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Virtual Conference
09:00
Supporting businesses in the aftermath of the coronavirus pandemic
As Europe starts to slowly emerge from the coronavirus pandemic, uncertainty remains for many businesses. The risk of insolvency among SMEs is high and there is a need for mitigating measures, including “bridge” support and debt/loan restructuring for a viable business. Stakeholders in the credit markets (lenders, credit reference agencies, borrowers) need to understand better the criteria and the tools applied to judge whether a business is viable (or not) and therefore whether it has (or has not) reasonable recovery prospects so that support can be deployed effectively. Here, role of Early Warning Mechanisms can play an important role.
Introductory remarks
Case Studies
Covid, the aftermath. Can covid become a catalyst towards change in economic and social issues?
The transformation of SME lending in a post-pandemic economy
Remarks by additional panelists
Panel discussion and Q&As
Closing remarks
11:00
Putting data to work to ensure responsible lending for consumers
Assessing creditworthiness before granting credit is a key component of responsible lending. In a digital, data-driven environment, the relevant factors taken into account for the assessment of creditworthiness are evolving. New data such as regular household bills or open banking transactions are way more easily and readily available. The ability to leverage that data has enabled the assessment of qualitative factors such as consumption behaviour and willingness to pay, which allows for greater, faster, and cheaper segmentation of borrower quality and ultimately lead to better and quicker credit decisions. The use of personal data raises however policy issues, including those related to data privacy, data protection and a potential loss of control and understanding of the dynamics underlying lending decisions.
Introductory remarks
Case Studies
Three key factors in creating a fair, safe and future-proofed credit market
Providing fully digital credit checks on consumers
Remarks by additional panelists
Panel discussion and Q&As
Closing remarks
12:30
Virtual Networking
09:00
Transitioning from Open Banking to Open Finance
Open banking was designed to increase innovation and competition in banking and payment services. Open banking uses include innovative alternatives to traditional credit scoring and affordability models. Open finance is an opportunity to build on the concept of open banking by extending open banking-like data sharing and third-party access to a wider range of financial sectors and products. More accurate creditworthiness assessments and increased access to credit by enabling third parties to review cash flow holistically and identify suitable credit products for businesses and consumers would be possible. Open finance would also create or increase risks and raise new questions of data ethics. The right commercial incentives for widespread open finance-type arrangements between firms would also have to emerge.
Introductory remarks
Case Studies
Open Finance: the end of traditional credit risk management?
Digital Scoring: A case study in Mobility Fintech
Remarks by additional panelists
Panel discussion and Q&As
Closing remarks
10:30
Virtual Networking
11:00
The power of AI in delivering better lending decisions for consumers
Advances in technology and software are enabling organisations to adopt more powerful and advanced analytical approaches to help drive growth, efficiency, competitiveness and manage risk. Take Machine Learning. Machine learning essentially means teaching computers to teach themselves by giving them access to lots and lots of data. Thanks to machine learning algorithms, an AI system will teach itself which data points are important, and which ones are not for a particular lending decision. With so much focus recently on getting analytics and AI to outperform traditional methods, there is now a need to expand and allow for all of that success to be explained. And, even, regulated?
Introductory remarks
Case Studies
NeuroDecision: fully explainable AI to boost financial inclusion
Customer retention using Next Generation AutoML
Remarks by additional panelists
Panel discussion and Q&As
Closing remarks
11:30
Virtual Networking
