial intelligence, and the smart heavenly mark, his study highlights the role of automation in the pre -emptive detection of fraud, protecting users through the ecosystems of payment, and ensuring compliance.
The safety of transactions in the digital age
Financial transactions occur at an unprecedented speed and expand the scope of the current digital economy, and it extends through e -commerce platforms, traditional banks, mobile phone applications and start -up companies in Fintech. Complex security challenges are presented by the spread of digital payment facades. These transactions include crossing sensitive financial statements. The exploitation of minor weaknesses, even data violations, tremendous financial losses, and the loss of consumer confidence.
In his research, Sardam argues that the time has come to get rid of old systems and algorithms based on fixed rules and incubation
By including artificial intelligence models that are able to constantly analyze compliance and risk indicators, institutions can ensure actual time with complex and sophisticated organizational landscape while reducing the burden of manual reports and scrutiny.
The importance of artificial intelligence and nerve networks
A strong mix of nerve network structure
The pseudo -smart wolf framework is another important innovation presented by SRIRAM in its paper. Using non -named or almost specific data, it can train artificial intelligence models subject to supervision. Even in the categories of complex or mysterious transactions, these models can enhance the accuracy of the classification by setting probability stickers for unknown data points and improving them through repetitive learning. This possibility can be very useful to discover non -typical behavior that indicates risks but does not correspond to the known fraud patterns.
SRIM uses deep nerve networks that can capture multi -dimensional relationships between data points, which are later used to generate alerts or actual time approvals. To simulate high -risk scenarios and evaluate the elasticity of the system against artificial fraud, he also merged the gynecological rivalry networks (GANS). This simulation is more important to enhance the ability of artificial intelligence to perform environments in the real world.
Discover fraud in real time
The traditional idea of discovering fraud around the bases -based engines, manual reviews, and black menus. Although these methods are somewhat effective, they are often slow, interactive and unable to deal with the complexity of modern financial behavior. SRIram search for an automatic, smart and expected model supported by machine learning and data analysis in actual time.
SRIram’s fraudulent detection framework uses a hybrid system that combines nerve networks, detect anomalies in actual time, and mysterious logic. By continuously monitoring transactions flows, this system determines known fraud patterns as well as anomalously emerging cases that are often missed by traditional systems.
One of the most important aspects of this system is its ability to analyze contemporary analysis. Instead of conducting an isolated assessment of transactions, it analyzes behavior groups through spending categories, time areas, devices and historical trends. This enables the system to distinguish between actual fraud and legal, but unusual.
The paper also discusses how models can be trained in advance on artificial attack vehicles by simulating fraudulent transactions using Gans. By learning to identify behaviors such as unauthorized activity across borders, mobility at the site, dividing transactions, and concealing identity, models become very effective in protecting institutions as well as individual users.
Final ideas
Harish Kumar SRIRAM research provides a future vision for smart and secure intelligence financial transactions. By deep focus on preventing fraud in actual time, automation that supports nerve network, and moral artificial intelligence practices, this initiative has the ability to set a new standard for innovation in payment technology.
“AI Al -Tulaidi provides capabilities to simulate, predict and improve transactions, while maintaining safety and compliance,” he says. “Our goal is to build ecosystems for self -learning and flexibility in fraud, and capable of adapting in the actual time to change financial behavior.”
