With a constantly changing financial sector, AI technologies will become more and more important for this industry and the pressure to innovate will increase. The hesitant application of machine learning and artificial intelligence is undoubtedly also due to the strong regulation and legal requirements that financial institutions have to meet.
But artificial intelligence is now an integral part of many financial advisory systems because it can extract insights from data and create patterns that would be very difficult for humans to discern. In addition, Model risk management can make predictions about how these patterns might repeat themselves in the future. Currently, the data available in the company often remains unused. Like other industries, financial companies have veritable mountains of data on user behaviour and transaction flows. Anonymized evaluations of this already existing data could offer those responsible important and, above all, favourable decision-making support.
High-quality data is necessary
For an AI-based technology to work successfully and to develop relevant theses for decision-making, high-quality data is required. A decision can only be made based on the right data, and this applies to both analogue and digital decision-making processes.
This is particularly important in the financial sector. Every day, millions of pieces of data are generated from different sources. Analysts need to monitor the accuracy, security, and speed of data to make predictions, develop new financial models, and create forecasting strategies. Machine learning algorithms are ideal for this because they can process, structure and analyze a large amount of data in a short time. They are therefore an important source of information for decision-making in companies – with it about customer service, fraud prevention or risk assessment.
Machine learning is also changing transactions and investment behaviour. Instead of just analyzing stock market prices, with the help of big data it is now possible to include political and social trends that can affect the stock market. With machine learning, trends can be tracked in real-time and action taken accordingly.
