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Artificial Intelligence: A smart investment for financial services firms



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Artificial Intelligence: A smart investment for financial services firms
 

 
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Artificial intelligence (AI) is rapidly gaining momentum as a vital business resource as organizations discover new use cases in their efforts to improve processes, increase efficiency and automate costly, manual tasks. Industries such as financial services are ideal for AI-driven applications and a related technology, machine learning (ML), because they can bolster customer service and leverage data to increase competitiveness.

AI includes software thatís designed to work in ways similar to the human brain, while machine learning encompasses programs that alter themselves based on data thatís fed into the programs in order to train them.

Recent industry research gives a sense of how AI usage is on the rise. Global spend on AI is forecasted to double during the next four years, growing to $110 billion in 2024, according to research firm IDCís Worldwide Artificial Intelligence Spending Guide.

Financial services firms, in particular, are optimistic about the potential of AI. A June 2020 report by The Economist Intelligence Unit (EIU), the research and analysis division of Economist Group, says AI is set to shape the banking and insurance ecosystem, driving a "massive wave of future growth for the financial services industry." Consulting firm Deloitte has also noted AI is significantly changing the traditional operating models of financial institutions, shifting strategic priorities and upending the competitive dynamics of the financial ecosystem.

AI and ML can be of great value in specific areas of financial services such as a*set management because they can process vast amounts of data much more quickly than people. For instance, a*set management involves making important investment decisions for clients, and to do this, firms need to conduct in-depth research and use powerful analytical tools.

AI combined with ML can accelerate the research process while offering reliable investment suggestions. Here are four areas where AI can benefit organizations, including a*set management firms.

Practical applications

Financial services firms can leverage several practical applications of AI and ML to improve processes and achieve competitive advantage. One is evaluating investment opportunities. Clients rely on financial services firms such as brokerages and a*set management providers to deliver informed advice with regard to investments. But evaluating investment opportunities is a task that can consume a considerable amount of time for any investment manager.

The process of going through massive volumes of information and comparing hundreds of data sources with each other is complex and in many cases tedious. By applying AI, managers can complete evaluations much more quickly and with greater accuracy and no bias.

AI-powered tools can deliver the best possible results and even project the success rate/potential opportunity for each of the proposed options. Investment managers can devote more time to opportunities that present the highest probabilities for success. They can avoid the time-consuming, error-prone process of examining data that, in most cases, wonít be looked at twice because of the low potential for returns.

Requests for proposals

Another application is processing requests for proposals (RFP). Because AI can use sets of unstructured and structured data, the act of scanning an RFP enables a system to read it and learn whatever is needed to produce a desired answer. This eliminates the time-consuming data submission process.

An AI application can even tell a user the opportunity a*sociated with a specific RFP. As more RFPs are processed, the store of data grows, along with the systemís intelligence. Finally, it can become capable of recommending adjustments to proposals based on previous performance and data input, which helps a firm achieve greater success in the long run.

Building a better business trip

Trip planning, which for managers and salespeople can be a logistical mess, is another application where AI can help. When on business trips, executives are often time bound and must decide which customers or prospects to meet with from the many options theyíre presented with. Itís human tendency to choose the meetings that would be the most fun or convenient, rather than those who might present the best opportunities for business.

As long as itís people who are making these decisions, this will remain a bias thatís hard to alleviate. By allowing a machine to execute the decision-making process, bias can be eliminated, and every factor can be considered and compared before a selection is made.

The final choice will still be made by the individual taking the trip and attending the meetings. But by letting AI software make trip-planning suggestions, financial services managers, salespeople and other professionals can take more time to prepare quality presentations for the meetings. This ultimately increases the likelihood of a positive outcome.

Managing fraud and risk

Fraud detection is yet another application where AI can play a key role. In fact, itís already benefiting from the use of ML. A machineís ability to track and compare numerous data points while instantly comparing the data to previous transactions on an account is far more efficient than any human performing the same task.

This helps firms provide better service to clients by detecting and reporting any unusual activity on their accounts. An example of this would be when software flags, in real-time, a transaction of much higher value than usual, indicating possible fraud.

Finally, AI can help with risk management. By using AI to quickly and effectively analyze non-traditional data such as images, web site activity, satellite imagery and social posts - while instantly making connections between data points and mapping out relevant trends - firms can more easily oversee risk management.

Implementing AI and ML technology into a*set management will empower clients with more accurate services, faster. The elimination of time-consuming tasks and the allowance of software programs to make well-informed, data-based suggestions, can put an organization in a prime position to increase efficiency, productivity and satisfaction. And the discussed processes are far from the only areas where AI can improve operations.

AI is bound by nothing but our willingness to try it and develop it. By giving the tedious tasks to machines to do, growth-minded companies can expand the fields in which they work every day and achieve their full potential.
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 2 months ago '11        #2
nfo187  topics gone triple plat - Number 1 spot x9
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$srmx
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 1 month ago '15        #3
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UBI is necessary.
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