Novel applications of advanced predictive analytics and artificial intelligence to improve SME competitiveness and access to funding

Abstract

Small and Medium-sized Enterprises (SMEs) are a collective group of organisations that make a significant societal and economic impact globally. However, these organisations face numerous challenges compared to their larger counterparts. Over the three papers that form this thesis, methodologies are developed from the Artificial Intelligence (AI) and Machine Learning (ML) domains to enhance SME competitiveness and access to funding.

The first paper addresses the access to funding challenge driven by information asymmetries and prohibitive costs faced by SME credit lenders. Specifically, a deep language model is applied to loan officer free-text assessments to predict default risk. The study shows that the text alone effectively predicts default and, when combined with traditional credit scoring data, is suitable for partly automating the SME lending process while offering insights to address information asymmetries.

The second paper then moves on to the competitiveness challenge. Many SMEs lack the resources and technical expertise to leverage advanced AI models and unstructured data. In this paper, unsupervised computer vision and deep learning methodologies are developed to create user-friendly feature representations for remote sensing data. Specifically, the derived LiDAR imagery representations are tested in a predictive context using socio-economic outcomes for small geographies in Greater London. The results demonstrate that these accessible representations outperform baselines using standard features alone, thus making them suitable for organisations like SMEs.

Finally, the third paper considers how SMEs can benefit from enhanced automation and decision-making by adopting AI systems. Focusing on developing a decision support tool to improve debt recovery using customer behavioural data, this study combines deep sequence learning and uplift modelling to identify customers more likely to respond to targeted interventions. When applied to a dataset supplied by a small utility company, the results show significant performance improvements compared to baseline models. As a result, such approaches can assist SMEs in reducing their debt book value and streamlining recovery resource allocation.

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Identifiers

ORCID for Matthew Paul Stevenson: orcid.org/0000-0001-6232-0745

ORCID for Christophe Mues: orcid.org/0000-0002-6289-5490

ORCID for Cristian Bravo Roman: orcid.org/0000-0003-1579-1565

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