Sergey Kondratenko: Artificial intelligence in financial technologies

One of the key success factors for FinTech companies is the introduction of artificial intelligence (AI) into their corporate processes, says Sergey Kondratenko. The data shows that more than 70% of the FinTech sector representatives are already actively using the capabilities of AI in their operations.

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 maximising overall profitability of the online business.

Sergey Kondratenko on the benefits of using artificial intelligence in finance

Global studies show that 80% of banks and fintech companies are aware of the benefits that can be obtained through the use of AI. Whether it’s personal finance, corporate finance, or consumer finance, AI’s high-impact capabilities have the potential to dramatically improve many services, Sergey Kondratenko points out. The expert notes that there are a number of main advantages of using AI in the field of finance.

Automation. Automation plays a significant role in keeping AI relevant in fintech. This process has become an important part of the operational activities of fintech companies.

– Thanks to artificial intelligence, financial institutions are automating a number of operations, including payment processing, customer support, billing and record keeping. In addition, artificial intelligence provides flexible human resource management, which is especially valuable in the context of remote work, – Sergey Kondratenko believes.

Secure payments. Fraud prevention and security are significant challenges for fintech companies. However, the introduction of AI into operational processes has led to increased security measures for transactions.

According to Sergey Kondratenko, the role of AI in this context is to carry out strong authentication and verification of the identity of users, constant monitoring of payments and other data.

Data analysis and forecasting. Fintech companies have a wealth of data about their customers and the current state of the market, and AI provides effective analysis of this information. For example, with the help of AI, it became possible to assess the solvency of customers, create individual offers and optimise marketing strategies.

Personalisation and improvement of customer experience. The use of AI allows fintech firms to create customised solutions and improve the customer experience. Companies are able to provide personalised recommendations to customers, better communication through chatbots and other innovative technologies.

Sergey Kondratenko: using AI systems in the field of customer service for fintech companies

For fintech companies to interact effectively with customers, both parties need to speak the same language. In this direction, AI has proved its effective application. Sergey Kondratenko proposes to consider how AI systems are used in practice to communicate with customers and what methods are used for this.

1. Systems based on natural language. Such systems have a key advantage in removing language barriers in communicating with customers. If the Internet and digital services have long helped to cope with geography, the language barrier remains. Support for the natural language of customers, including dialects, not only facilitates interaction and improves communication comfort, but also expands the reach of customers with disabilities.
2. Chatbots are an imitation of human speech behaviour during communication. These systems are suitable for implementation in automated call centres, online customer support services on the site and through mobile applications. Their application can be used in SMS or text communication on the website.
3. Robotic assistants. Such systems can be integrated into customer support centers to provide information about products and services, perform financial transactions, provide investment recommendations, statements and other documentation in offices. Such assistants have also proven themselves to be used to analyse candidates and evaluate their qualities when hiring, and can serve as a service for managing an investment portfolio.
4. Alternative financial advisors – robo-advisers or algorithmic trading. The main direction of this type of AI is online trading. These systems are able to provide real-time advice, monitor the market, open and close accounts, assess risks, and process a large number of transactions simultaneously. Such programs can function as mobile applications.

Challenges for artificial intelligence – 2023: problems and prospects – Sergey Kondratenko

By 2023, significant advances have been made in the field of AI, which have contributed to innovation in a variety of areas. Despite this progress, it is important to recognize that the adoption of AI comes with its own set of challenges. According to Sergey Kondratenko, problematic aspects create many challenges for AI that require careful analysis and development of strategic solutions.

Misunderstanding. Misunderstandings that occur in practice can hinder the development of reliable and accurate AI systems. To solve these problems, companies can invest in research and development to improve AI algorithms, models and methods.

Privacy issues. AI systems rely on vast amounts of data for effective learning and performance. They contain personal information, which raises privacy and data protection concerns. To address these issues, fintech companies should pay due attention to the implementation of reliable mechanisms such as anonymisation of data, secure storage of information, compliance with relevant rules and regulations governing data protection.

Computing power requirements. AI systems can be demanding to complete complex tasks. This can lead to significant infrastructure costs. To solve such problems, companies can use specialised AI chips and distributed computing systems.

Lack of data. AI systems rely heavily on large datasets that not all industries have access to. Techniques such as transfer learning, augmentation, and synthetic data generation can help mitigate the problem.

Unreliable results. In practice, the use of AI can give unreliable results due to incomplete data sets or the complexity of the task at hand. Therefore, companies should rigorously test and validate AI systems during development.

Lack of trust. Some people don’t trust AI, it’s often due to a misunderstanding of how it works. Companies can increase trust by providing clear and accessible explanations for how such systems come to their conclusions.

Unclear goals. The development of effective AI systems becomes impossible without a clear goal setting. To solve this problem, companies should conduct a comprehensive assessment of their business processes and identify specific areas where AI can be most useful.

Technical difficulties. The implementation of AI systems involves solving various technical problems such as data storage, security and scalability. Companies should invest in a robust infrastructure that can handle the volume and complexity of data.

bias in algorithms. To cope with such challenges, companies need to implement strategies such as careful selection and pre-processing of training data to minimise biased models, while conducting regular audits to ensure fairness in AI systems.

Implementation strategy. Each company has unique requirements and an effective implementation strategy must be tailored to its specific needs. Before implementing AI, it is essential to conduct a thorough assessment of the existing infrastructure, data availability and organisational readiness.

– Companies should formulate a clear roadmap that describes the steps, resources and timeframe required to successfully integrate artificial intelligence, says Sergey Kondratenko. – Collaboration with experts in the field and seeking external guidance can also provide valuable input during the implementation process.

The expert advises considering the introduction of AI as a long-term path, during which you must be ready to constantly adapt to new technologies, changing rules and business needs.

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