CubX will change your factoring business.
- CubX is a solution that uses state-of-the-art machine learning algorithms to analyze data, including credit scores, financial statements, and customer payment history.
- CubX uses data from today and in near real time, allowing us to accurately predict the likelihood of late payments or even defaults.
- CubX is made to identify risks early and classify them based on the creditworthiness and customer behavior of debtors, clients and receivables. It detects and predicts chances and risks in the ecosystems of a factoring organization.
Benefits of CubX
in a nutshell:
Scalability
CubX is scalable and thanks to its state-of-the art machine learning technology, it adapts to your particular ecosystem in a short period of time.
Potential opportunities
CubX enables our clients to identify not only various risks but also potential opportunities across clients, debtors, industries, or regions, allowing them to do so at an early stage within the respective ecosystem of each factor. Utilizing the full potential of factoring data.
Timely warnings
With CubX you can receive timely warnings about possible payment delays.
Risk monitoring
CubX offers you an improved portfolio for risk monitoring.
How it came about
Lorem ipsum dolor sit amet, consectetur adipiscing elit—150 ad 200 million transaktiones et volumen 200 billionum euro. Sed quid agimus cum his datis ultra simpliciter processum? Quomodo ulterius analysare et illas exsequi possumus?
Ad explorandum hoc, consulebamus data scientistas nostros ad aestimandum potentiale datorum et determinandum si ad praevisionem et praenuntiationes adhiberi posset. Dati subscripti quos usi sumus comprehendebant clientis magistri data, debitoris magistri data, et facturae solutionis data, ex ef3 et efX derivata.
We implemented a comparison between two approaches:
- Lorem ipsum dolor sit amet, consectetur adipiscing elit, ubi tam periti internos quam clientium intellectus adhibebamus ad analysandum data.
- Metodum disciplinae machinae discendi basatum, ubi data in systema inputamus et eventus a modello generatos aestimavimus.
It became clear that the machine learning approach provided several advantages over traditional data processing techniques.
Inter hos, machinae discendi permisit nos celeriter vastas cantidades datorum processus facere et complexas formas et relationes agnoscere. Praeterea, et maxime momenti, nos permittit ut decisiones obiectivas et datorum-driven faciamus, potius quam iudicium humanum, quod saepe a strepitu et praeiudiciis afficitur. Hae sunt paucae ex multis commodis, quae methodus machinae discendi offert.
Haec commoda agnoscens, ulteriora applicationes methodi machinae discendi exploravimus ad analysandum data talia ut scores crediticiae, declarationes oeconomicae, et historiam solutionis clientium. Hoc adduxit ad evolutionem CubX, instrumentum designatum ad cognoscenda pericula et occasiones in primis.
How it works
Conventional vs. ML risk management
Without ML
- Without machine learning risk management is a manual, rule-based process, where risk is identified through predefined criteria or ratios. Professionals analyze data to assess risks and develop strategies accordingly.
- Financial statements, credit agency scores, payment behavior analysis, as well as industry and market trend assessments, are commonly used in this process.
- As the volume of data grows, the challenge of processing large datasets efficiently increases. This can lead to inaccuracies in the analysis.
- Additionally, this traditional approach often reacts to risks after they occur, rather than proactively predicting and preventing them.
Machine Learning
- Machine learning can analyze and process vast amounts of data at high speed.
- It allows the identification of complex patterns and relationships within data.
- With machine learning it is possible to adapt to quick market changes.
- Additionally, it enables automatization of repetitive or time-consuming tasks.
- By relying on data-driven algorithms rather than human judgment, machine learning can reduce personal biases and subjective decision-making, leading to more objective and consistent outcomes.
