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Instantor Releases Report On How Machine Learning Is Revolutionising Credit Risk Management In Europe

Machine learning is a game changer for the financing industry in Europe

70% of companies surveyed said they’re working with machine learning to increase loan acceptance while reducing risk

44% of those surveyed who have not implemented ML, said it has to do with a lack of understanding on how it could impact operations

Instantor, the 3rd. fastest growing Swedish FinTech who makes tough calls easy within credit risk management presents “Credit Risk Management 2019 – How Do You Stack Up?”, a report based on a survey conducted by Instantor across Europe among top executives within leading financial organisations. The report reveals that two-thirds of these players are well underway to implementing machine learning (ML) and the majority benefits from its implementation within credit risk management.

A greater number of strict regulations have rapidly evolved in Europe in the last decade to protect the economy and the end-consumer. Consumers´ demands for fully digitised services, mobile-friendly interfaces and more convenient experiences have risen. Simultaneously, internal pressures to meet business targets while diminishing risk and compliance costs have increased too. These factors have urged financial institutions to come up with more efficient and innovative tools such as ML.

As part of the report, Instantor identifies other changes that financing organisations need to make to adapt to the current landscape: a shift to fully digitised and automated solutions; implement advanced analytics; partner with FinTechs; handle customer´s information with care to comply with regulations such as GDPR, and innovate services around data. Instantor recognises the opportunities being created by PSD2 that make it possible for financial institutions to gain new customers and tap into new data sources, such as transactional data.

“Credit Risk Management 2019 – How Do You Stack Up?” highlights significant benefits that can be drawn upon with the implementation of ML within financing, such as a boost in the bottom line and higher consumer satisfaction and retention. Instantor has identified an increase in the predictive power of the scoring models, a faster and more accurate loan acceptance process, and streamlined management as additional benefits that financing organisations can reap from ML implementation while gaining a definitive competitive advantage to stack up to competition.

The report covers the challenges in regards to ML implementation  – finding that reasons for not implementing ML techniques are related to a lack of understanding and its potential impact on operations (44%), and not knowing how it could affect the company’s performance (22%). “With the release of this report, Instantor begins a series of seminars and webinars aiming to transfer knowledge to credit risk professionals as part of our mission to democratise the financing system,” comments Raiha Buchanan, CMO at Instantor. 

“We are committed to empowering organisations with the technology and the knowledge they need to position themselves ahead of the curve. By better understanding the remarkable impact of ML utilisation for credit risk assessment, organisations will be able to take more at less risk.”, concludes Buchanan.


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