Emerging markets in Latin America face a clear gap between social and economic classes. Incorporating alternative data into the financial system could be presented as the possible solution to the lack of information on specific population segments, favouring financial inclusion and collaborating in political and economic decisions. However, would alternative data be the expected answer to solve economic differences? 

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The Latin American region presents a marked rift between classes affected by the lack of access to the economic circuit of this population segment that currently finds unbanked. Even though a considerable percentage of them could be potential bank clients, they are excluded from the economic system by not having a traditional credit history, thus widening the social gap.

Political and banking institutions' decisions depend on data that, given the complexities of LATAM, are often unreliable or very difficult to obtain. Faced with the region's difficulties, economic institutions - mainly fintech ones - have made a rapid turn in the population assessment process.

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In a market where a large percentage of citizens are unbanked, the challenge is greater. However, there are potential clients that the market has not yet detected. Faced with the lack of traditional data to perform a credit evaluation, alternative data with artificial intelligence is the possible solution for generating an alternative credit score.

Alternative data, obtained through metadata from smartphones, such as digital transactions, employment information, payment behaviours, and data obtained from social networks and satellites, allow financial companies to generate a credit score for people that do not have a traditional track record. Furthermore, the reliability of this alternative data favours incorporating a marginalised segment, thus allowing financial companies to increase their client base and benefit from financial and social inclusion.

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In turn, it enables companies to reduce the credit risk rate. This is due to the daily artificial intelligence evaluations that allow companies to define customers' behaviour patterns and anticipate them. With a much in-depth knowledge of this new segment, it is now possible to understand their needs, adapt financial decisions and generate customised credits for each person.

There is no doubt that alternative data is an excellent solution to the Latin American economic gap. By properly using its benefits and making a deeper analysis of peoples payment behaviour, it is possible to detect those who are reliable to receive a loan, reducing the gap between banked and unbanked people. In this way, financial inclusion is also transformed into social inclusion.