Financial segment is a platform offering solutions to multiple tasks using AI-embedded technologies. For example, such systems mitigate a human factor in a decision-making process, analyze large data volumes more quickly and thoroughly, cut costs, and automate customer support. Due to AI, clients of credit organizations get professional and customized services. Follow the article to investigate the application of AI in the banking sphere.

AI for credit granting

Scoring is a system used by banks and microfinance organizations to estimate individual’s solvency. It analyzes various data about the client: income, education, work experience, credit history, marital status, etc. Every criterion is estimated according to the scoring sheet. Then, all the scores are totaled up, and the bank gets the idea of client’s solvency. This data helps the organization decide whether to give a credit to this person or not.

Contemporary financial organizations use innovative scoring products. Such systems learn the information about clients using artificial intelligence and machine learning. They analyze social networks of potential credit users, their Internet and account activity, and check the regularity of their payments for mobile communications.

Examples

American company ZestFinance developed ZAML. This platform collects and analyzes various data on a client of the financial organization, including their Internet activity. Based on the received data, algorithms identify the level of client’s solvency. Besides, the system can detect prospective clients among those rejected due to formal reasons, for example, the absence of credit history.

Hong Kong scoring platform Lenddo scans profiles of the potential credit user in social networks as well as profiles of their friends. If the scoring brings positive results, the client gets a credit. Such a program is currently used in the Philippines, Kenya, Australia, Indonesia, Thailand, and other countries.

Combatting fraudsters

Banks continue to combat fraudsters who illegally get credits and cheat on people making them transfer money to fake accounts. Here, artificial intelligence comes in handy. AI-enabled systems detect suspicious transactions and vulnerable clients. AI also conducts biometrical analysis to identify fraudsters.

Examples

Sweden-based company BehavioSec developed an AI system that identifies an individual by keyboard typing speed, button pressing force, and other criteria. This biometrical analysis may recognize a disguised fraudster.

In Ukraine, PrivatBank leverages artificial intelligence to identify clients whose risk of suffering from fraudsters is the highest. The representatives of the financial organization make additional phone calls to such individuals and instruct them on safety measures.

Chatbots

Currently, participants of the financial market replace conventional operators with chatbots more and more often. Virtual assistants boost service quality and help reduce expenses on call centers.

Bots can answer any questions, tell about services, connect to bank employees on 24/7 basis. What is more, they are capable of money transferring from one account to another.

Examples

Ukraine-based PrivatBank uses several chatbots. They are integrated into Facebook Messenger, Viber, and Telegram allowing to carry out transactions, get currency rate information, issue credits, submit applications, and participate in special offers.

Alfa-Bank Ukraine uses a chatbot called Alla. It can be found in Facebook Messenger, Viber, and Telegram. Alla tells about a balance, credit card transactions, currency exchange rate, etc.

AI for debt collectors

AI-fueled technologies are used at the companies that collect debts. Intelligent algorithms help such organizations pay off their credits.

Example

China-located company Ziyitong leverages an AI-powered system that analyses the Internet information about the borrower and his/her friends. Then, a virtual assistant calls a credit owner and initiates a chat about the debt. A special algorithm analyses the dialog. After all, artificial intelligence detects which communicative approach would be most likely to make the client return money. Besides, the AI-fueled system contacts client’s friends to encourage him/her to pay off debts.

AI replaces bank employees

Some contemporary banks practice replacing human staff for computerized systems. This solution allows them to cut the quantity of personnel responsible for routine tasks. In this way, players on the financial market would reduce expenses.

Examples

In 2017, top banks of Japan claimed automation of 30 000 workplaces in a bid to increase income.

For example, Mizuho Financial Group expects that computer systems would replace 19 000 staff by 2026. Due to computing algorithms, the holding company intends to lay off personnel who perform back-office work. What is more, the financial organization would use a robot aided system of data processing to cope with 100 routine tasks.

Other Japanese companies such as Sumitomo Mitsui Financial Group and Bank of Tokyo-Mitsubishi UFJ will also start a workforce automation process.

Conclusion

Currently, artificial intelligence is one of the most prospective technologies in finance. AI-embedded platforms assist banks in the automation of many processes and building good relationships with clients. Such systems improve service quality and bring more profit to financial institutions. Due to AI, clients of credit establishments get customized services of higher quality and save time spent on tackling various tasks.