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The basis of any business is information. Now, there are a large number of different data analysis methods that are used by companies to automate and achieve maximum efficiency. Financial companies that provide financial services are no exception. They own and process a huge data amount that requires quality management and a high level of protection. Data Science plays a key role in orchestrating these and other processes.

1. Fraud detection and prevention

A key indicator of a financial organization success is the security of customer funds and data. This is a rather difficult and time-consuming process. It is important to use not only technologies that will help block fraudulent activities, but also technologies that can detect suspicious activity at an early stage and assess the situation. Such technologies need to be regularly monitored and updated.

The huge number of transactions and processes makes it impossible to manually track suspicious activity. However, Data Science is a great tool in this situation. It allows to create an algorithm for self-analysis of certain actions, which will automate the detection process. This technology is capable of self-learning. Processing more data leads to more experience and knowledge. Detecting counterfeit documents, copies of financial transactions and invoices, suspicious activities, and preventing fraud is possible thanks to Data Science.

2. Risk assessment and management

Machine learning and AI, among other things, help assess financial risks and security. New machine learning models enable more effective risk analysis and management.

Competitors, authorities, investors and other participants may pose certain risks to the business. Any situation must be resolved taking into account an understanding of the risks, potential losses and possible growth points. This process requires the analysis of a large amount of processed and raw data.

Complex self-learning Data Science algorithms will be most useful in this case. They evaluate data to analyze risks, allowing companies to create a reliable model for future development.

3. Client data management

The most valuable resource of any company is data, and its management is too important. It is advisable to process such a large data amount that financial companies own automatically. AI will provide fast and efficient analysis of unstructured data.

4. Personalization

The income of a modern business depends on accurately guessing customer needs. The better the company guesses the client’s desire, the higher the chance of receiving more income. It works the same way for financial companies. Customers will be much more willing to use the company services that has an offer tailored to them based on their income, needs and situation.

Data Science allows to track user behavior and provides a complete picture. The business is able to make more informed decisions, and the client receives a unique and personalized offer.

5. Analytics

Data collection and analysis are 2 key processes. Modern technologies make it possible to efficiently process huge data amounts of various types. This allows to track data changes and make changes to prevent risks. For example, analyzing customer data opens new opportunities, allowing to respond to customer interest and build a high-quality marketing campaign.