Fintech expert Sergey Kondratenko said that the portrait of the modern consumer is very detailed and multifaceted. It includes financial transaction information and other information such as personal data, online activity, shopping habits, media consumption, social media activity, travel, location, and interests.
Financial services companies use this information by combining customer data with information from various sources to understand their characteristics better. This is where big data technology comes to the rescue, automatically analyzing large volumes of data and helping effective management in financial companies. It is based on personalizing products and services, allowing companies to customize their offerings and assess risks and outcomes more accurately.
Sergey Kondratenko is a recognized specialist in a wide range of ecommerce services with experience for many years. Now, Sergey is the owner and leader of a group of companies engaged in different ecommerce segments and successfully operating in other 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 the overall profitability of the online business.
Sergey Kondratenko: How does big data help improve service and protect clients in fintech?
It should be noted that the volume of big data in the financial industry is snowballing and, by 2024, is expected to reach a whopping 149 zettabytes. Already, 4 out of 5 consumers are looking for deep personalization of services provided by fintech companies. Studying how this tandem works and what fintech can offer consumers is essential.
According to Sergey Kondratenko, there are several separate technologies with the help of which fintech and big data effectively interact – data processing, analytics, and data security.
1. Processing large volumes of data makes tracking user activity in real-time for future analysis easier.
Data mining allows you to extract valuable information from large volumes of unstructured data stored in data lakes. Data visualization in customizable dashboards provides visibility into key business processes. Stream computing systems aggregate data from IoT applications and devices, analyze it in real time and provide clean data sets for consumption.
2. Sergey Kondratenko believes data analysis is an important aspect of personalizing services in fintech. It includes improving risk analysis methods using artificial intelligence and machine learning, namely:
- Decision-making is based on data and artificial intelligence algorithms, which provide optimal solutions to difficult situations.
- Automating business processes with intelligent solutions reduces delays and improves customer service.
- Social analytics helps monitor brand image and solve problems early, which is very popular among customers.
3. From a security perspective, big data allows for the creation of detailed customer profiles, ensuring strong protection of their data, which is especially important for financial institutions. Artificial intelligence and big data are widely used to detect suspicious activity and prevent fraud and hacking attempts.
Areas of application of big data: How does personalization work in fintech?
Having become familiar with the capabilities of big data techniques, Sergey Kondratenko suggests considering how they work in various application areas.
- Online payments: In online payments, big data and analytics based on machine learning have long been used to ensure the security of services and detect fraud. A recent trend has been to combine payment processing with POS (point of sale) credit mechanisms, allowing customers to receive credits directly at checkout. These systems use machine learning algorithms and big data to instantly assess risk and available credit, reducing backlogs and increasing customer confidence. In the US, for example, SoFi.com is using these technologies to help young professionals manage their finances more efficiently and securely.
- Insurance: Modern insurance companies actively use big data and machine learning to create personalized, low-risk offers. An example of such innovation in car insurance is the Swedish company Greater Than. They use vast amounts of data and accident statistics, using machine learning to help insurance companies assess risks and set prices more accurately.
- Lending: Sergey Kondratenko says that big data and artificial intelligence models are actively used in microfinance and other forms of lending. This helps reduce credit assessment costs and makes loans accessible to people. Increased availability of instant loans also promotes economic growth and improves the performance of small and medium-sized enterprises.
- Real estate: Real estate fintech companies have two main strategies realized as either of the two below.
a. First, they strive to increase sales at higher margins. This is achieved through dynamic pricing, constant market monitoring, and the provision of detailed property information in listings, as well as minimizing the risk of non-payment. They actively collect data from various sources and analyze it to make the most suitable proposals.
b. The second aspect of the real estate business is customer retention. They deploy mobile IoT devices to leased properties and provide 24/7 infrastructure monitoring. Thanks to the constant flow of information into the back office, they can optimize proposals for the rental and maintenance of facilities, focusing on the needs and capabilities of a specific client.
5. Trading and investing: The use of real-time big data analysis and machine learning algorithms plays a key role in online trading platforms, says Sergey Kondratenko.
The expert gives the example of Goldman Sachs, a company that uses machine learning and natural language processing (NLP) algorithms. Thus, a quantitative analysis of investment candidates is carried out here, as well as an assessment of the media context and the tone of public opinion about them. This allows for more accurate and efficient investments to be achieved. Small companies can also use such mechanisms to monitor the brand and adjust customer-focused merchandising strategies.
Sergey Kondratenko: Big Data and quality of customer service
Having considered the main areas of financial technology and the big data analytics used in them, no one has any doubt that the main advantage of big data is a personalized approach. Sergey Kondratenko is also convinced of this; he believes that individualization of services is becoming a fundamental aspect of modern financial technologies. In his opinion, the process of achieving this goal for fintech enterprises includes several vital stages:
- Initial data collection and customer segmentation/profiling begin with accumulating comprehensive client information. The data is then analyzed and classified to identify different customer segments.
- Ensuring that data is available throughout the company – Ensuring that the entire organization has access to data in a consistent format, facilitating consistent analysis and decision-making.
- Creation of personalized products and services in different directions. Here, Sergey Kondratenko considers it necessary to highlight three main areas:
- Long-term personalization involves anticipating customer needs and creating relevant, personalized content. This content is delivered to customers through various communication channels.
- Real-time personalization. This includes providing customers with personalized recommendations in real-time—for example, recommendations for shopping or trading through mobile applications or browser extensions.
- Personalize communications using artificial intelligence, such as bots, for 24/7 customer support. Bots can access a customer’s previous contact history and profile, allowing them to provide personalized recommendations and solutions.
Thus, personalization of products and services with the help of big data in fintech becomes more accessible and targeted, says Sergey Kondratenko. According to the specialist, focusing on clients’ personal needs in a modern financial world with many opportunities is a promising path that companies should follow to achieve success.
Photo credit: The feature image is symbolic and has been done by Christopher Isak with Midjourney for TechAcute.
