Banking sector needs artificial intelligence

The banking sector needs artificial intelligence and the banking sector is increasingly demanding mixed profiles with knowledge of artificial intelligence. This includes economic data analysts, software engineers, user experience developers, or profiles associated with digital sales. Moreover, the demand for this type of worker is constantly evolving and revolves around a central requirement: the need for these people to absorb business knowledge.

The impact of artificial intelligence on the Banking Sector

In recent years, the use of artificial intelligence-related tools has increased significantly in all economic sectors, thanks to the huge amount of data and the increased ability to process information. Applying these tools to financial services assumes the potential to increase not only sector benefits but also global consumers with better banking services.

Artificial intelligence is nothing more than a set of theories and algorithms that allow computers to perform tasks that normally require the capabilities of human intelligence to be recognized or interpreted. But artificial intelligence can go beyond human intelligence, which improves capabilities.

The most important part nowadays of artificial intelligence is in what is called machine learning, and it is nothing more than machine learning, where the computer can extract the results from the statistical analysis of a series of data points with these conclusions. The automatic operation is improved, which increases the efficiency of the algorithm.

If we look at European Commission data in 2018, private investment in artificial intelligence already exceeds 6,500 million euros in Asia, and 12,000 million dollars in the United States and Canada, while in Europe we are still too late to invest 3,500 million euros.

Artificial intelligence in the banking sector

The winds of technological change are sweeping the huge banking sector, not only as digital banking services in Europe but even in the most remote areas of the world. The banking sector needs artificial intelligence It’s an exciting time for emerging banks and innovative financial institutions, while big banks have no choice but to change the appearance of the new digital avatar.

AI analysis with the development of big data for automated financial advice can generate significant benefits for both industry and consumers, such as enabling greater financial inclusion, improving customer protection and privacy, improving customer experience, cybersecurity, and ensuring good risk management and reducing the huge costs of compliance Regulatory.

Thanks to digital banking initiatives, it is not necessary to leave our homes, either to buy or pay bills. Providing a seamless digital banking experience is now more necessary than ever if financial institutions want to stay in the game.

Meanwhile, artificial intelligence can pose new challenges that must be addressed to take advantage of all these new opportunities. Promoting a broad dialogue on all aspects of the development of artificial intelligence and its impact remains a key priority for the banking sector.

AI is also expected to have widespread credit in the banking sector. About 67% of CEOs expect AI to achieve net gains in jobs within their bank in the next three years. Between 2018 and 2022, financial services companies that invest in artificial intelligence and human-machine cooperation can increase their revenues by 32%.

Meanwhile, it is estimated that due to the adoption of new technologies such as artificial intelligence, the need for human experts will increase to do so and could increase the levels of staffing in organizations by 10% between 2018 and 2022. In addition, 77% of banks plan to use intelligence Artificial automation tasks largely or large in the next three years.

Today we already see in different banking entities how they use automated chat programs and voice bots to interact with clients and resolve requests before human intervention. The technology behind them – natural language processing and generation – will make it harder for customers to know if they’re talking to a human or AI interface.

Where does artificial intelligence go in the banking sector?

According to a report by PricewaterhouseCoopers (PwC), the robotics and artificial intelligence application in this syndicate will evolve in a manner similar to the arrival of ATMs.

It should be noted that in 2020 it is estimated that there will be twenty times more data in the world than we have today, and it is a great opportunity for financial institutions to understand, in real-time, the behaviors and needs of their clients. This is how data analysis will become a major tool for the growth of this sector.

All services will be automated, so far the financial instruments are developed by other companies that do not belong to banks we can say that the banking sector needs artificial intelligence.

Artificial intelligence to control credit risk

Applying state-of-the-art tools based on artificial intelligence technologies – such as machine learning – to project future potential losses in adverse macroeconomic scenarios is a major success as a preventive measure in the entities that implement them. This type of program allows the visualization of different future scenarios at the same time developing models that include a much larger amount of variables than the usual technologies in which entities operate. The result is that with the application of machine learning, the predictive power of traditional statistical models is greatly improved. The economist explains: “The range of improvement in predictive power provided by machine learning ranges from 25% to 50%, which translates into commercial terms that are not insignificant amounts.”

Despite the fact that between 15% and 20% of banks ’investment in artificial intelligence goes to risk management solutions, this field is somewhat more reluctant to introduce technologies like machine learning. The machine learning model uses many variables more than traditional ones and they are able to find mutual relationships that no one can find, and therefore have a high degree of success and a much greater predictive power. This is its great advantage but at the same time the biggest obstacle in its application to risk, since risk models, in this case, are subject to the control of the regulator, especially since the last financial crisis, and it is necessary to overcome the feeling of black funds with an unknown performance. “The AIS group has solved this problem by developing tools that allow us to follow the path that leads these models to a certain result, so it is highly recommended to use them in risk control,” says Aguirre.

According to the AIS Group, although risk management is not the area in which the largest investment in innovation is taking place in machine learning, this does not mean that banks do not invest in these technologies in controlling risk, among other reasons because they are very accurate and have a return. High. “The current trend is geared towards monitoring portfolios and recovering unpaid loans more than assessing credit requests,” says the executive.

Kenneth

Kenneth is a proud native of sydney, born and raised there. However, he pursued his education abroad and studied in Australia. Kenneth has worked as a journalist for almost a decade, making valuable contributions to prominent publications such as Yahoo News and The Verge. Currently, he serves as a journalist for The Hear Up, where he focuses on covering climate and science news. You can reach Kenneth at [email protected].