The introduction of artificial intelligence in finance is happening rapidly: very soon many of the processes of a bank or insurance company will be fully managed by neural networks. It’s efficient and convenient, but there are also some things to be aware of.

What are the pros and cons of AI in finance

Today, learning neural networks and robotic information analysis systems are increasingly being incorporated into the work processes of banks, insurers and investment companies. This includes trading, underwriting, insurance billing, fraud detection, borrower credit risk assessment, market scenario modeling, and robotized communication with customers using voice and text bots.

And the more artificial intelligence infiltrates companies, the more likely it becomes that there will be a lot of moments where decision-making, risk management, and data access are required. Bankers themselves agree that they don’t always understand why, for example, scoring robots produce different results of calculations. This means that to date, the effect of artificial intelligence (AI) will be difficult to see in banks’ reporting, as it’s difficult to calculate and even more difficult to formalize.

Well, here’s more about AI and its advantages and disadvantages in finance.

What is AI

Artificial intelligence is the ability of machines or computer programs to learn, think, and reason like the human brain. The AI system receives data and instructions, from which the system draws conclusions and performs functions. Over time, it continues to learn human thinking and logic, becoming more efficient along the way.

AI is everywhere, whether it’s automatically searching Google or driving a car. Artificial Intelligence, with its vast array of technologies, allows machines to “feel” like the human brain, to learn and to act.

Today’s artificial intelligence systems are capable of performing complex calculations at tremendous speed. They can process huge data sets and make accurate predictions.

What are banks using AI for

Artificial Intelligence is already being used by banks to provide services to customers and improve business processes. However, the heyday of this technology may be yet to come. AI in banking has accelerated access to products for many customers and automated some stages of internal processes, which has also impacted the speed of service.

Banks use AI in these cases:

• Customer scoring.
  • Automatic decision-making on customer requests for credit services.
• Voice assistants and chat bots.
  • They’re used when clients contact the bank’s call center or chat room to reduce service time and optimize employee performance.
• Anti-Fraud and Financial Monitoring.
  • AI is used to counter financial fraud by analyzing atypical behavior of individuals and legal entities.
• ATM maintenance.
  • AI predicts ATM load and reduces collection costs.
• Document processing.
  • AI can automatically process and enter customer data when opening accounts and performing banking transactions that require proof of identity.

How can the quality of AI be controlled

A robot is a subject of action, but not a subject of law. Therefore, it’s not clear on whose shoulders the responsibility for its decisions rests. It could be the manufacturer or bank that uses it, or the manager who supervises its operation. Hence the question of what information an AI developer could give to the regulator because it’s both know-how and intellectual property, and a competitive advantage that cannot be open to all.

Second is the issue of cybersecurity. When regulatory scrutiny and control of AI begin, the threat of “corporate spying” and customer data leaks will immediately increase.

Third, if AI is used incorrectly, fines and sanctions should be defined by the regulator. Companies may need to create a separate risk exposure record.

These are all important issues that must be fully explored.

What are the advantages of the AI in finance

To conclude, artificial intelligence technology can provide companies with a wide range of benefits.

For example, artificial intelligence efficiently processes large amounts of information. As a result, people will soon be able to get real-time dashboards of information in the blink of an eye. Essentially, computers will take over the manual work of analyzing data and creating meaningful reports.

Using this data and metrics, FP&A (Financial Planning and Analysis) professionals will be much more efficient in making budgets and forecasts. They will have more time to build business relationships as well as consulting work.

Risk assessment with advanced software is already showing promising results across all industries, and finance is leading the way. Similarly, AI can be more effective than humans when it comes to detecting and preventing fraud.

In addition, AI-based solutions will eliminate bias from metrics. Of course, we will have to say goodbye to human error as well. After all, machine algorithms don’t get tired and don’t want to sleep, no matter how many hours of overtime they spend per week.

Better data quality will lead to more efficient FP&A teams, which is the ultimate goal of every such team. Better graphs and informative charts will lead to better decisions.

What are the disadvantages of the AI in finance

Currently, most organizations cannot afford premium AI solutions. High-tech technologies are too expensive for most companies. Nevertheless, with the rapid development of technology, the situation may quickly change. However, there’s also the dangerous nature of artificial intelligence.

Of course, the main thesis is that AI will make humans obsolete. Once machines have grown their own intelligence, there will be no stopping them. However, the situation in practice shows that this is nothing more than a myth. No matter how sophisticated algorithms are, they cannot copy our common sense. In other words, human intuition remains the elusive ingredient that makes the difference between robots and humans.

Data misuse in business often leads to enormous losses, so the growth of AI must be accompanied by continuous improvement in security procedures.

The black box of artificial intelligence comes with ethical and economic risks, so FP&A teams must be prepared to confront the challenges and prevent the malicious use of new apps and tools.

In other words, you have to think three steps ahead.

Risks include systems getting out of control and causing harm to humans and society, incomprehension and unpredictability of algorithm actions, insufficient stability and reliability of decision-making systems. It’s often difficult to figure out why the AI chose a particular decision. This may lead to distrust of systems using artificial intelligence technology.