A new report from Numis entitled The State of AI in 2017 explains the potential AI and machine learning for wealth managers; as AltFi reports, “AI enables asset managers to deliver to the mass affluent a degree of personalisation and service quality previously reserved for high net worth clients.”; the technology can also help to improve quality, decrease cost and help to make most of the asset management industry into robo advisors. Source.
Simility, a machine learning adaptive fraud prevention product, raised $17.5mn from PayPal, The Valley Fund and Trinity Ventures; the company helps to prevent fraud and abuse in real time using machine learning and big data analytics; Rahul Pangam, Co-Founder and CEO of Simility, tells Crowdfund Insider, “Digital disruption in the financial and commerce sectors has resulted in the need for a fraud and risk management solution that goes beyond legacy.”; the company plans to build out data science teams and continue expanding globally. Source.
A new report published by Backbase and IDC says the APAC region is set for a dramatic increase in adoption...
Pagaya is an asset manager based in New York; they announced that they have received $75 million in debt financing from Citi; the funds will be used for its Opportunity Fund to invest in loans by online lenders such as Prosper and LendingClub; the company uses machine learning to determine loans worthy of investment. Source
Cion Digital's new Advisor Lending Platform connects wealth professionals and firms with lenders in a more efficient process than was previously available. They also now offer their product suite to a more significant portion of the financial services and retail sectors.
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Artificial intelligence (AI) and machine learning are becoming increasingly relied upon by financial services companies and the credit sector; the algorithms powering these solutions have also advanced the use of AI and machine learning; while these advancements have helped new solutions they have also created new risks; these risks are primarily focused in three phases: input, training and programming; risks can also be higher when using nontraditional data; a report from White and Case titled, "Algorithms and Bias: What Lenders Need to Know" provides details on the evolution of algorithms in artificial intelligence and machine learning and explains important factors to consider for credit providers. Source
Auto lenders are not yet ready to turn credit decisions over to artificial intelligence and machine learning; Mike Kane, Ally Financial...
Upstart has been quietly doing something that many others have found difficult: building a profitable online consumer lending business. I...
Douglas Merrill, the CEO of ZestFinance, writes in Forbes that more data for making an underwriting decision is usually better;...