Machine learning and predictive analytics have started to make a real difference in the VC world when it comes to finding investments; VC’s typically traveled a lot and met with thousands of companies to find a few investments; by using machine learning to break down troves of data, like job postings or performance in the App Store, investors can find potential gems without the same laborious effort; VC firms are investing in tools to help them refine searches and comb through thousands of companies quickly; increased computing power and cheaper ways to rent server capacity has really helped VC’s, even the small firms, use these techniques every day. Source.
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Fintech
Third party data sharing is becoming more prevalent in financial services as fintech providers increasingly provide more personal financial management solutions. In Europe the Revised Payment Services Directive (PSD2) is also revolutionizing data sharing for the fintech market.
American Banker provides insight on the pros and cons of data sharing also including insight from Wells Fargo and JPMorgan, who have developed leading data sharing partnerships and solutions. While the majority of the challenges to data sharing are refuted by technology experts there are a number of considerations that have limited the data sharing process.
Some of banks' leading concerns for data sharing include connectivity risks, system overloads and differing regulatory standards. Data can be affected when transferred to a third party, it can be difficult to determine data being extracted and system updates can affect data flow. Banks have also reported system overloads from data extractions at peak hours. Additionally, banks report that differing regulatory controls and standards can cause security risks when working with third party data aggregators. Source
