The data problem nobody mentions – and why it is holding back the adoption of AI in the leasing sector

Part of an online series by Basikon and LECTURA on data quality, automation and AI in the leasing sector – next session: late 2026

Every week, leasing and credit teams across Europe lose hours to the same invisible problem: asset data that is not suitable for what the business needs it to do. Automation, AI-supported decision-making and regulatory reporting requirements all place the same fundamental demand on financial institutions: robust, structured asset data. In practice, precisely this foundation often remains incomplete with direct consequences for process efficiency, valuation quality and compliance. 

The issue that we are facing

Incomplete asset descriptions, missing manufacturer mappings and inconsistent categorisations are not the exception in leasing and credit environments; they are everyday reality. One frequently underestimated element: closed data fields. Unlike free-text fields, they enforce standardised inputs: controlled value lists, validated model descriptions, consistent categories. Only then do data become machine-comparable, aggregatable and automatable. 

The scale of the problem is bigger than most teams realise. According to Gartner, poor data quality costs organisations an average of 9.7 million dollars per year. Employees spend up to 27% of their time correcting faulty data: Time that is then unavailable for credit decisions, customer relationships and portfolio management. 

“Most companies underestimate how much of their data lies dormant, and is therefore simply unusable for AI.” Karl Devos, SaaS expert, Basikon 

This is what it looks like in practice: every open field in the asset capture process is a point at which a person has made a discretionary decision, and a point at which a machine will later struggle. Multiply that across tens of thousands of leasing applications, and you create a data foundation that quietly undermines every efficiency initiative built upon it. 

What structured data actually enables

LECTURA provides the structured foundation: over 200,000 machine models, standardised, categorised and directly integrable via API. Once the data foundation is in place, concrete areas of application open up: residual value trajectories become granular and model-specific, portfolio backtesting (the comparison of actual figures with residual value forecasts) becomes scalable, and ESG carbon emission reports in accordance with GHG Scope III and PCAF can be generated automatically, without any additional manual effort.

These are not future scenarios. They are available today for institutions whose data are ready. According to the Cambridge Centre for Alternative Finance Report 2026, 49% of traditional financial institutions cite data quality as their biggest obstacle to AI adoption. Most institutions are ready for automation. Their data are not. 

The second factor is the software that processes this data. A 100% API-first platform retrieves asset data in real time, feeds it into leasing applications and uses it automatically for valuation and credit decisions. Basikon integrates the LECTURA asset database directly into front- and middle-office processes, from automatic asset enrichment to AI-supported balance sheet analysis. The result: less manual effort, reduced human error and a robust foundation for regulatory requirements. 

“Clean data is not an IT issue – it is a business decision. Once you understand that, you quickly realise that the software is the easier part.” Martin Feith, Country Manager DACH, Basikon 

On 28 April 2026, Basikon and LECTURA hosted a joint webinar on this topic, including a live demo and practical examples. For those who missed it: the next session in the series will follow at the end of 2026. To register, contact Basikon or LECTURA directly. 

About Basikon – Your tech. Your rules. 
Basikon was born from two simple questions: Why does implementing a credit platform still take years? And why does changing a rule require a developer, a ticket and three weeks? In 2019, four software experts decided to stop asking, and instead build the next generation of core lending technology. Low-code, 100% API-first, modular and flexible, with a dynamic database and a graphical workflow engine that puts configuration back into the hands of the business, not into the IT queue. Six years later, more than 35 financial institutions across Europe and Africa are on-board, with an average implementation time of just a few months. Not years. 

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