AI & ML
Predictive analytics is fundamentally changing how financial institutions assess risk, detect fraud and personalise services. Industry experts from FintechOS, Alteryx and PAYSTRAX share insights on how these technologies are revolutionising the financial sector beyond traditional methods.
The conventional approach to credit risk assessment, which relies heavily on historical credit data and financial statements, often fails to capture the complete financial behaviour of small and medium enterprises. This limitation has prompted a shift towards more dynamic evaluation methods.
“ AI-driven models leverage machine learning to assess creditworthiness dynamically, incorporating real-time and behavioural data for more accurate and fair lending decisions, boosting access to credit through predictive analytics,” explains Mark Dearman, Director of Banking Industry Solutions at FintechOS, a company that provides digital solutions for financial institutions.
Transactional bank data has emerged as a key source for these predictive models, offering valuable insights into spending patterns and cash flow. Open banking initiatives further enhance this capability by providing direct visibility into account activity.
Mark emphasises that by integrating predictive analytics with alternative data sources,“ financial institutions can expand credit access while mitigating default risks,” creating opportunities for previously underserved individuals and businesses. Financial product management platforms that incorporate AI are enabling lenders to build credit models that evolve with changing economic conditions. This innovation not only improves risk prediction but also fosters financial inclusion for businesses and individuals traditionally overlooked by conventional assessment methods.
The evolution towards more sophisticated predictive analytics raises important questions about balancing personalisation with privacy concerns. As financial products become increasingly tailored to individual needs, institutions must navigate complex data protection challenges.
“ The techniques to anonymise data and maintain privacy have dramatically improved over the last decade, enabling organisations to leverage rich data to customise their product offerings,” notes Alan Jacobson, Chief Data & Analytics Officer at Alteryx, a data analytics software company.
A critical consideration in this process is ensuring that data-driven personalisation doesn’ t introduce unintended biases. Alan highlights the importance of continuous testing:“ Does this factor unintentionally exclude or disadvantage certain groups in a way that wasn’ t intended? Continuously testing for these unintended consequences is essential to ensuring safe and effective outcomes.”
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