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How to maximise the potential of AI in leasing

  • 28.06.2024
  • 6 min

The leasing industry is significantly transforming, especially with the integration of artificial intelligence (AI). This technological shift guarantees the streamline of various aspects of leasing, from customer experience to risk management. In this article, we delve into the benefits and challenges of implementing AI in the leasing sector, focusing on the implications of the AI Act on this transition.

The role of AI in leasing 

AI in leasing refers to using artificial intelligence in the sector to automate tasks, streamline processes and improve customer service. With AI leasing assistants, agents can focus on more strategic tasks, increasing efficiency and providing a more personalised experience for renters.

However, several obstacles need to be overcome before the adoption of AI leasing technology can be fully realsed. These include privacy and security concerns, seamless integration with existing systems, and managing data quality.

As companies strategise to integrate AI leasing into their operations, they must also monitor outcomes and follow necessary shifts to maintain a competitive edge in the market.

Benefits of implementing AI in the leasing sector

Let's examine some of the ways AI is helping develop advanced lease administration software.  

Enhanced customer experience

AI has the potential to significantly improve customer interactions in the leasing industry. One notable application is the use of chatbots, which can handle customer inquiries efficiently and provide personalized leasing options based on customer data. This not only enhances customer satisfaction but also allows agents to focus on more complex tasks.

 72% of consumers are willing to pay extra for a leasing contract that provides customization options.

Source: tchek

Automated application processing

Another key benefit of AI is the automation of application processing. AI algorithms can quickly and accurately process large volumes of leasing applications, reducing the time and effort required for manual processing. This leads to faster decision-making and improves overall operational efficiency.

Better risk management

AI is also a powerful tool for managing risk in the leasing industry. Advanced algorithms can detect and prevent fraud by analyzing patterns and anomalies in data. Additionally, AI can assess credit risk more accurately, ensuring leasing companies make informed decisions about to whom they extend credit and minimize potential losses.

Decreased operation costs

Finally, automating operations through AI leads to significant cost savings. By reducing manual intervention in routine tasks, companies can lower administrative costs and optimize resource allocation. This allows leasing companies to operate more efficiently and focus on strategic growth.

Challenges of AI in the leasing sector

Implementing AI in the leasing industry presents several challenges, ranging from technical to regulatory and ethical issues. Here are some of the main challenges.

Challenges of utilizing AI in leasing
Compliance with privacy regulations
Ensuring data security
High implementation costs
Concerns about employment stability

Compliance with regulations such as the EU's AI Act

The European Union's AI Act establishes a comprehensive framework to regulate the development and use of artificial intelligence, particularly in sectors like financial services. 

The Act categorises AI systems into four risk levels:

  • Unacceptable risk systems (e.g., predictive policing, biometric identification) are banned.
  • High-risk systems (e.g., AI-powered credit assessments) must follow strict rules on risk management, data quality, transparency, and cybersecurity and require registration with an EU database.
  • Limited-risk systems (e.g., chatbots) must comply with transparency obligations like labelling AI-generated content.
  • Low or minimal-risk systems are encouraged but not mandated to follow a code of conduct.

Financial institutions must interpret these guidelines carefully to remain compliant while leveraging AI technologies. Companies must develop a compliance framework, inventory and classify AI assets by risk levels, and form a cross-functional team to manage AI risk and compliance.

Ensuring data security

The integration of AI in leasing necessitates robust data security measures. Protecting sensitive customer data from breaches and unauthorised access is paramount. Companies must invest in advanced security protocols to safeguard their data and maintain customer trust.

Higher implementation costs

The initial investment required for AI implementation can be substantial. This includes costs associated with acquiring technology, training staff, and maintaining AI systems. Leasing companies must weigh these costs against the potential long-term benefits to determine the viability of such investments.

Concerns about employment stability

The adoption of AI raises concerns about its impact on employment within the leasing industry. As AI automates more tasks, job displacement is potentially risky. Companies must address these concerns by reskilling employees and creating new roles that complement AI technologies.

Examples of adopting AI in leasing

Adopting AI in the leasing industry has led to innovative solutions and significant improvements in efficiency, decision-making, and customer experience. Here are two real-life examples of how AI is being utilised in leasing.

VeloBank

VeloBank, a leading player in the leasing industry, has been a pioneer in AI adoption. The company has harnessed the power of AI, specifically using natural language processing and machine learning algorithms, to tackle operational challenges, such as processing customer complaints and responding to credit inquiries. Its AI-powered chatbot for credit analysts is a prime example, providing real-time answers to questions about mortgage loans, thereby boosting efficiency and accuracy. Also, VeloBank is using AI, particularly computer vision and text analytics, to verify the environmental friendliness of products by reading and analysing unstructured product descriptions.

Citi Handlowy

Citi Handlowy is actively exploring innovative AI applications, such as "pay how you drive" and "pay how you live'' leasing models. These models require large, well-organized data sets, which the bank is currently working on. AI is primarily used for back-office processes, employee support, and document data retrieval. Despite regulatory challenges, such as GDPR compliance and the need for extensive documentation, Citi Handlowy is committed to implementing AI safely and sustainably, underlining its potential to enhance security and operational efficiency.

By the end of 2024, we can expect to see generative AI playing a significant role in how business is conducted in the leasing industry. However, it will also be critical for AI and human interaction to integrate responsibly, with human expertise guiding and regulating AI operations

Kim YoungVP at Fogelman Properties

TUATARA supports the use of AI in leasing

AI is set to transform the industry by enhancing customer experience, automating processes, managing risks and reducing costs. However, the journey is challenging, particularly regarding regulatory compliance, data security, and implementation costs. By carefully navigating these challenges, leasing companies can harness the full potential of AI, driving innovation and growth in the sector. 

Fintin is the pioneer of utilising the power of AI in leasing through personalised offerings and first-class customer experience. We implement modern technologies that can define the future of the industry. Contact us for more information about our approach.

Also, learn about our experience of using innovations in the leasing industry by watching the below materials (in Polish):


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