Smart Scoring: How AI Is Reshaping Lending
The adoption of AI solutions enables banks to optimize resources, speed up data processing, and automate routine tasks. At the same time, the core objective remains the same: banks aim to maintain customer loyalty and deliver personalized financial products.
As technology advances, many processes in the financial sector have become automated. Neural networks can now handle complex tasks and analyze vast volumes of data. AI models have also become a key tool for making more informed lending decisions.
From Automation to Customer-Centric Banking
Artificial intelligence is steadily expanding into multiple areas of everyday life. The greatest gains are seen in sectors with high volumes of routine tasks, strong analytical demands, and a need for personalized customer engagement. Banking is one such sector, where AI is widely used in credit scoring.
According to the Belarusian outlet Smartpress, more than half of financial institutions worldwide are already using fintech solutions in their operations. The banking sector has become a major buyer of AI technologies across customer service, fraud prevention, risk assessment, and analytics. Credit scoring, in particular, has been automated for years, with most lending decisions now made without human involvement.
The Bank of Russia reports that demand for domestic AI models, platforms, and tools in the financial market has grown significantly in recent years. In its 2025 report, the regulator also links the future development of AI to issues of ethics, trust, data sharing, and regulation.
The use of AI and machine learning systems not only automates processes and improves forecasting accuracy, but also reduces financial risks and operating costs. Customers gain access to more services without visiting a bank branch and receive personalized offers in apps based on their behavior and preferences, effectively becoming co-creators of their own financial decisions.
Meanwhile, AI adoption has increased productivity in front- and middle-office functions and accelerated the development of a domestic AI and fintech stack. In particular, there is growing demand for import-independent enterprise AI platforms.
Banks are shifting from offering standard products to asking what customers actually need and assembling tailored solutions. This reflects a broader global trend toward the so-called AI-native bank, where technology fundamentally reshapes the banking model.
Trust and Security Challenges
AI is expanding beyond credit scoring into the entire lending lifecycle. Instead of relying only on traditional financial indicators, models can incorporate additional data sources to assess customer preferences and creditworthiness. AI can also support document collection, pre-fill applications, and evaluate default risk, aligning with the risk-based approach emphasized by the regulator.
For countries in the CIS, EAEU, and parts of Asia and the Middle East, Russian AI solutions for lending operations may offer practical value. These include underwriting automation, anti-fraud systems, model validation tools, and secure data platforms.
But trust in AI-driven lending remains a key concern. Strict compliance with legal and data security requirements is essential. Clear audit mechanisms and well-defined human accountability are necessary to support adoption.
Financial Transformation
The next phase of AI development in finance is expected to center on the broader adoption of generative AI, including large language models. In 2023, the Bank of Russia identified neural networks and related models as a core component of the future financial system and supported the creation of conditions to accelerate AI development.
This includes fostering AI adoption in financial markets while maintaining a risk-based regulatory framework.
In 2025, Sber reported that it had moved to large-scale AI deployment in lending: for retail customers, AI models now handle 100% of operations, while for medium and large businesses the figure has reached 90%.
Meanwhile, the European Commission and the European Banking Authority have placed AI in finance under a separate regulatory focus. Its use in credit assessment is currently classified as high risk, requiring stricter standards for transparency and governance, particularly in credit scoring and fraud prevention.
Today, the main risks associated with AI in lending relate to data privacy, data quality, and security. Banks must deliver fast and convenient services without undermining customer trust.
Toward a More Mature Model
AI in banking is primarily an advanced data analysis tool for credit scoring that enables faster and more accessible lending. Competition will increasingly center on trust in these new technological models. Customers need to understand how decisions are made and be confident that their interests are protected.
Banks that successfully integrate AI into their architecture while ensuring transparency and explainability in credit decisions will gain a competitive edge and strengthen customer trust. An improved lending process is expected to better meet customer needs and address evolving challenges in financial markets.
