According to a recent fintech survey by Banca d’Italia, a significant share of digital transformation projects among financial institutions focuses on AI, with investments reaching €593 million in 2025.
Within this landscape, a substantial portion of resources is clearly being allocated to the renewal of credit management processes. Global data confirm this trend, showing that the smart lending platform market is expected to grow at a compound annual growth rate of 23.5% over the 2025–2035 decade.
In this article, we examine how smart lending is taking shape in the banking sector, explore the role of AI in redesigning credit assessment, disbursement, and monitoring phases, and outline the main technological drivers enabling faster, more data-driven lending models.
Key Points
- Banks are making significant investments in the digitalization of credit management processes to integrate AI to make them faster, safer, and more sustainable.
- One rapidly expanding area is smart lending, a model that digitalizes and orchestrates credit assessment, disbursement, and monitoring.
- Banks can achieve faster credit processes, more accurate decisions, improved risk management, and a high-quality customer experience.
- Artificial intelligence enables adaptive risk models, enhances the use of structured and unstructured data, and increases decision-making speed and consistency.
Why credit processes need to be renewed
Traditionally, lending decisions have relied on historical and structured information such as financial statements, credit bureau reports, and collateral, analyzed through well-established models and supplemented by qualitative assessments based on the experience of credit officers.
While this framework has ensured stability over time, it also shows significant rigidity: data are static, not timely, and spread across multiple systems; underwriting, approval, and disbursement phases are overloaded with manual steps that slow down the entire process; and risk monitoring is largely performed ex post. This results in several structural limitations, including:
- Long underwriting and response times that are incompatible with the expectations of businesses and customers accustomed to immediate digital services;
- Poorly scalable processes, where higher volumes lead to increased costs;
- Risk assessments based on partial information, with a degree of uncertainty;
- A low-quality customer experience.
Smart lending: what it is, how it works, and why it is changing credit
To address these challenges and balance cost reduction with an improved customer experience, the concept of smart lending has emerged.
What is Smart Lending
Smart lending is an advanced credit management model in which assessment, disbursement, and monitoring processes are digitalized and orchestrated end-to-end, making extensive use of advanced analytics and AI. Smart lending goes beyond simply shifting access to credit to digital channels; it intervenes at the core of the decision-making process:
- How data is collected;
- How they are analyzed;
- How decisions are made;
- How decisions are updated over time.
How smart lending works: the three pillars
Smart lending redesigns the credit process, transforming it from a linear sequence of activities into a continuous, data-driven, and orchestrated flow. The starting point remains the loan application, but the way information is collected and used changes radically.
- In the initial phases, the digital platform performs a structured and automated data collection, including traditional documentation as well as up-to-date external sources. These data are normalized and made immediately available to risk assessment models.
- AI- and machine learning-based credit scoring and pre-assessment models then estimate the risk profile and sustainability of the loan. Standardizable, low-risk decisions are handled automatically, while more complex or borderline cases are routed to analysts, who intervene with advanced decision support.
- In parallel, workflow automation manages operational activities such as onboarding, document management, signatures, and compliance checks, ensuring consistency, traceability, and error reduction.
The smart logic extends beyond disbursement. Once credit is granted, systems continue to monitor the position, activating early warning mechanisms in the event of significant changes in the risk profile.
What data smart lending is based on
One of the elements that distinguishes smart lending from traditional models is the breadth and variety of information used to support decisions.
Internal bank data remains central, including customer profiles, relationship history, financial statements, cash flows, and outstanding exposures. These are complemented by external data that help quickly identify relevant signals, such as public and open data sources, sector-specific information, and digital traces like social platform ratings, reviews, and online reputation.
Main smart lending use cases
Smart lending applies wherever decision speed, risk definition, and a seamless customer experience are critical factors.
- Digital loans and fast disbursement financing;
- Credit for SMEs and professionals;
- Pre-scoring and preliminary assessments that provide early-stage responses;
- Monitoring of existing credit;
- Optimization of lending processes through standardization and automation.
Taken together, these use cases show that smart lending is not a vertical solution, but a cross-functional enabler that improves credit performance across the entire loan lifecycle.
The role of artificial intelligence in smart lending
Artificial intelligence does more than accelerate existing processes; it introduces new risk assessment models that are difficult to achieve with traditional tools.
Machine learning and adaptive risk models
The main contribution of machine learning lies in its ability to build risk models that learn over time. Unlike static scores trained on historical datasets and updated at fixed intervals, ML models continuously recalibrate based on new data and observed behaviors. This makes it possible to detect emerging patterns—such as changes in cash flows, shifts in seasonality, or anomalies in payment behavior—that traditional models may miss.
Analysis of unstructured data and sentiment analysis
Another distinguishing factor is AI’s ability to extract value from unstructured data such as texts, documents, communications, qualitative information, and digital content.
This includes sentiment analysis, which transforms qualitative signals—such as online reviews and public feedback—into variables interpretable by risk models. While not decisive on their own, these insights enrich the applicant’s overall profile, especially in contexts where financial information is limited or outdated.
AI as a tool for consistency
Beyond accuracy, AI addresses another critical aspect of lending: decision consistency. In highly manual processes, similar cases can lead to different outcomes depending on context or the operator involved. AI models apply uniform criteria at scale, reducing variability and decision asymmetries while preserving human judgment, especially in complex cases.
From prediction to action: process automation
The value of AI becomes evident when predictive capabilities are translated into automated operational actions. Machine learning models feed decision engines that determine loan outcomes in real time, define risk-consistent conditions, or establish the required level of due diligence.
Standardizable, low-risk cases are handled automatically, while more complex situations trigger controlled, automated escalations to operators, who work with already structured information. The same logic also applies to the post-disbursement phase.
Smart lending solutions: a high-level architecture
A smart lending solution is designed to support the entire credit lifecycle, not to replace existing systems but to augment them with automation and advanced decision-making capabilities within a human-in-the-loop approach.
Data acquisition and integration
A key architectural component involves data collection, normalization, and overall data management. Smart lending solutions aggregate information from internal and external sources, making it promptly available for assessment processes.
AI/ML engine for analysis and decisions
The analysis and decision engine typically combines traditional risk models with machine learning algorithms. This is where scores, risk estimates, and sustainability assessments are calculated, applying business rules and credit policies.
Process orchestration
Workflow orchestration governs underwriting, approval, contracting, and disbursement. Platforms can integrate digital onboarding, document management, electronic signatures, and compliance checks.
Integration with core banking and open banking
Smart lending solutions securely interact with core banking systems and leverage API-based integration to connect with open banking services and external data sources.
Security, compliance, and governance
Across all layers, security and compliance controls (such as access management, decision traceability, data protection, and AI model governance) are essential to ensure that lending innovation remains sustainable and aligned with the stringent control requirements of the banking sector.
Kirey’s approach to smart lending
Designing and implementing a smart lending solution is a highly complex undertaking. On one hand, decision system outputs must be reliable and robust to support critical choices such as credit approval; on the other, introducing these models requires a transformation of processes and operating methods, with a direct impact on organizational structures and credit governance. Moreover, smart lending operates in one of the most heavily regulated sectors, where transparency and regulatory compliance are non-negotiable.
At Kirey, we have been supporting leading financial market players for years in transformation journeys that go beyond operational efficiency, aiming to build structural innovation capabilities. Smart lending is one of the areas through which we pursue this path, to make institutions more modern and aligned with the expectations of customers who are increasingly focused on speed and experience quality.
Contact us to find out how we can embark on this journey together.
