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Intelligent automation has a massive potential to streamline the complex trading process by precisely capturing the market patterns.
FREMONT, CA: The capital market landscape is on the path to transformation with the emergence of new technologies. Investment firms are aiming to enhance their business and operational strategies. Technologies such as artificial intelligence (AI), with robotic process automation (RPA), is being considered the torchbearer in this regard. In fact, the above-mentioned technologies are often collectively referred to as intelligent automation.
Investment firms are eyeing technology as a means to foster intelligent automation across the various middle and backhand processes. A combination of technologies such as RPA, cloud, and AI is also being considered as the technology-driven solution to complex problems in the capitals market. With the emergence of new and domain-specific use cases in the investment sector, the scope of intelligent automation is gaining ground. Here are the various aspects of the capitals market that can potentially benefit from the incorporation of intelligent solutions.
Trade Allocation
Intelligent automation can play a key role in the various processes associated with trade allocation. Significant improvements in automation can be achieved through intelligent automation in the trade automation processes such as converting unstructured data into structured formats, categorization of the extracted data and sending the outputs from one application to the other for further processing.
Reconciliation
Automated tools can be deployed across the various reconciliation processes such as data comprehensibility, data mapping, and during the development of matching rules based on experience. Historical breaks in a portfolio can be analyzed via supervised or unsupervised automated systems. The automated systems can not only suggest the reasons for a break but also offer solutions to resolve such breaks immediately.
Trade Monitoring
Automated systems can provide crucial advantages to traders. For instance, AI-driven automated systems can be used for improving accuracy in trade prediction. AI-driven systems can also improve their performance by learning from past trading and error resolution experiences. Thus, the automated systems can assist in spotting trading errors and in the continuous enhancement of a firm’s capabilities, to optimize the use of available capital.
The massive potential of intelligent automation in trading cannot be denied. It is essential for investment firms to adopt technologies that propel automation and address the existing trading challenges.
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