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Implementing AI in Trading and Lending
AI can easily identify something on an existing image/footage by code coating colors, then connect them and develop an image that can be compared to the database, determining what it is looking at
By
Apac CIOOutlook | Monday, November 02, 2020
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AI can easily identify something on an existing image/footage by code coating colors, then connect them and develop an image that can be compared to the database, determining what it is looking at.
FREMONT, CA: Artificial intelligence (AI) has become an essential technology in the financial sector, automating and optimizing processes, and customer engagement. It is necessary to understand the financial institution’s target before implementing AI into their operations.
Trading
Machine learning is constant in trading, while AI will have to learn different variables because new strategies and tools are always emerging in the market. It needs to reason and find a link between a set of events and price change in an asset, whether it is stocks or assets. Also, AI needs to find the right connection between these events so that it does not repeat in the future, which will result in manually removing that particular algorithm and repeating the entire process.
The learning stage for AI in the trading department is delicate because of the variables. AI can easily identify something on an existing image/footage by code coating colors, then connect them and develop an image that can be compared to the database, determining what it is looking at.
Lending
In lending, a lender and borrower register on the platform where they put their requirements to give out or receive a loan. The AI tried to find the standard match for both the lender and the borrower in a matter of seconds. It is also developed to bring results down to single digits and, at times, one, to find the ideal candidate. The developers let the AI run its course with matching lenders and borrowers and confirm or deny the match’s accuracy. The AI becomes more accurate with more denial it receives, thus reaching a stage where it finds 100 percent correct matched in just a few weeks of testing.
