The sphere of finances has always been associated with immense accuracy, high risks and strict demand for the quality. In order to keep up to these standards, the industry has to adapt to the requirements of the modern business environment that is currently re-shaped by technology.

According to Juniper Research, the amount of global spending on Artificial Intelligence will reach $7.3 billion by 2022, raising from $2 billion in 2018. Such growth is stimulated by the fact that companies are planning to invest heavily into AI in order to improve the customer experience and services overall. No wonder the financial industry is keeping up and began to transform.

Background for change

One of the most critical factors is customer behavior and expectations towards businesses.

People are used that everything is now digitalized and so they expect an immediate response and transparent and fast services from any industry. Banks and other financial institutions have to adapt to these requirements.

Banking services are traditionally associated with complex paperwork and lots of confusing procedures. So by applying AI, banks have a good chance to turn the situation around and educate customers about their processes.

Another important factor is an inability of traditional financial methods and tools to cope with the amount and type of data that is available today. The data comes from the most various sources and in the most various forms – and all of it has to be processed and analyzed.

The implementation of AI successfully solves the issues mentioned above. Let’s have a look at some of the examples.

Virtual assistants

Today almost any website has an implemented chatbot. While some of them may be annoying, the overall use of chatbots has numerous advantages for both business owners and clients. The primary goal of such a bot is to provide the users with the required information, help them navigate the website and spend less time on information search.

Advanced bots, known as virtual financial assistants, can also help users with certain transactions, like money transfer or bill payments. These assistants can even give recommendations on financial decisions and do a bit of consulting.

We are talking about personalization here – which is the next big marketing thing, according to BrightEdge.

For the business owners, the development and implementation of virtual assistants do not cost much, but increases customer satisfaction and loyalty and improves customer experience. Such bots also free company employees from routine tasks and allow allocating the resources more efficiently.

Risk and fraud prevention

Financial industry relies on the data heavily and one of the most critical issues for all financial institutions is risk management and fraud prevention.

For a long time, this was a job for risk managers who were responsible for the company not losing its money. However, the design of risk prevention model by such specialist takes a lot of time and will always contain the probability of a human error.

Machine learning solves this issue by analyzing the data and building strong predictive models that help reduce potential risks and fraud. It is especially important for the lending industry, where NPL (non-paid loans) are a scourge.

Automation of processes

Traditionally, financial institutions dealt with the great amount of paper workflow. Even though they already switched to digitalization, still, many processes are performed manually or simply take too much time and resources.

The ML-based solutions, that some development companies offer, automate full work cycle and immediately speed up the processes, at the same time improving their efficiency. Not only are the tasks allocated better, but also the whole work process becomes more transparent and clear.

In addition, if such software is customizable, you can align it with your business goals and strategy in order to gain the maximum value out of it.

Improved credit scoring in lending

Lending business deals with many risks, as lenders have to carefully assess each borrower and, based on historical data, decide whether the borrower is creditworthy. There are certain scoring methods that have been around for decades but now they are giving in to ML-based credit scoring.

Machine learning solves the issue of thin-profile borrowers aka those who don’t have past credit history. Machine learning is also able to extract the value out of data that comes from social networks or telecom.

This improves the scoring process and can actually help the companies increase the number of borrowers by making more accurate decisions about giving a credit.

Conclusion

Artificial intelligence is a powerful tool with proven results and excellent performance. It is able to transform the financial industry and helps both huge organizations and small businesses to increase their profit and efficiency of the processes.

The competitive advantage that AI grants can be a true game changer so financial companies should already start implementing the technology into their processes. But in order to receive maximum value of it, it is important to set right goals and choose right tools.