What stops business lenders from making the right decisions? Is there bias – in bankers’ minds or in the decision algorithms they use?
With the emergence of new types of digital data, SME lenders are looking to take advantage of new data-driven lending models. In our latest online discussion, we shine a light on the psychological and behavioural biases impacting decision models today and how removing them could open up new revenue streams, all the while delivering better risk management based on real-time data.
Martin McCann, CEO and co-founder of Trade Ledger, sat down with Tobias Baer, an independent senior advisor to financial institutions and fintechs and a scholar cooperating with the University of Cambridge, to explore the topic further.
By watching this discussion, you’ll gain a clear understanding of:
- what types of data are most useful when making lending decisions
- the £5.4 trillion SME lending missed opportunity and how data-driven lending can overcome it
- what algorithmic bias is and how to avoid it
- what the business lender of the future will look like
SME lending: an untapped opportunity
Historically, small and medium-sized enterprises are the backbone of the global economy, providing the lion’s share of employment, and driving growth and productivity. However, they struggle to raise the funding they need to realise their full potential, resulting in massive missed economic opportunities.
Business lending to SMEs is often considered to be complex and unprofitable. The lending gap is estimated at somewhere between US$ 1.5 trillion and US$ 5.4 trillion globally and growing. Data asymmetry is one of the main reasons for this: SMEs might be confident of repaying their loans, but lenders have found it difficult to assess their risk and obtain collateral – particularly for the service industries that make up 80% of the economy, which tend not to have physical assets as security.
As the economy digitises, the amount of data and the number of data sources proliferate. This additional data gives lenders the opportunity to overcome data asymmetries between lenders and borrowers. Consequently, both parties can make better informed, quicker, and better-priced loan decisions, helping to bridge the persistent SME funding gap, and substantially moving the needle from a customer experience perspective.
To find out more, watch the on-demand discussion now.
Independent Senior Advisor to financial institutions and fintechs
Tobias is an ex-McKinsey Partner and has worked with financial institutions, ranging from some of the world’s largest banks to start-ups, for over 25 years in more than 50 countries. Tobias is a scholar, cooperating with the University of Cambridge, focusing on the intersections of data science, risk management, and psychology. He built McKinsey’s Credit Risk Analytics capabilities and has successfully taken bias out of commercial underwriting decisions around the world. He is the author of the book Understand, Manage, and Prevent Algorithmic Bias: A Guide for Business Users and Data Scientists.
Co-founder and CEO, Trade Ledger
Prior to founding Trade Ledger, Martin accrued 25 years’ experience creating, growing, mentoring, and investing in innovative global technology companies across North America, the UK, continental Europe, Russia, Asia, Australia, and New Zealand. He has been CEO of a venture capital firm, General Manager of SAP’s Cloud Solutions Division overseeing a period of extraordinary growth, and CEO and Research Director of software consultancy Inn-vision. Martin holds a BEng and MEng (Manufacturing Systems Engineering) from the University of Warwick, and is engaged in an international research examining the transformation of procurement and supply chains and the emergence of embedded finance for global businesses.