With 70% of the global workforce relying on them for employment, are Micro, Small, and Medium Enterprises (MSMEs), the real heroes of the global economy?
Yes – in fact, as per the World Bank’s International Finance Corporation, MSMEs account for 50% of the GDP. Imagine the global economy as a bustling city, with MSMEs as its intricate network of roads and highways. Besides quietly carrying the weight of this significant size of the workforce, these enterprises are also ensuring the seamless movement of goods, services, and innovation worldwide.
Yet, much like neglected infrastructure, cracks in their foundation—limited credit access, systemic barriers, and lack of timely support—threaten to disrupt their flow and progress.
The post-COVID-19 era has seen a marked rise in MSME numbers and employment. In 2023, SMEs worldwide were projected to be around 358 million, a leap from 328 million just 3 years prior.
In the US, the numbers tell a compelling story. Here, small businesses (SBs) with less than 500 employees account for 99.9% of all business. The 34.8 million SBs in the country employ 59 million or close to 46% of employees. They have been credited with giving rise to around two-thirds of net new jobs in the private sector in the last couple of decades. Since 2019 alone, a massive 70% of net new jobs owe their existence to SBs. With a 50% uptick in new business applications compared to 2019 or an average of 4.3 lakh per month in 2024, the sector is witnessing a meteoric rise.
Specifically, SBs in the US face quite a few challenges towards sustainable growth. While credit penetration to the sector stands highest at 50%, findings from a 2023 survey by Goldman Sachs reveal that over 75% of SB owners are primarily concerned by the ability to access capital and credit. Higher cost of lending to buffer against greater risk, gaps in effective evaluation and monitoring of credit risks alongside steadily rising debt have made it increasingly difficult for small enterprises.
Supporting and ensuring high performance from this sector hinges on technological interventions to bridge these gaps. Solutions emerging in the field of AI such as Agentic AI could be a step in the right direction by traditional lending institutions and Fintech companies.
Agentic AI to power up lending practices
Rooted in the word ‘agent’, Agentic AI involves an autonomous AI architecture with numerous AI agents fielding diverse capabilities to enable real-time data gathering, pooling, and decision-making with marginal human intervention. In contrast, AI models such as Generative AI need extensive training and inputs from humans to perform tasks.
In the context of small businesses, Agentic AI could prove to be game-changing.
Conventionally, banks and Fintech institutions have exercised greater caution while lending to small businesses. This stems, firstly, from operating in a constantly evolving regulatory landscape with heavy compliance requirements. Other challenges range from extended risk analysis and delays in assessment of creditworthiness, protracted loan disbursal processes, inconsistencies in data quality, to operational inefficiencies, and inability to comprehensively integrate data for better borrower risk assessment, among others.
Now imagine what a traditional lending institution would be able to do with a collaborative team of AI agents who could quickly collate all this data in a matter of minutes. It would help them make quicker, more effective data-driven decisions while ensuring more inclusive and equitable lending practices to empower the small business economy.
Keeping financial inclusion at its heart
While the small businesses sector is among the largest employers in the country, it is counter-intuitive to overall growth if banks and Fintech players don’t step up to efficiently support them. In fact, the financial services sector has the opportunity to create real, impactful change with Agentic AI through:
- Ensuring a win-win scenario: Through intelligent assessment and proposal of new and customized product groups and offerings, Agentic AI could enable traditional lenders and Fintech players to bring underserved communities under the lending umbrella. From offering credit to insurance, and savings-related products, there is immense potential for widening the customer base and spread of offerings for low-income groups, historically underserved communities, and SBs – a win-win for everyone.
- Efficient target segmentation: A good use case for Agentic AI would be target segmentation for loan disbursal, risk assessment, etc. Specific to the US, the clubbing of all types of businesses with less than 500 employees under the 'small businesses' head – from a tech startup, a mom-and-pop store, to a jewelry business – could hinder lending decisions for lenders and borrowers. Agentic AI could help evaluate in depth, individual as well as segment-specific dynamics for an adaptive, mutually beneficial lending experience.
- Unique, diversified product offerings: By gauging dynamic trends and offering micro and macro insights into evolving customer requirements and financial climate, lenders have a wider playing field to explore innovative products and services. In the same vein, an early warning trigger mechanism can also let lenders know when a product is underperforming or prone to issues and should probably re-evaluate their product and portfolio strategies.
- Evolution as a valuable partner: By taking a more personalized yet informed every step with the customer – right from offering vital financial advice to sophisticated investment strategies through evaluation of different market instruments and customer risk appetite, an Agentic AI system can become a key enabler in the bank’s evolution as a preferred financial partner for customers.
Potential scope of Agentic AI for small enterprises
The processing of loan to SBs and the management of debt could critically benefit from the application of Agentic AI.
With regards to loan processing, the challenges for small businesses range anywhere from being considered too risky to receive credit to inadequate repayment potential, past history of defaulting, modest business performance over a particular period, poor credit scores, etc. However, by adopting a data-driven approach that assesses a host of metrics drawn from traditional and alternative data in real-time, the Agentic AI system could suggest several scenarios that support loan approval with favorable outcomes for both lenders and borrowers. This process could be modified by the AI system and tailored as per the specifics of every individual customer.
Such an AI-led process could potentially take approval rates up by 50% while increasing automated decision-making by up to 90%. This will not only make loan disbursal processes faster and more efficient, but will make them more fair and help open up access to crucial credit for SBs.
In the case of managing debt, the challenge of delayed payments, non-payment, and bad debts remains a considerable and concerning one for lenders. AI can help bridge this gap in three key ways. Firstly, it can pool in data pertaining to the borrower’s current financial commitments, repayment history, income and spending. Secondly, it can suggest to the borrower how to effectively manage and balance income and debt, over time, without spreading their funds too thin. By recommending financial instruments to invest in to counter and fulfil debt and future cash requirements, banks can also build trust with their customers that they are invested in their journey and prove invaluable. This will ensure better recovery and lower loan default rates over a period.
Thirdly, by assessing every customer’s financial circumstances, the AI system can create a repayment plan and schedule that is comfortable for every individual customer. It could also make suggestions based on metrics such as current good/poor performance, market indicators, and investments to nudge larger/smaller repayments. This will help strengthen the lender’s position as an invaluable partner, ensuring a long-standing association.
This will not only help traditionally-led SBs but also the large chunk led by women, as well as those from underserved and marginalized communities.
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