FinTech as a tool for financial inclusion: a perspective of South African financiers on SME development
Bibliographic record
Abstract
According to Statistics South Africa, SMEs play the significant role of creating employment opportunities for more than 25% of South Africans. In addition, this not only increases chances but ensures that many have access to means of production, and it also increases the (GDP) and overtime improves the standard of living in many communities. It has been noted that the high unemployment rate of almost 32.9% at the first quarter of 2023 can be significantly reduced by ensuring that sectors such as SMEs are viable and well-funded. It has been reported that nearly 75% failure rate of most SMEs is attributed to limited financial access. Even if financial institutions make efforts to provide SMEs with financial assistance so that their operations run smoothly and they remain profitable going concerns, SMEs still struggle to get financial access which makes it difficult to sustain operations and create job opportunities. Moreover, it was reinforced that for c s to be effective there is need to ensure that they are well developed and have access to financial services. It is therefore justified to undertake this study to explore the role played by FinTech in enhancing financial access to SMEs in South Africa. The study employed the thematic qualitative analytical approach to analyse primary data which was collected from twelve (12) professional bankers, development finance institutions personnel and South African government agencies. These participants were relevant, suitable and credible because they provide various support and services to SMEs including providing transactional services such as tailored current accounts to help manage daily transactions and make payments. In addition, some equally assist in providing financial advice such as identifying financing options, risk management services such as insurance and credit guarantee scheme. The findings indicated that FinTech has significantly contributed to ensuring that SMEs are not financially excluded by assisting them to make use of automated systems which often helps them to improve service delivery. This occasionally helps to foster collaborations among institutions for instance, DFIs partnering with government agencies to work on incubation programmes for SMEs. Moreso, strategic partnerships among businesses are established as businesses have the opportunity to network on platforms such the South African Innovation Summit (SAIS). Although FinTech was assumed to have significantly improved operations and the productive capacity of SMEs some challenges have also been highlighted, and these include failure to comply with stipulated regulations particularly in government institutions who are expected to adhere to PFMA. The other challenge which was identified was the reluctance to use FinTech as people still prefer face to face contact with financial service providers. It was also established that some participants were not technologically savvy therefore, relevant skills development programmes had to be implemented. The other challenge was that government agencies and some (DFIs) were found to be lagging behind with 4IR and AI whilst banks level of readiness was advanced. For instance, it was noted that while it is convenient and effective to provide mechanisms useful in ensuring financial inclusion of SMEs like banking applications (Apps) and other various online systems like the bridge portal, simplyBiz, finfinder, SAP, CRM, and sefalas; the sad reality is that these platforms may either not be sufficiently provided or not fully utilized. Such a setback not only excludes SMEs from accessing financial services such as loans and online banking it reduces their growth rate. On the other hand, some barriers to financial access by SMEs were found to be bureaucracy, collateral and lack of business skills. Therefore, it is recommended that government and DFIs should fully adopt and implement FinTech as it is a tool for financial inclusion of SMEs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".