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Machine Learning in Insurance
Insurance companies only process 10-15% of the available data most of which is structured data. Analyzing the remaining data, structured and unstructured, would provide any insurer with tremendously more information, which can impact the premiums they charge, fraud prevention, and general risk management. Successful implementation will be specific to the insurer, leveraging the existing organization while finding places to add value.
The Cure for AI Fever
AI is best seen as a process, not a tool. There is no single solution, magic bullet AI that will cure all your organization's problems. Instead, AI is something that fits into your organization's problem-solving process. AI will be critical in some areas, very helpful in others, and perhaps not as helpful elsewhere. As a manager, you are responsible for knowing where AI fits and how to best deploy it in your organization.

The Roles of Alternative Data and Machine Learning in Fintech Lending
A recent paper by the Federal Reserve Bank of Philadelphia illustrates the value of machine learning in lending. In particular, the paper stresses non-traditional inputs in determining creditworthiness and predicting loan performance, such as with Lending Tree, for example. We believe that these machine learning methods can still be further improved and streamlined, creating additional opportunities for lenders.