AI and ML in financial services
Machine learning in finance is rapidly developing – there are already dozens of options for its use in the financial sector. So why does the industry use AI for finance?

Credit Solvency Assessment
Artificial Intelligence helps banks more confidently issue credit to those who pass system checks. For this, programs and algorithms analyze all available information about a potential borrower, study their credit history, changes in their level of wages, and on this basis determines the reliability of the client and the security of the loan. Moreover, Chinese banks have already gone further and decided not to limit themselves to analyzing the data exclusively.

They began to introduce facial micro-expressions recognition technology. This allows them to find out if customers are lying about their financial situation when they come to take out loans. To do this, they developed AI systems that, with the help of smartphone cameras, detect minimal changes in facial expressions that are invisible to the naked eye. Thus, banks identify potential fraudsters, and they have already reduced their losses from unpaid loans by 60%.

Decision-Making
This is a global task that is successfully solved through the introduction of AI and ML in Financial Services. When an algorithm can analyze all of the available structured and unstructured data (both internal from the company’s business processes and external such as customer requests and their actions on social media), a financial institution can discover both useful and potentially dangerous trends. It helps assess risk levels and allow people to make the most informed decisions.

Fraud Protection
Banks and payment systems have already been developing models to identify and block most fraudulent transactions. These models are built on the client’s transaction history as well as the client’s behavior on the Internet. Systems based on Artificial Intelligence that detect online frauds have been developed from Big Data technologies.

Fraudulent social engineering will also be reduced by Artificial Intelligence. For example, when an impostor pretending to be a bank employee fakes data, his activity will be neutralized. Such systems will make financial deception unprofitable for criminals, and most felonious schemes will “die.”

Service Level Improvement
Many banks have implemented AI-based applications that allow customers to get answers to current questions. For example, a client can find out his expenses this month, the amount spent on food, credit card debt, the most affordable insurance, etc.

There are applications that, when connected to a payment system, analyze accounts. For example, for mobile communication or the Internet. These offer the owner more potential to save and make money. Sophisticated algorithms analyze user behavior online and allow financial institutions to develop more personalized and mutually beneficial offers. For example, if a customer is looking for opportunities to buy a car, the bank can develop a suitable loan offer after analyzing the customer’s financial situation.

Customer Retention and Acquisition Based on Data Analysis
Based on the analysis of the individual financial behavior of a client, banks are developing appropriate advertising or proposals. In this way, banks also receive information about the intentions of a customer or a potential customer. They get the opportunity to attract a new client who currently needs a personalized offer and can also take measures to withhold services if the client plans to refuse to work with this bank.

Increase Efficiency
Systems with AI help automate and optimize the processes that occur in bank branches. In the future, the use of paper media will be completely abandoned. All information will exist in electronic form. Thus, AI can facilitate the work with internal operations, taking on routine operations and handling them much faster (without sacrificing accuracy) than any employee.

In addition, the ability of AI to collect and structure constantly changing information can increase the efficiency of reporting. AI can also design internal documentation and even a list of frequently asked questions; all of these will be continuously updated as needed.

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