Machine learning (ML) is a special type of artificial intelligence (AI) that allows computers to learn without specific programming.
In financial technology, ML uses algorithms to quickly and efficiently process and analyse large amounts of data. This can help the company reduce operating costs, increase the efficiency of internal processes and increase competitiveness.
How does FinTech apply machine learning?
Automation of processes and improved decision-making
Replacing manual work and automating routine repetitive tasks with software solutions is one of the popular ML applications.
Neural networks allow fast and efficient data analysis. Even a large amount of data could be carefully analysed and useful information could be obtained for real-time decision-making.
This helps the company save time and money, increase productivity and meet changing customer requirements.
Customer support
Using ML in FinTech minimizes human error and improves customer support quality. It also helps to better understand their needs and personalised service recommendations. In some cases, AI technology is able to recognise models and sentiment as well as understand and respond to unique requests.
For instance, dialogue systems and chat bots. They process user messages, accept suggestions and complaints and solve elementary tasks at any convenient for clients time.
Security. Fraud prevention
Special ML algorithms analyse client’s transactions, decisions and other actions. Detecting suspicious activity that potentially threatens user, report about it, thus preventing account stealing and payment fraud.
In AZN Research, trained neural networks also detect anomalies in financial transit processing of financial transactions. It is necessary to comply with the safety rules associated with the movement of funds.
This operation enhances the security of your organization and allows you to keep your client’s assets intact.
Predictive analysis
ML algorithms play an important role in forecasting trends in the financial market. Companies use them to predict market risks and financial anomalies, reduce fraud, identify financial opportunities, etc.
Companies are training models on huge amounts of data, such as financial interactions, loan repayment, company shares, customer interaction. This means that neural networks can predict future trends in lending, insurance, and stocks.
Prediction of consumer trends helps a lot in understanding a customer behaviour. In addition, based on this historical data, trained technologies can provide users with service advice.
Machine learning introduction is profitable for the FinTech industry. Its algorithms are used in various tasks in the financial sphere: from crediting to improving the security of financial operations. They can also be directed to both individual customers and corporations.
At AZN Research, we use the advantages of machine learning techniques to optimize our work processes.
For example, Sentiment analysis is used to determine the qualitative evaluation of feedback in CIS.
The application of OCR (or optical character recognition) is needed for template and document reading. It allows you to automatically determine the type of uploaded document to optimize the process of registrations, creation of questionnaires, etc. Also, the use of OCR is necessary to fill in the forms with values from read documents: license numbers, different dates, MRZ (or machine reading zone), identification numbers, etc.