Revolutionizing the world of soft loans on fintech

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Web data extraction | Credit scoring is, without a doubt, the main barrier that must be overcome by those who want to access credit for consumption. As, in general, financial institutions rate potential clients by their credit history, many people are excluded from the possibility of having the money necessary to be able to face a specific expense.

Scrapers on fintech / Financial Inclusion using Web Data Extraction

The new financial organizations that have emerged in recent years – the so-called fintechs – understand that potential clients can still qualify for a loan even if their credit history does not support them, and that their ability to pay could be determined by analyzing large amounts of data, especially data. that traditionally have not been part of the credit evaluation. The DNA of fintech companies is based on a faster and more accurate credit risk assessment, and on reaching a larger customer base, minimizing failures.

This is where two relevant and necessary technological procedures come into play in this context: web data extraction (also known as information extraction) and machine learning. The enormous amount of information coming from the Web is presented in an unstructured way. These data, previously wasted due to not being tabulated, become useful records in a database, ready to be used, thanks to the information extraction tools. ScrapingPros applies this type of technology and tools to boost the business of different companies in the financial industry, through intelligent processing of data, its own or from the Internet.

Machine Learning and Prediction models on Fintech

Through algorithms and machine learning techniques, it is possible to find significant patterns in the data, which allow the development of prediction models and superior and inclusive credit scores for the financial market. The information collected can also guide users in the type of credit product suitable for their needs and conditions.

Accuracy and predictive capacity are dramatically enhanced with the application of web data extraction and machine learning techniques. For this to happen not only requires the consideration of a robust data source model, but also a correct calibration of the data collection.

In short, it has become vital for the future of finance that these non-traditional entities acquire an increasingly leading and relevant role. The financial scenario, as well as the decision-making process, are being drastically altered, and technological innovation is a fundamental part of this transformation.