All you need to get started is your data More importantly, the ability for businesses such as lenders to access and leverage the benefits of machine learning requires nothing more than what they are already awash in – namely data. A great number of third party providers has emerged offering machine learning as-a-service, often over trusted and highly secure cloud providers such Amazon Web Services. There are great benefits to the as-a-service model. The key elements of, say, a credit risk determination model built for one lender can be easily ported to another lender, and then be modified and tailored to fit individual lender needs and requirements. Further, a lender need not invest in additional hardware, software, or hard-to-find, sophisticated IT staff to create the models and reap the benefits of machine learning. All you really need is data. And finally business professionals, such as the credit risk manager at a typical lender, can easily use the machine learning models offered by the better third party providers, lessening the dependence on IT. For a number of reasons, machine learning is poised to become a significant technology in credit risk determination. For one thing, the lending market itself is undergoing significant change and disruption, with well-funded, non–traditional lenders – so-called fintechs - muscling into the market backed by the latest digital solutions. No longer is it acceptable to take days or even weeks to approve or deny a loan application. Increasingly it must be done nearly in real-time, which implies high level of automation at the lender. “Machine learning is perfect for building models to predict risk, identify correlations, and categorize individuals and activities,” says Nik Rouda, senior analyst and machine learning specialist at ESG. “Its models can be trained on the vast amounts of transactional data and customer profiles.” Also lenders are being nudged and cajoled by government forces to lend responsibly to smaller borrowers, including small businesses and individuals. The data needed to properly conduct credit worthiness for these constituents is far different than loaning to bigger borrowers. More automation is needed to lessen the dependence on expensive manual processes, which are not cost-effective when dealing with
There are great benefits to the as-a-service model.
– NIK ROUDA, SENIOR ANALYST AND MACHINE LEARNING SPECIAL I ST AT ESG “Machine learning is perfect for building models to predict risk, identify correlations, and categorize individuals and activities.”
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