Machine Learning And Neural Networks In The Fintech Industry – Sergey Kondratenko

According to fintech expert Sergey Kondratenko, thanks to machine learning, financial organisations can quickly, reliably and accurately solve a wide range of problems related to the processing, analysis and interpretation of data.

The next stage in the development of automated innovative technologies in the field of fintech is neural networks. Complex algorithms allow computers to fine-tune their decision-making process, in other words, to “learn.”

As Sergey Kondratenko explains, neural networks in the field of financial forecasting and analysis can be used to more accurately predict future events based on the analysis of past data.

How do these two technologies work in modern financial practice?

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: Automation of Solutions in Machine Learning Technology

The fintech ecosystem covers a wide range of categories, including payments, insurance, investments, lending, etc. The most promising and investment-attractive segments for the implementation of machine learning, according to Sergey Kondratenko, are the following solutions:

1. Payments and money transfers. The volume of digital payments and money transfers, which are carried out using various electronic means of payment, such as e-wallets, payment cards, mobile applications and others, is measured in billions of transactions around the world. Thanks to machine learning technologies, the payments and remittance sector is able to process huge volumes of transactions quickly and securely.

An example of this development is TransferWise, a peer-to-peer money transfer service that successfully uses AI to optimise infrastructure management and prevent financial crime.

2. Lending and financing. P2P lending provides an opportunity to bypass banks and lending companies, since ordinary people can carry out lending transactions directly, without intermediaries. An online P2P lending service such as Kabbage allows you to complete loan transactions using your laptop or smartphone.

– Such solutions use machine learning (ML) algorithms that analyse data from public profiles and other information. In this way, they determine whether the applicant meets the requirements for a loan. In addition, portfolio analysis and credit risk assessment models also rely on AI advances, explains Sergey Kondratenko.

3. Retail banking. Banks are actively using machine learning methods for various purposes, such as improving customer experience, increasing operational efficiency, optimising budgets, ensuring confidentiality, and much more.

An example of such innovation is JPMorgan Chase, a leading financial services company on a global scale. Here, machine learning technologies were introduced to create the COiN platform, which is used to analyse specific data and documents.

4. Insurance. The insurance industry is characterised by a large amount of data required for underwriting and decision making. Therefore, it is considered one of the most attractive for machine learning. As Sergey Kondratenko reports, three areas are leading today, including digital assistants, insurance market analytics and driver performance monitoring.

For example, the Italian virtual insurance agent Neosurance uses the interaction of a machine learning system and IoT to analyse and obtain information about the client.

5. Blockchain and cryptocurrencies.

– Thanks to blockchain, fintech has enormous potential for development and increased efficiency, emphasises Sergey Kondratenko. – And machine learning can be considered as an accelerator for the development of this technology. Combining them allows you to analyse huge amounts of data incredibly quickly. Moreover, security is also improved if the chain is controlled by artificial intelligence.

For example, Coinbase, a well-known cryptocurrency transaction service, opted for a new AI-based identity verification system when faced with the problem of slow user verification. Now buying/selling digital currencies such as Ethereum and Bitcoin through this service has become much more secure than before.

The world of money requires quick and sometimes immediate action. Therefore, speed is vital for a company to become a competent player in the fintech market. And machine learning algorithms, in turn, make it possible to speed up data processing and find solutions for managing huge amounts of information.

Application of Convolutional Neural Networks For The Analysis of Financial Data – Sergey Kondratenko

Convolutional Neural Networks, or CNNs, by their status are used in image and video recognition, but they have also rocked the world of finance. The same algorithms that help you unlock your phone with your face now predict stock prices, detect fraud and much more, says Sergey Kondratenko.

CNNs, a subset of deep learning, have traditionally excelled at processing grid-like data such as images. But creative thinkers adapted them to financial data, and the results were promising.

Here is a list of the advantages of CNN in fintech, which Sergey Kondratenko highlights:

• CNNs can change based on the data, making them more flexible than fixed algorithms
• Data processing. The financial industry is filled with huge amounts of data, and CNNs can process it efficiently
• Early results show that CNNs outperform traditional models in some applications

Practical Application of CNN in Financial Markets

In financial markets, where huge amounts of data are generated every second, CNNs are a reliable tool for analysis and forecasting, reports Sergey Kondratenko.

• One of the main applications of CNN is to identify market trends. Traders and investors need to monitor market trends to make informed decisions. CNNs can analyse time series of stock price data, identify patterns and trends, and provide information on which trading decisions can be made
• High Frequency Trading. Here decisions must be made in a split second. CNNs, which can quickly process large amounts of data and make predictions, provide a significant advantage in this area
• Additionally, CNNs help improve financial security. Financial fraud is a serious danger. As Sergey Kondratenko noted, CNNs can learn to identify patterns associated with fraudulent transactions and thereby help in their detection

Recurrent Neural Networks For Time Series and Transactions

In the modern era, technology penetrates into all areas of our lives. There has been an interesting shift in financial markets, with AI and machine learning coming to the fore. Among these ML technologies, one that stands out as having unique capabilities is the recurrent neural network (RNN). It has enormous potential to revolutionise predictive modeling in finance.

– At the heart of RNN is a simple but powerful concept – memory. Traditional neural networks process input data independently of each other, without regard to sequence or order, Sergey Kondratenko focuses on the features of the technology.

RNNs have proven particularly useful in financial equity markets. Their inherent ability to process sequential data makes them well suited for time series analysis of financial data. Sergey Kondratenko gives this example: when predicting stock prices, RNN can take into account past stock performance, recognise trends and make more accurate forecasts.

In financial markets where volatility often reigns, the predictive capabilities of RNNs can provide a much-needed edge to investors and analysts. From forecasting stock prices to forecasting market indices, RNNs can play an important role in making informed decisions in the financial sector.

Today, the effect of technological innovation is clearly visible in the financial sector. What to expect next? In an answer to this question, Sergey Kondratenko suggests that the future of algorithmic trading and other applications in the financial industry is moving closer to the revitalisation of automated systems. Data processing is moving to a more advanced level, when digital assistants become full-fledged labor resources, and traditional money services are gradually becoming a thing of the past.