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3 years ago in Financial Economics , Technology By Varun
Do the conclusions avoid making definitive statements about the quality or effectiveness of interventions (which is outside the typical scope of a scoping review)?
In my research on fintech and social equity, I'm observing a tension between the promise and peril of AI in credit. Proponents argue it can extend services to the "thin-file" unbanked, while critics warn of encoded biases. From a practical, evidence-based perspective, how is this technological shift actually reshaping financial inclusion?
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By Anuj Patel Answered 1 year ago
My work with fintech partnerships has shown this is not a simple upgrade. AI models, using alternative data like cash flow or rent payments, can indeed include those ignored by traditional models reliant on credit history a net positive for inclusion. However, I've also seen cases where these models inadvertently exclude entire demographics due to biased training data or opaque proxies. The key difference is scale and subtlety: traditional methods explicitly excluded; AI models can do so implicitly at massive scale. The impact hinges entirely on conscious, ethical design and robust bias auditing.
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