Artificial Intelligence (AI) approaches are shaping the “bank of the future” and bringing about substantial changes in the operations of financial companies. Every day, such perspectives become even more important to enable business transformation — and this involves operational gains, increased productivity, more revenue and the creation of new digital products, in addition to promoting a better experience for consumers.
According to the 2022 FEBRABAN Banking Technology Survey, Artificial Intelligence is the second most applied technology in the financial sector, second only to investments in cybersecurity. And because it is a sector that deals daily with a huge amount of data, Machine Learning in the finance area is increasingly necessary.
As a matter of fact, Machine Learning, an Artificial Intelligence approach that allows a system to learn from data, identify patterns and make decisions with minimal human intervention, has proven to be extremely useful in detecting fraud and anomalies. . The use of machine learning in the financial sector is important because with it it is possible to perform predictive analyzes to have a better understanding of consumer behavior.
How did Machine Learning begin to be used in finance?
Initially, Machine Learning in the financial market began to be used due to the area’s need to have more concrete predictions about the next market movements. Thus, they would be able to anticipate some actions and maximize their financial return.
Financial Market Applications
- Process automation: consists of a technique to eliminate repetitive day-to-day tasks and reduce bureaucracy. In the case of the financial market, process automation is one of the most common applications of Machine Learning. Automation is used to:
• Expand the portfolio of available services;
• Improve customer experience;
• Optimize resources by directing them to the correct locations;
• Reduce costs. - Algorithmic trading: it is a strategy used by the financial market to monitor market movements in real time. In this way, it is possible to make decisions more assertively and intelligently; financial market professionals can identify patterns, predict bullish or bearish trends in the stock market, and much more.
- Credit scores: this is already a recurring and consolidated practice in the financial market. Basically, credit scores depend on a number of issues that are analyzed, such as the risk of breach of contract or risk of default. Here, Machine Learning analyzes hundreds of data from customer profiles and makes predictions of the risks that institutions run.
- Financial system security: with the growing number of financial transactions and cyber crimes, financial institutions are betting on Machine Learning as a security strategy to detect fraud, identify risks and isolate cyber threats to the institution’s systems.
As the financial market has a large volume of data, Machine Learning has the potential to improve many aspects of the financial ecosystem. With the growing number of transactions, users and third-party integrations, threats to financial system security also increase. In this context, Machine Learning should be used by financial institutions also in the security strategy, risk management and compliance. That’s because machine learning algorithms are trained to detect fraud. Institutions can use this technology to track account transaction parameters in real time. In this way, they are able to identify fraudulent behavior with high precision, warning the customer and even preventing the transaction when the probability of fraud reaches 95% (source: Cantarino Brasileiro).
Here at Topaz, we provide technology that generates security from the Onboarding process to the completion of the transaction with the most innovative in Machine Learning and AI, in addition to our solutions offering easy integration and high availability.