AI and Machine Learning Are Revolutionizing the Leasing Sector

ARTICLE WRITTEN FOR THE WORLD LEASING YEARBOOK 2025

By Olfa Dridi, Business and Application Expert, CODIX

THE LIFE-CHANGING POWER of artificial intelligence and machine learning in the leasing and finance industries:

Artificial Intelligence (AI) and machine learning (ML) are transforming how today's businesses and industries operate. With their ability to analyze, process, and transform huge amount of data into knowledge and deliver real-time insights, AI and ML are empowering decision-making processes. These technologies are clearly redefining roles, reshaping best practices, and creating new opportunities for innovation across multiple sectors such as healthcare, education systems, the e-commerce industry, supply chain and transportation sectors, agriculture, as well as finance and banking [1].

Artificial intelligence and machine learning are often used interchangeably, but AI represents the capability of a computer system to mimic human cognitive functions such as problem-solving, decisions-making and learning. Whereas, ML is a subfield of AI that uses algorithms trained on data to produce adaptable models capable of performing various complex tasks.

ML methods: ML, as part of AI, offers powerful benefits for financial institutions. It falls into three main methods:

Supervised ML: Supervised learning (classification), is defined by its use of labeled datasets (observations, measurements, etc.) to train algorithms to classify data or predict outcomes accurately. Supervised learning helps institutions solve a variety of real-world problems. Some methods used in supervised learning include neural networks, naïve Bayes, linear regression, logistic regression, random forest, and support vector machines (SVM).
Unsupervised ML: Unsupervised learning (clustering), also known as unsupervised machine learning, uses machine learning algorithms to analyze and cluster unlabeled datasets. These algorithms can discover hidden patterns or data groupings without the need for human intervention.
Semi-supervised learning: Semi-supervised learning offers a middle ground between supervised and unsupervised learning. During training, it uses a smaller labeled dataset to guide classification and feature extraction from a larger, unlabeled dataset. Semi-supervised learning can address the problem of insufficient labeled data for a supervised learning algorithm.[2]

In a fast-evolving digital landscape, AI offers institutions new ways to innovate, reduce costs and increase customer satisfaction through ML algorithms. By embracing AI, organizations are better positioned to meet industry challenges and stay competitive in a dynamic and evolving market.

Especially in such a challenging and competitive environment where strategic decision-making are extremely complex and often require the consideration of several objectives while satisfying hard constraints, AI is becoming an indispensable tool for a large-scale companies and institutions to deliver sophisticated solutions and to find a match between the opportunities within the environment and the company’s strengths and weaknesses.

AI for leasing industry:

The potential of AI in the leasing industry cannot be underestimated. Leasing is a process that relies on a wide range of data such as credit histories, market trends, customer preferences, assets usage, leasing contracts, vendors, reports, debt collection, and shareholders, … Traditionally, exploiting and managing this data required significant human effort and was subject to errors and time delays. AI tools, however, have pioneered a new era of innovation by learning from the past to predict future trends and threats, due to their ability to process large-scale datasets with exceptional speed and accuracy.

According to a new study, the international financial sector may gain an extra $1 trillion each year by implementing and applying AI tools [3]. According to [4], the global financial services industry is projected to earn around USD 28.529 trillion from 2025 to 2030, at a compound annual growth rate (CAGR) of 6 %. The primary cause of this is the significant use of AI in the restructuring of banking operations, particularly after the COVID-19 recovery.

In this article, we address the important role of AI, and ML, as part of AI, in reshaping the financial world, with a particular focus on their impact on the leasing sector. We will examine how AI technologies are not only improving operational efficiency and decision-making processes, but also redefining customer experiences and risk management strategies in the leasing industry. We will explore the various ways AI and ML are transforming this sector in today's financial world.

Leasing industry challenges and opportunities with AI tools and ML algorithms:

The leasing industry faces a number of challenges today, for which artificial intelligence (AI) and machine learning algorithms offer suitable solutions [5].

RISK ASSESSMENT AND CREDIT MANAGEMENT

Leasing companies have faced challenges throughout the years for various reasons, one primary cause lies in risk assessment standards and policies [8]. The companies need to evaluate their lessees’ financial stability and situation in order to:

Determine the feasibility and advisability of granting them loans;
Identify the strengths of the applicant’s situation;
Identify weaknesses of the potential lessee;
Identifying the key factors essential to the lessee’s ongoing success;
Ensure long-term profitability.

Assessing the risks associated with leasing contracts is a critical and complex process based on gathering and analyzing a huge amount of financial data to draw up an accurate customer profile, focusing on reducing default rates and improving credit decisions [4].

As a result, and regarding today's leasing landscape, which is marked by increasing complexity and risks that require rigorous assessment of customer credibility as an essential guarantee for lessors, efficient information management enables sound decisions to be made, while poor information management can lead to inappropriate decisions. AI makes it possible by analyzing large volumes of financial, historical and behavioral data in real time, in order to accurately assess each customer's risk profile while maintaining a high level of accuracy and compliance.

Credit scoring is a highly effective application of AI in the Leasing sector; it utilizes advanced ML algorithms and data analytics techniques enabling enhanced precision and efficiency in credit assessments, scoring processes, proactive risk management strategies, minimizing delinquency impacts, and facilitating more informed decision-making [7].

The main steps of the AI-based credit scoring algorithms are:

The data collection and analysis: They can collect, process, and analyze large datasets, including financial statements, auditor reports, market data, etc., to offer accurate, efficient, and comprehensive insights into a lessee’s financial behavior.
The main elements of AI-based credit scoring algorithms are based on the use of ML models; these models are trained on large datasets to identify patterns and correlations that may indicate a lessee’s likelihood of repaying a loan. By analyzing huge amounts of data sets, machine learning algorithms can discover sophisticated relationships between seemingly unrelated factors and a lessee’s financial reliability.
Predictive analytics: the AI-based credit scoring algorithms ensure to identify patterns and make predictions with high accuracy. This enables a more personalized and accurate evaluation of the lessee’s creditworthiness.
Financial Analysis: This analysis assesses factors such as profitability, liquidity, leverage, cash flow, and overall financial health, helping to understand the lessee’s ability to meet its financial obligations.

TRANSFORMING CUSTOMER EXPERIENCE

In today’s customer-centric market, improving customer experience is crucial for leasing companies looking to maintain a competitive edge.

AI plays a significant role in personalizing interactions and providing tailored solutions for clients due to its ability to process unstructured data, make sense of customer feedback, and adjust interactions to optimize the customer experience. AI enables solutions such as behavioral data platforms, virtual assistants and chatbots to provide immediate insights.

AI-driven chatbots are becoming standard practice in the leasing sector, offering 24/7 support to customers. The chatbot tools are able to deliver efficient answers to frequently asked questions, guide clients through the application process, and provide instant feedback, making the leasing experience more transparent and efficient. AI can predict customer needs and behaviors, allowing leasing companies to proactively address issues and offer relevant solutions before customers even realize they need them, delivering personalized products that meet their preferences.

The use of AI offers new opportunities to improve customer satisfaction and loyalty by providing leasing companies with the flexibility to better respond to evolving and changing needs.

REGULATORY COMPLIANCE AND FRAUD DETECTION

In the digital age, cyber fraud has become commonplace. Leasing companies need to ensure transaction compliance and security. AI is also used to enhance security by detecting fraudulent activity. It can detect unusual trends or inconsistencies in data and alert managers for further investigation, thereby reducing fraud-related financial losses and limit the damage caused [9].

AI-driven tools for fraud detection can generate various models to enhance data understanding and fraud prevention:

Descriptive Model: Models that explain data by identifying patterns and relationships. For example, helping companies understand the characteristics of the underlying data.
Predictive Models: Models that predict the future and anticipate possible fraudulent actions. By analyzing historical data, predictive models can anticipate high-risk fraud situations, enabling proactive fraud prevention.
Summarization Models: Models that summarize data; these models can provide insights into frequent fraud patterns, types and impacts.
Feature Extraction Models: Models that extract the most important features from the data; these models can extract critical indicators of fraud. They improve fraud detection processes by identifying and ranking the data characteristics most strongly associated with fraudulent actions and by determining the causes of fraud among internal and external factors.

These AI models enhance the effectiveness of fraud detection systems, making them more efficient, predictive, and adjustable.

CONCLUSION

AI and ML play a crucial role in the evolution of the leasing industry. By integrating AI tools and applying ML algorithms, leasing companies can not only improve their operational efficiency, but also improve the accuracy of their risk assessments, automate complex processes, and better manage fraud. These technologies enable leasing companies to optimize their decisions-making process, while minimizing losses and increasing customer satisfaction. In an increasingly competitive market, AI and ML offer an undeniable strategic advantage, making the leasing sector more innovative and forward-looking. Companies that embrace AI tools and ML strategies today will be better equipped to address the demands of tomorrows.

References:

1. How AI is changing the leasing experience
2. Machine learning
3. ScienceDirect: An effective role of artificial intelligence and machine learning in banking sector, Journal of Measurement: Sensors, June 2024
4. Benefits of artificial intelligence in the banking sector
5. Sizing the sector in the global economy
6. Leasing and finance companies: the TOP 8 challenges
7. Trends, challenges and opportunities in a new age of leasing
8. The future of credit risk assessment
9. Top 10 fraud risk trends to watch out for in 2023

This article was published in the World Leasing Yearbook 2025.