Case Study Of Data Mining Application In Banking Industry

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HSBC is an extreme example of the costliness of not being compliant with AML laws; the company was fined $1.9 billion for its failure to control money laundering.

As part of its effort to prevent this from happening again, HSBC implemented Ayasdi’s solutions.

AI-based Fin Tech solutions will both save financial institutions billions in cost and create billions in additional revenues, potentially creating more than a trillion in additional profits in the financial services industry.

A study done by Accenture showed that the implementation of AI in the financial sector could lead to a 31% increase in profitability rates by 2035.[1] Moreover, AI will allow to customize financial services delivered to clients, leading to an enhanced customer experience.

Another key use case for AI in financial institutions is big data mining and process improvement.

Banks are flooded with consumer data, legal documents, etc.

A recent survey reported that[4]: A common use case for AI in the front office is customer service bots.

However, more advanced technologies have lead to a wider variety of solutions such as automated advisory assistants that take into account the clients’ interests, financial portfolio, financial history, and desire risk to offer them individualized investment strategies.

Ayasdi is a Fin Tech venture that provides anti-fraud solutions to banks.

They found that many of the world’s best banks had false positive rates of up to 95% or higher.


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