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Hot : Human Centered AI in Small Business Credit

Empowering entrepreneurs in bridging their finance gap

Exciting news! My latest research has been given the green light for publication in the prestigious European Journal of Finance. Teaming up with esteemed professors Karen Elliott, John Goedee, and Roger Leenders, our paper delves into a novel inclusive credit scoring approach aimed at bridging the multi-billion dollar finance gap at reasonable terms for sole traders and small businesses.

This work is particularly crucial as we witness a surge in entrepreneurial spirit among millennials and Gen Z individuals, who now make up 60% of new business ventures. With a staggering global population of over 5 billion, they represent the largest customer group ever seen. Sadly, specific sub-groups such as women entrepreneurs, as highlighted by the World Economic Forum, are confronted with a significant access to finance gap amounting to $1.7 trillion. They own 22% of micro-enterprises and 32% of SME’s.

The evolving landscape of next-gen finance demands solutions that are not just entrepreneurial-friendly but also digitally savvy, micro-personalized and sustainably inclined. However, a major hurdle persists in accessing finance for these businesses, compounded by the need for digitalized application processes.

Enter human-centered AI, a game-changer poised to revolutionize small business credit on a global scale. By enhancing efficiency, accuracy, and customer experience, human-centered AI has the power to transform lending to MSME’s. But how exactly can it benefit small business lenders?

Entrepreneurs are the backbone of innovation, tirelessly dedicated to growing and adding value to their companies. However, almost 70% of MSME’s do not use external financing from a financial institution. To fuel their growth and build a sustainable business model, choosing the right type of capital is essential. For some, like those with compelling business ideas and high growth potential, equity investments may be the way to go. But for many, building a solid, steadily growing business is the priority. For them, options like invoice financing can be a game-changer. With interest rates typically ranging from 1% to 7%, it's often the most cost-effective solution for accessing working capital, especially for companies with creditworthy customers and accounts receivables. The issue however is that receivables finance providers are far from tailored to the needs of next generations. 

Revolutionizing receivables finance with Human-Centered AI and Alternative Data

Next week, I'll be delivering a keynote at the World of Open Account conference in Warsaw, Poland, focusing on the application of human-centered AI in receivables finance. This conference shines a spotlight on receivables finance across Central and Eastern Europe (CEE). According to the European Investment Bank, 24% of small and medium-sized enterprises (SMEs) and 27% of young firms in CEE, say access to loans or other finance is a problem. Innovative firms are also more likely to be credit-constrained, particularly young start-ups.

Research conducted by the SME Banking Club among 76 receivables finance lenders across seven CEE countries plus Turkey has revealed some intriguing findings. While 27 of these lenders have partially digital processes, only 17 offer a fully digital process. Of these, a mere 3 provide both online and mobile application options, with just one offering a mobile-only application. And despite the digitalization efforts, only 20 out of the 76 lenders have automatic decisioning in their processes.

These findings highlight a significant mismatch between the current state of receivables finance providers and the pressing needs of the next generation. There's a clear demand for instant, micro-personalized, and mobile-first solutions that cater to the evolving needs of businesses. It's time for the industry to embrace innovation and adapt to meet these demands head-on.

Our newest research unveils compelling advantages that human-centered AI and alternative data bring to small business lenders. By incorporating AI technologies like machine learning and natural language processing, lenders can streamline receivables finance processes, reducing manual efforts and errors.

Moreover, AI, combined with alternative dynamic data sources, offers enhanced credit risk assessments beyond traditional financial metrics. Analyzing dynamic non-traditional data such as open banking, social media activity, digital footprint, biometrics and entrepreneur personality profiles provides deeper insights into a borrower's financial behavior, improving inclusive credit scoring, decision-making and risk management.

Incorporating alternative dynamic data into credit risk models enables lenders to gain market share by extending receivables finance to a broader customer base, including freelancers, sole traders and small businesses and identify and mitigate risks more effectively, minimizing losses due to defaults and delinquencies. Early warning signals of financial distress or changes in borrower behavior can prompt timely remedial actions, preserving loan portfolios' quality.

Furthermore, human-centered AI enhances the customer experience by offering tailored financial solutions and personalized interactions. Understanding individual preferences allows AI algorithms to recommend suitable financing options, providing proactive support throughout the finance lifecycle.

Real-time analysis of applicants and invoices accelerates lending decisions, vital for receivables finance where timely access to working capital is critical. Automated data collection and analysis streamline underwriting processes, expediting funding approval for borrowers.

Predictive analytics powered by AI optimize cash flow forecasting and liquidity management, enabling businesses to anticipate and manage financial needs better. This enhances financial stability and growth opportunities for both businesses and financiers in the receivables finance ecosystem.

By embracing dynamic alternative data analytics, lenders can differentiate themselves clearly in the next gen credit market, offering innovative solutions that attract new, young customers, like woman-entrepreneurs, and strengthen existing relationships. Developing personalized financing products meets evolving market demands, driving business growth and profitability and enhancing the lender's reputation as a customer-centric institution.

In summary, the fusion of human-centered AI and alternative dynamic data elevates receivables finance, providing advantages for both lenders and borrowers. Not only does it tailor the customer experience in factoring to the next gen lifestyle-demands, it also enables micro-personalized offerings that deeply engage next-gen customer segments. According to McKinsey, it also boosts profitability by 9% to 15%. It's a mutually beneficial scenario that drives the industry into a new era of innovation and opportunity. One of the most exquisite advantages lies in its ability to provide financial inclusion and financial wellness for small business entrepreneurs at large, particularly empowering groups such as women entrepreneurs.

 

 

 

 

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