WHITEPAPER

    UNLOCKING THE POTENTIAL OF AN EARLY WARNING SYSTEM IN BANKING USING MACHINE LEARNING

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    Gain a deeper understanding of how an EWS brings substantial value for banking & what is the role of Machine Learning in the credit management industry!

    Executive summary

    In May 2020, the European Banking Authority (EBA) circulated its final guidelines on loan origination and monitoring that went into effect on 30 June 2021. Applying to all credit institutions in Europe, the guidelines state that lenders must implement Early Warning Systems (EWS) for the effective management of their portfolios.

    While credit risk monitoring has always been a hard mountain to climb for financial institutions, the pandemic crisis has increased exposures. In that context, creditors globally realise that detecting risk early on will enable them to preempt defaults in advance, and eventually reduce losses.

    Deploying an Analytics-driven Early Warning Mechanism and leveraging the power of Machine Learning algorithms can drive tangible benefits for Banks.

    By reading this report, you can:

    • Gain a deeper insight on what creditors should have in mind during the implementation of the new European Banking Authority (EBA) Guidelines about Early Warning Systems (EWS)
    • Find out more about the role of Machine Learning and Artificial Intelligence in the credit management industry and what challenges financial institutions need to tackle so that they can drive significant results
    • Understand the key aspects of an EWS and why process, technology and solution architecture need to be masterfully combined
    • Discover a complete overview of how an EWS actually brings substantial value for financial organisations that harness its power

    Fill in the form and get the paper