AI in financeAI is changing the world, we investigate how AI in financial services changing especially when markets are volatile.

A great benefit of AI is that it can be used in different ways. For instance, AI-driven chatbots can help AI in financial services communicate with their customers. AI also serves as the basis for virtual assistants. Machine learning algorithms also enable algorithmic trading and can be used for risk management, fraud detection, and relationship management.

AI in financial service offers numerous benefits. Perhaps, the main advantage of AI is that it gives countless automation opportunities. In turn, automation can help AI in financial service increase the productivity and efficiency of many processes. Besides, given that AI can replace humans in certain situations, it helps eliminate human biases and various errors caused by emotional or psychological factors.

Obviously, AI is also better at analyzing data. Machine learning enables computers to identify patterns in data, providing decision-makers with valuable insights, and helping organizations get more precise reports.

So, how is artificial intelligence used in finance?

Automation

Automation helps companies eliminate human errors and enables employees to focus their efforts on more important tasks that a machine cannot complete. According to statistics, AI helps companies save up to 70% of the costs associated with data entry and other repetitive tasks.

Many big companies realize the advantages of AI, so they develop their own AI-driven solutions or use existing automation solutions that enable you to adapt and use them for your specific objectives. 

Credit Decisions

Artificial Intelligence also helps banks assess potential borrowers much faster and more accurately, while also saving costs. AI-based solutions can immediately analyze countless factors that can have an impact on a bank’s decision. AI uses more complex credit scoring approaches than traditional systems so banks can understand whether somebody is a high-risk applicant or simply doesn’t have enough credit history.

AI-powered software offers a higher degree of objectivity. Machines are not biased, which is a critical factor, especially in financial app development. Loan-issuing applications and digital banks allow banks to provide various personalized options and integrate alternative data, including smartphone data, into the decision-making process.

AI is beneficial not only for banks but also for many other companies. For instance, automobile lending companies report that the use of AI enabled them to cut their losses by up to 23% annually.

Trading

The trend of data-driven investments has been demonstrating steady growth during the last decade. Two years ago, data-driven investments closed on a trillion dollars. AI and machine learning are used in so-called high-frequency trading, also called quantitative or algorithmic trading. This type of trading becomes more and more popular because it offers numerous benefits.

AI-driven trading systems can analyze massive amounts of data much quicker than people would do it. They can work with both unstructured and structured data. The fast speed of data processing leads to fast decisions and transactions, enabling traders to get more profit within the same period of time.

Besides, predictions made by AI algorithms are more accurate because they can analyze a lot of historical data. AI algorithms can test different trading systems, offering a new level of validation effectiveness so that traders can evaluate all the pros and cons before using a certain system.

AI can analyze a specific investor’s long-term and short-term goals to provide recommendations on the strongest portfolios. Financial institutions often use AI to manage their entire portfolios. The forecasting capabilities of AI have also been appreciated by numerous companies.

Risk Management

Risk management is another area of application of machine learning in finance. Given that AI offers incredible processing power and can handle massive amounts of both structured and unstructured data, it can handle risk management tasks much more efficiently than humans. Machine learning algorithms can also analyze the history of risks and detect any signs of potential problems before they occur.

One of the main advantages of AI in finance is that it enables organizations to analyze various financial activities in real-time, regardless of the market environment. Organizations can choose any important variables for their business planning and use them to get detailed forecasts and accurate predictions.

Fraud Prevention

AI has also proven to be very effective in preventing and fighting fraud. Cybercriminals constantly develop new, more effective tactics, but AI-based solutions can use machine learning and quickly adapt to the hackers’ strategies.

Such solutions are especially effective when it comes to fighting credit card fraud. This type of fraud has become more and more common during the last few years because of the growing popularity of online transactions and eCommerce.

AI-driven fraud detection tools can analyze clients’ behavior, track their locations, and determine their purchasing habits. Therefore, they can quickly detect any unusual activities that diverge from the regular spending pattern of a certain client.

Banks can also use artificial intelligence to deal with other types of financial crime. For instance, AI can be used to fight money laundering. Machine learning algorithms can quickly detect suspicious activity and minimize the costs of investigating money-laundering schemes. According to research, AI can decrease the cost of investigations by 20%.

Personalized Banking

The advantages of AI become obvious when it comes to personalization and providing additional benefits for users. For instance, banks use AI-powered chatbots to offer timely help while also minimizing the workload of their call centers. Financial organizations can also use various voice-controlled virtual assistants.

Such solutions are self-learning so they become more and more effective as you use them. Both virtual assistants and chatbots can also be integrated with other software. For instance, they can schedule payments, monitor account activity, and check balances.

There are also many apps that offer personalized financial advice so that users can achieve their financial goals. These smart systems can track regular expenses, income, and purchasing habits to provide the necessary financial suggestions and optimized plans.

What to Expect in the Future from AI in Finacial services

Customer service

Conversational interfaces and chatbots are becoming more and more common. There are many universal chatbot solutions that can be used by companies from different niches, but companies like Kasisto are already developing industry-specific software intended for banks and other financial organizations. Such software will help customers make the necessary calculations and evaluate their budgets quickly.

Besides, voice recognition enables banks to provide assistance in the most convenient way possible. Such solutions will inevitably become a huge competitive advantage because banks that offer quick interaction and querying will be able to attract customers of traditional banks that require their users to log onto banking portals, look for the necessary functions, and search for the necessary information themselves.

Standards of Security from AI in Finacial services

Passwords, usernames, and security questions may disappear from the financial industry in the next few years. Security is especially important in the financial industry because most people would rather have their social media accounts hacked than become victims of hackers who want to steal their credit card information. Therefore, the financial industry is most likely to use AI-backed security solutions to make sure that no one can access their customers’ data.

We’ve already mentioned that AI can detect unusual and suspicious behavior. Thanks to speech recognition and facial recognition, as well as the analysis of other biometric data, banks might add new layers of security or even replace traditional passwords with more effective approaches.

Recommendations

Automated solutions for financial sales already exist, but not all of them involve machine learning. Most often, these are rule-based systems. However, virtual assistants can also provide recommendations in a smarter way. For instance, they are already capable of making suggestions on possible changes to the portfolio, but they can also analyze various websites with recommendations on insurance services and help you choose a plan that meets your objectives.

AI-driven apps are becoming more and more personalized, and personalized recommendations are no longer used exclusively by companies like Netflix and Spotify. For instance, insurance companies have already started to rely on big data, and virtual assistants capable of providing personalized recommendations might replace personal financial assistants.