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10 ways to use Machine Learning and AI in Finance

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How much do artificial intelligence, machine learning, and finance have in common? It turns out that a lot more than most of us might think. Like most other industries in the global economy, the financial services sector is already using innovative solutions. Are you looking for some examples? We’ve prepared a couple of them for you.

In business finance, artificial intelligence (AI) and machine learning (ML) can represent significant time savings in processing heavy tasks and analyzing large volumes of documents. In personal finance, on the other hand, the emergence of chatbots promises better customer service while reducing costs for organizations. For this reason, banks and financial organizations are deploying various types of technology to facilitate the ordinary operations of their customers. As a result, ML and AI solutions can benefit organizations and individuals using their services.

ML and AI in the finance sector

Let’s look at ten interesting ways to use ML and AI solutions in finance.

1. Preventing financial crimes

ML in finance helps detect and reduce the occurrence of financial crimes, such as online fraud and money laundering. Moreover, ML clustering and classification algorithms can help reduce the problem of false alarms.

2. Solvency

Process automation is a crucial use case for AI and ML, and assessing customer solvency is an obvious use of ML to automate processes. Well-implemented credit algorithms can provide a massive advantage to financial institutions while improving the customer experience.

3. Customer support

Chatbots and robo-advisors are the cornerstones of many innovative customer service solutions. They have made investments and financial decision-making more accessible to the average individual customer.

4. Customer service

Chatbot and robo-advisor solutions have another advantage. They are available 24/7. Customers don’t have to wait for a consultant to call or be available for a chat. That improves the entire customer service experience.

5. Cyber-security

In the financial industry, cyber-security protocols are essential. For example, ML and AI algorithms can detect malicious files or monitor an organization’s network traffic for anomalies like repeated attempts to access unauthorized applications. Thanks to that, the tedious task of constant monitoring is delegated to the computer.

6. Proofreading contracts and translations

The use of advanced algorithms to verify financial records is another example of the practical use of ML and AI systems.

7. Handling complaints and requests

Every day in finance, many instances of requests and complaints flowing in happen. Intelligent tools can take the demand of a human being and process it while identifying the complainant’s state of mind to best guide them and resolve the issue quickly to satisfy the customer.

8. Marketing

Finance, like other sectors of the economy, is implementing marketing activities. More and more ML and AI algorithms can retrieve financial information, corporate communications, legal news, and social media posts in seconds to decide the best marketing actions based on which organizations can plan promotional activities.

9. Decision-making

AI can analyze vast amounts of data, often in milliseconds. For example, they can explore the evolution of stock prices in real-time. Thus, the algorithms can “decide” to buy and sell securities while changing strategies several times in just a few moments, which is virtually impossible for a human.

10. Reducing costs

All solutions that automate repetitive processes or end the customer service process even before a case goes to a consultant are vital sources of savings for financial institutions.

AI and ML are the future of finance

Of course, the above list is not a complete catalog of how finance uses ML and AI. However, based on it alone, we can see that these sciences are undoubtedly the cornerstone of FinTech and will guide strategic decisions and operational development in the sector in the coming years.

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