Today, artificial intelligence (AI), machine learning, robots, have gone beyond the imagination and are embodied infeasible business scenarios, becoming a profitable investment. In the financial sector, operations are already trusted in accounting, fraud detection, customer credit rating, resource planning, and reporting. But the introduction of technology entails new challenges and risks.

Automation for profit

Algorithms responsible for the cybersecurity of data detect fraud before it happens, and can quickly check the transactions of all portfolios of the bank. If a person takes a loan, AI will be able to evaluate it as a potential borrower faster and more accurately than a living specialist, taking into account more parameters.

Automation of routine processes allows you to protect the company from errors that the employee can make by negligence. Robots are also able to perform more functions, reducing company costs. Therefore, banks introduce robotic collectors that call customers with little debt. According to forecasts, if you trust AI to 30 processes, you can save four times as much - up to 85 million rubles.

Financial institutions use AI to create chatbots that answer customers the simplest and most common questions. The bot can even quickly form an investment portfolio based on the preferences and interests of a particular client, as well as prepare detailed reporting of expenses and remind about payment of bills.

Another crucial area in finance, where AI is necessary, is compliance with regulatory standards. It monitors changes in the law and helps to comply with it - from the "know your client" rules and the fight against money laundering to the laws governing asset management.

Why train a machine?

Machine Learning (MO) is a technology-based on artificial intelligence. It is based on a mathematical model that identifies patterns in data arrays and predicts how the situation will develop.

How does MO work in practice? In all companies, over time, the blacklist of counterparties is growing - companies with a high risk of default. At first, those who delay payments or are registered in risky jurisdictions fall into it. Over time, the filter becomes more complex, and machine learning will help to identify previously implicit patterns associated with macroeconomic indicators, credit ratings, data from third-party auditors, and how the company is written about on the Internet. Technology will do such a job better than a person who may simply not be able to cope with such a volume of information.

Challenges to Innovation

However, the role of AI is not without problems. The most important is the lack of qualified personnel. According to the study, 30% of the financial world only heard this term but did not understand how AI works. And today, the entire industry faces an important task - to increase the level of technical literacy.

The second serious problem is the lack of data for work. The larger the initial data, the higher the accuracy of AI predictions: with a small sample, the probability of error is 20%, with a broad array - up to 2%.

The introduction of AI into the work of financiers is hindered by several other barriers: the cost of operation, the lack of apparent benefits from the use of civil defense, the requirements of regulators and the ethical issue.

New technologies - new risks

When introducing new tools, the company faces risks that were not previously encountered in its practice. They can lead to financial and reputational costs. This raises the legal question of liability in the event of an error: who will be to blame - a financial specialist or an AI developer.

Consider a practical example. A trained algorithm may not always be able to avoid bias. Thus, according to a historical sample, in recent decades, women have been less likely to approve loans. And, based on the data presented, the algorithm will conclude that women are unreliable borrowers, and will refuse even creditworthy ones. The bank may face claims from regulators who see gender discrimination in these decisions.

The introduction of AI and machine learning will scale financial systems. This is relevant, given the projected increase in the number of financial transactions until 2025. A person cannot handle this amount of information. But this does not mean that AI will force a living specialist out of the financial sector. If the algorithms are engaged in routine operations, then the employee will always have the ultimate control and live communication with customers.

Conclusion

Generally speaking, AI is indeed a great tool in finances to avoid complicated situations and arrange matters, which usually require a lot of effort from humans. When you have a robot, doing tasks it is almost impossible it would make any error. But while the role of artificial intelligence is growing in finances, it could lead to other problems too.