From Credit Bureau to Cloud: How Fintechs Are Reinventing Credit Scoring
* Millions of people without access to credit due to a lack of banking history.
In today's dynamic financial ecosystem, credit assessment is moving beyond traditional models based solely on banking histories and credit bureau reports. Today, fintechs are revolutionizing credit scoring by incorporating alternative data, artificial intelligence, and the power of open finance, creating more inclusive, accurate, and agile processes.
According to the World Bank, around 45% of adults in Latin America do not have access to formal credit products because of a lack of banking history. This represents an enormous potential market for fintechs that adopt more flexible scoring models.
The problem: limited and exclusive evaluations:
For decades, millions of people have been left out of the formal financial system because they lack a traditional credit history. This situation excludes young people, freelancers, self-employed workers, and entrepreneurs from accessing financial products that are key to their growth.
Main impacts:
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Low financial inclusion: millions of people without access to credit due to a lack of banking history.
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Inaccurate assessments: limited data increases risk for institutions and raises rejection rates.
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Slow and costly processes: manual models that do not scale as quickly as the market demands.
A study by Experian revealed that 70% of consumers seeking credit for the first time are automatically rejected due to a lack of traditional credit history.
The solution: alternative scoring with open data:
Fintechs are adopting more advanced models that integrate diverse sources of information to assess applicants' credit profiles more accurately:
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Tax and spending data: information from the SAT (Mexico's tax authority), invoicing, personal expenses, and connected accounts that offer a real view of financial behavior.
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Open Finance: API connections that make it possible to comprehensively analyze users' financial transactions and patterns.
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Artificial intelligence models: algorithms capable of identifying payment habits, spending behavior, and digital signals that predict ability and willingness to pay.
According to an Accenture report, alternative scoring models can increase loan approvals by 20-30% for previously excluded profiles, while also reducing default risk.
Expected results:
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Higher approval rates with lower risk.
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Real financial inclusion for historically underserved segments.
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Faster, fairer, and more personalized processes for users and lenders.
Conclusion: towards a new standard in credit evaluation:
Traditional scoring is being replaced by smarter, more open, and fairer models. At Finvero, we combine traditional and alternative data to deliver more accurate, secure, and agile origination and credit assessment solutions.