Data science has helped financial lenders significantly reduce lending risk by rapidly analyzing a wider range of data on potential customers and using it to provide a single representation of risk: the credit score. 15 Predictive models are typically trained using thousands of customer profiles, each made up of hundreds of data entries. After “learning” from a combination of historical data on customers, plus peer group and other data, the models are ready to perform the same credit- scoring tasks on real-life loan applications. By reliably predicting the likelihood of customers paying back their loans—or displaying other defined behavior in future—robotical- ly-charged credit scoring systems enable human staff to work much faster and more accurately. 16 At Provenir, we have expanded our own risk analytics and decisioning platform to support a wide range of programming languages including Python. With access to an ever-widening range of algorithms and data libraries, Python’s speed, flexibility, stability, and ease of integration with almost any information source have made it today’s go-to tool for data scientists. Because it works so well with AI, Python enables you to build self-sufficient non-linear models with a large number and variety of historic data variables. This ultimately helps our clients create more sophisticated statistical models at higher speeds, gain a more accurate picture of prospective customers, and drive faster, more reliable credit risk decisions.
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Data science has helped financial lenders significantly reduce lending risk by rapidly analyzing a wider range of data on potential customers
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