According to the World Economic Forum, up to 463 gigabytes of data per day will soon be produced using innovative technologies. Here the question arises: how to cope with such a huge amount of information, especially in finance?
Fintech expert Sergey Kondratenko explains that Big Data uses technologies to collect, classify, process, and analyse massive and complex data. To achieve maximum efficiency in using Big Data, modern methods of analysis, machine learning and artificial intelligence are used.
Sergey Kondratenko: Application of analytics and machine learning for big data analysis
Machine learning algorithms provide a valuable tool for collecting, studying and implementing data in large financial institutions. They can be used in data segmentation, data analysis and modeling of possible scenarios. To support this statement, Sergey Kondratenko presents several examples that illustrate how machine learning can be used to analyse large amounts of information in the fintech industry:
- Conducting marketing research and audience segmentation are key aspects for the success of any business. The target audience is a fundamental component, practically the basis of the enterprise’s activities. Therefore, to achieve results, it is necessary to be well oriented in the interests of the audience and target market.
- Conducting market research becomes an integral part of the strategy of a fintech company. They can provide valuable data to generate leads. Machine learning is a powerful tool in this context. It uses supervised and unsupervised algorithms to more accurately interpret consumer behavior patterns.
This type of marketing becomes especially important for fintech companies at the start of their activities. It allows you to better understand how to move forward to maximise the effectiveness of attracting and retaining customers in the financial technology industry.
- Customer behavior research continues even after the company already has an idea of its target audience. Machine learning plays an important role in this context, as it allows you to gain a deeper understanding of your customers’ behavior and develop a sustainable strategy for interacting with them.
“This machine learning technique, known as user behavior modeling, is the result of the combined influence of humans and computers. It collects data by analysing users’ thought processes and provides information to make informed decisions. Facebook, Twitter, Google and many other companies successfully use user behavior modeling systems to better understand their customers and provide them with relevant offers”, says Sergey Kondratenko.
- Personalisation of recommendations becomes an integral part of the strategy of any fintech company. Whatever products or services companies provide, they must establish a strong connection with their customers, providing only what matters to them. In this context, the use of machine learning on large amounts of data finds its best application in recommendation systems. This technology combines context analysis with customer behavior predictions and allows you to influence user experience based on their online activity.
- Forecasting future trends is one of the key applications of machine learning algorithms based on big data analysis. These algorithms allow businesses to study and predict future trends. As Sergey Kondratenko explains, with the help of machine learning networks that can constantly learn and improve their analytical skills, companies are able to automatically analyse new data and use their past experience to make predictions.
- Machine learning plays an important role in decision making, especially using the time series analysis method. It allows you to analyse large amounts of data, including time, and provides managers with valuable insights that help them make informed decisions for the future.
The expert is convinced that for enterprises, in particular retailers, this method becomes an effective tool for predicting future events with high accuracy. It allows you to aggregate and analyse data on time trends, plan production more effectively, and predict demand for products and services. This improves the ability of enterprises to respond to changes in the market in a timely manner.
The transition to using machine learning in fintech is a significant step that requires preparation and integration, says Sergey Kondratenko. In his opinion, this is not just the introduction of new technologies at the top level, but a change in the entire ecosystem of a fintech company.
Sergey Kondratenko: Using Big Data for analysis and risk management in finance
According to Sergey Kondratenko, Big Data plays a significant role in risk management for financial institutions. It provides them with valuable tools and opportunities. The expert suggests paying attention to some of them:
- Ability to identify and respond to potential threats in real time. Traditional risk management systems most often rely on historical data analysis and periodic reporting, which may not always detect new threats or sudden changes in the market. Financial institutions can take advantage of Big Data for continuous monitoring and analysis of data in real time. This allows them to proactively identify risks and respond quickly to them.
- Sergey Kondratenko emphasises that Big Data significantly improves risk assessment. This method allows financial companies to dig deeper into data, identify hidden patterns, and assess risks in more detail, which helps them manage them effectively.
- An integrated approach to assessing risk indicators involves combining data from both structured and unstructured sources, including text data from news and social media. For example, analysing public sentiment on social media can provide information about public perception and sentiment toward certain organisations or industries. This can be very useful when analysing reputational risks.
- Simplify predictive modeling and scenario analysis is also of great importance for risk management, as Sergey Kondratenko notes. Financial institutions, in his opinion, can develop forecasting models. These models allow you to assess the likelihood of specific threats occurring and calculate the financial consequences. Fintech companies can more accurately identify potential vulnerabilities and implement risk mitigation strategies by conducting multi-scenario analysis. This predictive risk management method helps institutions stay ahead of threats and minimise potential losses.
- Improved Compliance Efficiency in risk management is essential for financial institutions that operate in a highly regulated environment. Big Data can significantly improve the ability of organisations to analyse huge volumes of information to identify any compliance violations. This allows companies to ensure compliance with regulatory standards and avoid fines by automating compliance processes.
- Simplifying implementation of Know Your Customer (KYC) and Anti-Money Laundering (AML) measures is also a significant aspect. As Sergey Kondratenko explains, by analysing customer data, financial companies can identify suspicious activity and potential threats. This allows them to meet their regulatory obligations more effectively and successfully combat financial crime.
Machine learning automates and Big Data transforms the risk management process in financial institutions. By harnessing the power of big data, agencies can detect hazards in real time, conduct deeper risk analysis, predict future threats, and comply with regulatory requirements. According to Sergey Kondratenko, Big Data helps fintech companies solve risk management problems and maintain stability in a constantly changing financial environment.
|Sergey Kondratenko is a recognised specialist in a wide range of e-commerce services with experience for many years. Now, Sergey is the owner and leader of a group of companies engaged not only in different segments of e-commerce, but also successfully operating in different jurisdictions, represented on all continents of the world. The main goal is to drive new traffic, create and deliver an online experience that will endear users to the brand, and turn visitors into customers while maximizing overall profitability of the online business.|