Fintech Data Infrastructure Requirements in the Era of AI and Clouds

Stanislav, given your many years of experience in developing platforms for mission-critical applications, why do you think AI has become so important for fintech today – an industry where speed, reliability and data security are of paramount importance?

I believe that the role of artificial intelligence in modern fintech is determined by several factors. First of all, it is a competitive advantage.  Fintech companies that actively implement AI are able to automate routine processes and personalize services for each client. This creates a unique user experience, allowing them to stand out from competitors in a saturated market.

Second, the impact of rising user expectations cannot be underestimated. Today’s customers are accustomed to instant access to information and high quality service in real time. It is becoming virtually impossible to meet these requirements without the use of AI technologies. AI ensures prompt resolution of issues, automated support and flexible adaptation to individual needs.

Finally, a critical factor is the need to process large volumes of data. Modern fintech generates huge volumes of transactional data that require real-time analysis to detect fraudulent transactions, assess risks and make informed decisions. AI, with its ability to process and analyze large amounts of information, is becoming a critical tool for effective management and strategic decision-making in this area. Without AI, fintech simply will not be able to scale and function effectively in the modern world.

How do you see the opportunities presented by AI changing the fintech landscape?

AI provides many opportunities for financial organizations. It is difficult to list them all. But I will talk about a few notable examples.

First is the automation of credit decision making. AI allows for comprehensive real-time analysis of data about a potential borrower, including their credit history, transactional activity, and even social media. This significantly speeds up the scoring process and allows for more informed decisions on loan issuance, reducing risks for financial organizations and increasing customers’ access to financing. For example, I worked on a project where we helped a large bank to dynamically recommend the best loan options based on profitability and risk. I designed a system that used GridGain Platform’s distributed computing capabilities to dynamically invoke AI models to estimate the risk and profitability for different combinations of loan parameters like amount, length, and APR. That system was capable of processing thousands of loan options in seconds and suggesting the best one to the customer in real time.

Second, fraud detection is becoming increasingly effective thanks to AI. Behavioral pattern analysis and anomaly detection can prevent fraudulent transactions before they occur. AI can detect unusual transactions, suspicious account activity and other signs of fraudulent activity, ensuring the security of financial transactions. I worked on multiple systems like this for top banks worldwide. The main challenge in these projects was keeping the processing very fast, often within tens of milliseconds, while allowing it to process vast data volumes.

Third, there are many opportunities in the realm of investment banking such as various analytics supporting investment decisions. Access to data with better speed and better quality can be an enormous advantage for investors. AI helps to extract valuable insights and automate routine operations. I helped to implement several systems like this by using GridGain to run AI models and analytics on large data sets. This required not just implementing the data storage but also ensuring that the data is constantly updated with the newest market information.

To realize the full potential of AI, you need the right technological foundation. GridGain Platform enables AI applications with its speed and scalability for processing huge amounts of data in real time – capabilities that are essential for fraud detection, risk assessment, and offer personalization in financial services. The platform’s distributed architecture and high availability features help banks handle mission-critical AI operations.

When we worked on GridGain 9, my focus as the product owner was to make sure we improved the architecture to ensure even better scalability, especially in cloud environments where many of our fintech clients are moving. We also added new foundational capabilities such as columnar storage that enable real-time analytics and AI on a completely new level. These enhancements came directly from what I was seeing in the field – banks needed better cloud performance and more sophisticated analytics capabilities to support their AI initiatives.

Given the evolution of cloud technology and its growing role in data infrastructure, how do you see the shift to cloud solutions impacting fintech architecture and security requirements? 

In my opinion, cloud technologies have a fundamental impact on the development of data infrastructure in financial technologies, providing new opportunities while simultaneously posing significant challenges for companies.

I would like to note that cloud providers offer out-of-the-box AI/ML services. This means that fintech companies no longer need to spend huge resources on developing their own machine learning solutions from scratch. They can use off-the-shelf tools to build and train models, analyze data, and automate processes, which significantly accelerates time-to-market and reduces costs.

In addition, cloud infrastructure makes global scaling much easier and more affordable. Fintech products can operate internationally, reaching new markets and audiences without the need to invest heavily in building their own infrastructure in different regions. The cloud provides flexibility and scalability, allowing companies to quickly adapt to changing business needs.

Cloud platforms provide powerful tools for integrating heterogeneous data sources into a single ecosystem. This allows fintech companies to combine data from various sources such as banking systems, payment gateways, CRM and social media to gain a more complete and comprehensive view of customers and transactions.

However, moving to the cloud also comes with increased security requirements. Fintech companies must rethink their approach to data protection and comply with stringent regulatory requirements such as GDPR and PCI DSS. Robust encryption, access control and security monitoring mechanisms must be implemented to protect sensitive information and prevent data leakage. Overall, the cloud presents a tremendous opportunity for data infrastructure development in fintech, but requires careful attention to security and compliance issues.

To maintain the balance between the scalability and convenience of the cloud and security of the traditional private data centers, data infrastructure needs to be very flexible in the way it can be deployed. One of the products I launched at GridGain is GridGain Nebula – a Platform-as-a-Service product that allows running GridGain Platform on the public cloud infrastructure. To ensure security there, we implemented features such as support for private cloud networks and transparent data encryption. At the same time, when we developed GridGain 9, we ensured that it is equally easy to run on both public and private infrastructure.

Given the growing adoption of AI and cloud technologies in fintech, what architectural changes and technology requirements do you see becoming critical for today’s data infrastructure, and how does GridGain 9 help organizations meet these requirements?

The adoption of AI and cloud technologies is fundamentally changing the requirements for modern data infrastructure in fintech, making speed, scalability, flexibility and security critical.

In modern fintech, decision-making must be instantaneous, whether it’s preventing a fraudulent transaction or offering a personalized product. This requires an infrastructure that can process data in real time and perform analytics on the fly, without delay.

Elastic scalability is important. The system must automatically scale to meet peak loads, such as during major sell-offs or periods of increased activity in financial markets, without any performance degradation. This requires an architecture that can flexibly adapt to changing business needs and keep mission-critical applications running uninterrupted.

A modern infrastructure must provide flexible integration between different data stores and sources. Data from different systems such as banking databases, payment gateways, CRM systems and social media must be combined to provide a complete and comprehensive picture of customers and transactions.

High fault tolerance is absolutely essential for mission-critical financial applications. The infrastructure must be designed to provide automatic disaster recovery and guarantee continuity of operation even if individual components fail.

In addition, support for vector databases is becoming increasingly important for AI-embeddings and semantic search. AI models such as neural networks generate vector representations of data that need to be stored and processed efficiently. Vector databases provide specialized solutions for storing and searching this format.

Finally, the infrastructure must meet the stringent security requirements of the financial industry. Data must be protected from unauthorized access, leaks and cyber threats, and it must comply with regulatory standards such as GDPR and PCI DSS.

Importantly, GridGain 9 combines all of these capabilities into a single platform, delivering less than a millisecond latency and horizontal scalability for mission-critical fintech applications. This allows companies to create modern and reliable financial services that meet the highest requirements.

Stanislav, based on your experience, what do you see as the most significant obstacles to successful integration of AI into the fintech environment and what strategies based on the use of platforms such as GridGain 9 can help organizations overcome these challenges?

In my opinion, despite the huge potential, integrating AI into fintech comes with a number of major obstacles that require careful and strategic consideration.

Integration with legacy systems is a major challenge. Many financial organizations still use legacy systems that are difficult to integrate with modern AI solutions. This slows down the AI adoption process and requires significant effort and cost to modernize the infrastructure. In my time with GridGain, I was in charge of many projects aimed at modernizing legacy systems. I integrated GridGain with legacy relational databases, data lakes, and even mainframes. For mainframes, I helped launch GridGain for z/OS – a solution that allows to bridge the gap between modern in-memory and distributed systems and the tried-and-proven mainframe installations without a rip-and-replace application rewriting. It was an interesting challenge adapting GridGain Platform for the z/OS use and ensuring performance and stability there.

Quality and accessibility of the data is another problem. On one hand, the available data may be insufficient or not immediately usable by AI due to poor quality, changing formats, and other challenges. On the other hand, accumulating data and making it accessible faces increasing scrutiny from regulatory constraints from policies such as GDPR to the new policies being developed that target the use of data with language models.

The rising costs of AI solutions and supporting infrastructure are putting financial pressure on companies. The cost of developing, training and deploying AI models, as well as the cost of cloud computing and data storage, can be significant.

The speed of change in AI requires constant updating of infrastructure and competencies. The rapid evolution of technology and the emergence of new machine learning algorithms and methods create the need for continuous learning and adaptation to changing conditions. This also leads to a workforce problem – there is a shortage of specialists skilled in the key technologies in this environment. I can tell this from personal experience too after driving GridGain’s AI strategy and research for years now – keeping up with that pace isn’t easy.