The lending and credit scoring sector have more data than ever before at their disposal. How they leverage this data to create value for their clients and social impact determines the outcomes they can achieve in the financial services space. In 1959, Arthur Samuel, a pioneer in the field of machin...
The release goes on, "NVIDIA DRIVE provides a holistic safety platform that includes process, technologies and simulation systems, as described below: (1) Process: Sets out the steps for establishing a pervasive safety methodology for the design, management and documentation of the self-driving system. These include NVIDIA-designed IP related to NVIDIA Xavier covering CPU and GPU processors, deep learning accelerator, image processing ISP, computer vision PVA, and video processors – all at the highest quality and safety standards. Included are lockstep processing and error-correcting code on memory and buses, with built-in testing capabilities. The ASIL-C NVIDIA DRIVE Xavier processor and ASIL-D rated safety microcontroller with appropriate safety logic can achieve the highest system ASIL-D rating."
This report considers the financial stability implications of the growing use of artificial intelligence (AI) and machine learning in financial services. Financial institutions are increasingly using AI and machine learning in a range of applications across the financial system including to assess credit quality, to price and market insurance contracts and to automate client interaction. Institutions are optimising scarce capital with AI and machine learning techniques, as well as back-testing models and analysing the market impact of trading large positions. Meanwhile, hedge funds, broker-dealers and other firms are using it to find signals for higher uncorrelated returns and to optimise trade execution. Both public and private sector institutions may use these technologies for regulatory compliance, surveillance, data quality assessment and fraud detection.
Petal has received a $13 million funding round from Valar Ventures, a New York-based venture capital fund that specializes in financial technology, to use artificial intelligence to fill holes in legacy risk vetting. "The problem is not that people have a history of bad credit, but have no history of credit at all," said Jason Gross, Petal's CEO. "They're young or have lacked access to financial services." Petal will use the funds to add scale for its formal launch. The company has been signing up prospective users since the fall and currently has about 40,000 consumers who preordered its alternative payment cards.
It's time to move beyond alarmist rhetoric about workplace automation and consider how human-machine collaboration can deliver a higher level of productivity. The question to ask isn't how many jobs will be replaced by artificial intelligence (AI) and robotics, but how work can be reconfigured in order to achieve the optimal integration of talent and machines. The digitalization of the workplace gives organizations a new set of options for getting work done. Traditional jobs can be deconstructed into independent, component tasks to be completed anywhere in the world by employees, talent on platforms such Upwork or Topcoder, freelancers, alliance partners and, yes, automation. Consequently, in deciding how to best integrate humans and machines, the focus should be on the work, not jobs.
As U.S. banks wrestle with the decision of whether to use artificial intelligence to help calculate credit scores and make loan decisions, a potential role model is MyBucks, a company that's been doing this for more than a year --and has even begun offering 15-minute, AI-based loans through WhatsApp and Facebook Messenger. MyBucks is a Luxembourg-based fintech that owns several banks and provides loans and basic banking products in seven African countries, Poland and Spain; it's expanding rapidly into other countries. U.S. regulators have signaled a willingness to accept banks' use of AI in lending. And the evidence so far, at least in MyBucks' case, shows that AI can improve credit quality and reduce defaults. MyBucks' Haraka app, which is now offered in Zimbabwe, Uganda, Swaziland and Kenya, and in early 2018 is expected to be introduced in the Philippines and India, can score a customer within two minutes.
Which works better for modeling credit risk: traditional scorecards or artificial intelligence and machine learning? Given the excitement around AI today, this question is inevitable. It's also a bit silly. While some new market entrants may have a vested interest in pushing AI solutions, the fact is that traditional scorecard methods and AI bring different advantages to credit risk modeling -- if you know how to use them together. Take, for example, our new credit decisioning solution, FICO Origination Manager Essentials – Small Business.
Artificial intelligence (AI)-generated marketing campaign company Persado has unveiled a new product, along with a fresh $30 million credit facility to fuel further expansion, reports Julie Muhn at Finovate (Banking Technology's sister company). Persado One, the new offering, provides personalised emotional engagement at scale. The company says the new development represents the "most significant advancement to date" of Persado's AI platform. Available through the company's enterprise level of service, Persado One uses deep learning algorithms to deliver personalised messaging to a customer based on their emotional profile. The company is also launching a professional class of tools to help marketers predict response rates, generate higher performing campaigns, and refine language according to a brand's style.
In all, the IIC awarded $150,000 to each of the four grand-prize winners, and $35,000 each to 12 runners-up competing in four categories: Financial Inclusion; Income Growth and Job Creation; Skills and Matching; and Technology Access. EFL (Financial Inclusion category): Three billion people worldwide lack the credit history lenders require to make a loan. Digital Citizen Fund (Technology Access) helps girls and women in developing countries gain access to technology, virtually connect with others across the world, and obtain necessary skills for success. New Day (Skills and Matching) is a smartphone-centric, low- to mid-income employment platform for developing markets worldwide, enabling scalable and rewarding job matching, skills building, and employer transparency.
Lenders traditionally make decisions based on a loan applicant's credit score, a three-digit number obtained from credit bureaus such as Experian and Equifax. Credit scores are calculated from data such as payment history, credit history length and credit line amounts. Upstart uses machine learning algorithms, a subset of AI, to make underwriting decisions. The platform's algorithms analyze 10,000 data points to evaluate the financial situation of consumers.