2 min read

Machine Learning Usecases 02- Finance

In finance and banking, machine learning is used for credit scoring, risk analysis, client analysis, trading exchange forecasting, and fraud detection.
Machine Learning Usecases 02- Finance

Machine learning in finance is rapidly developing – there are already dozens of options for its use in the financial sector. So why does the industry use AI for finance?

Credit Solvency Assessment

Artificial Intelligence helps banks more confidently issue credit to those who pass system checks. For this, programs and algorithms analyze all available information about a potential borrower, study their credit history, changes in their level of wages, and on this basis determines the reliability of the client and the security of the loan. Moreover, Chinese banks have already gone further and decided not to limit themselves to analyzing the data exclusively.

They began to introduce facial micro-expressions recognition technology. This allows them to find out if customers are lying about their financial situation when they come to take out loans. To do this, they developed AI systems that, with the help of smartphone cameras, detect minimal changes in facial expressions that are invisible to the naked eye. Thus, banks identify potential fraudsters, and they have already reduced their losses from unpaid loans by 60%.

Decision-Making

This is a global task that is successfully solved through the introduction of AI and ML in Financial Services. When an algorithm can analyze all of the available structured and unstructured data (both internal from the company’s business processes and external such as customer requests and their actions on social media), a financial institution can discover both useful and potentially dangerous trends. It helps assess risk levels and allow people to make the most informed decisions.

Fraud Protection

Banks and payment systems have already been developing models to identify and block most fraudulent transactions. These models are built on the client’s transaction history as well as the client’s behavior on the Internet. Systems based on Artificial Intelligence that detect online frauds have been developed from Big Data technologies.

Fraudulent social engineering will also be reduced by Artificial Intelligence. For example, when an impostor pretending to be a bank employee fakes data, his activity will be neutralized. Such systems will make financial deception unprofitable for criminals, and most felonious schemes will “die.”

Probyto AI allow organizations to build and manage such usecases and track the benefits of AI adoption in their businesses. Currently, FREE 60 minutes AI Consultation is being provided to the registered users in the Probyto AI Demo platform. If you haven't registered yet, register now to Probyto AI Demo.

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Source: https://spd.group/machine-learning/ml-in-finance/

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