Undoubtedly, artificial intelligence is becoming an integral part of dynamically developing industries. Forward-thinking financial companies are actively following new AI algorithms in various fields. In this article, we’ll look at 5 of the most popular examples of artificial intelligence in finance.
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What are the benefits of AI in finance?
It is believed that the development of artificial intelligence in finance will increase efficiency in the financial system. New technologies influence customer service processes and the precision of credit risk management. At the moment, artificial intelligence is used in after-sales service or the preparation of banking offers for customers.
The benefits of implementing AI in this industry are huge. Let’s just mention several of them?
- Saving money
- Minimizing errors
- 24/7 interaction with the customer
- Faster and more accurate forecasts
- Improved risk analysis
- Reducing the need for repetitive work
Top 5 examples of artificial intelligence in finance
Suffice it to say, AI in finance is completely changing our interaction with money. From personalized banking, fraud detection through high-frequency trading, credit decisions, and risk management, AI helps the financial industry optimize and streamline processes.
AI provides individual customers with comfort and additional benefits. For instance, with intelligent chatbots, customers gain comprehensive self-help. This solution has another advantage. As customers can solve problems on their own, they don’t have to call the hotline, which in turn reduces the workload on the call center. Virtual assistants are gaining in popularity. They can, i.a., check account activity, check account balance, or even plan payments. And mobile banking applications, introduced in one of the banks in the US, can remind customers to pay off their bills and help in planning expenses.
The rise in online transactions has increased credit card fraud. Artificial intelligence has been spreading its wings for several years, and now it is excellent in battling financial fraud. AI systems may detect fraud by analyzing a person’s purchasing behavior and triggering alerts if something seems out of the ordinary or when it doesn’t match your usual spending habits. In addition to improving internal security, AI has simplified corporate operations. Thus, AI is the flagship tool in the fight against financial crime.
It’s no surprise that AI is used in trading as it helps analyze patterns in big data sets. Intelligent Trading Systems monitor:
- Structured data (spreadsheets, databases)
- Unstructured data (news, social media)
AI-powered algorithms process and analyze data more efficiently than people. And faster processing means faster transactions and decisions which is extremely useful in trading.
Additionally, it is worth mentioning support vector machines (SVMs) here. They have supervised learning models with associated learning algorithms that analyze classification and regression analysis data. The SVM models are trained to detect signs of a market price change, and they respond by adjusting their bids within a fraction of a second. Since they have been so successful, many high-frequency trading firms have incorporated them into their trading modules.
Risk management is an ongoing and important tool in finance, banking, and other industries. Artificial intelligence and machine learning can prove useful in using data to identify threats, provide better information, and save time. The risk management system mainly applies to banks and insurance companies. Machine learning algorithms can adjust data records and calculate whether an applicant is eligible for a loan or insurance. These models have thousands of variables to estimate the risk level for each client.
You can quickly and accurately assess a potential borrower based on various data and factors thanks to AI banking.
INFORMED CREDIT DECISIONS
As mentioned earlier, artificial intelligence allows us to process data quicker. Artificial intelligence in banking allows a more effective and more accurate (more factors are taken into account) assessment of a potential borrower.
Comparing the traditional determination of a customer’s creditworthiness in terms of timely loan repayment (the so-called credit scoring) with the scoring provided by AI, it can be observed that the latter system is based on more complex rules. As a result, lenders are able to distinguish between borrowers without an extensive credit history but with high creditworthiness and those who will not be able to meet their obligations.
Moreover, the advantage of AI-powered credit scoring is its objectivity. The machine will never be biased, and it works based on the verification of data, not emotions.
The combination of AI and finance can be very profitable. According to Business Insider, the implementation of artificial intelligence solutions will save the banking industry 447 billion USD by 2023. And a bank in North America that automates its mid-office tasks using AI could save 70 billion by 2025. You too can be part of this revolution! Take advantage of solutions based on artificial intelligence and invest in the development of your business.