Banks have been in the business of deciding who is eligible for credit for centuries. But in the age of artificial intelligence (AI), machine learning (ML), and big data, digital technologies have the potential to transform credit allocation in positive as well as negative directions. Given the mix of possible societal ramifications, policymakers must consider what practices are and are not permissible and what legal and regulatory structures are necessary to protect consumers against unfair or discriminatory lending practices. In this paper, I review the history of credit and the risks of discriminatory practices. I discuss how AI alters the dynamics of credit denials and what policymakers and banking officials can do to safeguard consumer lending.
Fledgling gal-bots are the latest hires in the virtual assistant landscape. Meet Mia and Marge: two virtual assistants in the banking world – each brought into existence by women, both of whom carry deep institutional knowledge, subject matter expertise and long-standing credibility. UBank's Lee Hatton (Mia) and The Royal Bank of Scotland's (RBS) MaryAnn Fleming (Marge) are among 40 women who have been recognized as 2019's women leaders in A.I. by IBM. These leaders have succeeded in garnering acceptance of A.I. in the workplace, elevating their customers' experience and their companies' brands. It seems mortgage consumer complaints consistently surface around the loan application process according to UBank CEO, Lee Hatton.
Enterprises adopt artificial intelligence in an effort to positively impact their business performance. But the power of AI goes beyond business and can even change human experiences. This 21st century technology is serving as a driver and even impacting consumer services across a variety of industries, from retail, finance and beyond. The following client experiences serve as a gateway to better understand AI, which not only helps create a reaction, the technology can also help us act proactively in advance. Imagine a young couple who just became first-time parents and want the peace of mind that if anything happens, their new family member is protected.
Medical information and data has grown exponentially in recent years, posing new challenges for life insurance underwriting. With often voluminous medical histories to assess risk, the process can take an inordinate amount of time. Applicants can end up frustrated, dropping out of the application process and seeking other alternatives, perhaps with competitors, in search of a quicker turnaround. In response, insurers are turning to natural language processing – a more focused implementation of conversational AI – to assist with sifting through massive amounts of medical documentation to identify and even assess mortality risk. The benefits are manifold: not only does this result in an accelerated and accurate new business underwriting process, but it's also a way to create a quality data set for improved predictive underwriting.
Austin-based robotics company Diligent Robotics has secured $3 million in seed funding, according to company database Crunchbase, topping the city's recent funding headlines. The cash infusion was announced Oct. 1 and led by Ubiquity Ventures. According to its Crunchbase profile, "Diligent Robotics [creates] robot assistants that assist people with chores... Moxi is our hospital robot assistant that helps clinical teams with their routine, non-patient facing tasks so they have more time for patient care." The three-year-old startup has raised two previous funding rounds, including a $2.1 million seed round in 2018. The round brings total funding raised by Austin companies in artificial intelligence over the past month to $35 million.
Peer-to-peer lending marketplaces like LendingClub and Prosper Marketplace are driven by what is essentially a brokers fee for connecting investors and borrowers. They are incentivized to increase the total number of transactions taking place on their platforms. Driven by ease-of-use, their off-the-shelf credit risk assessments are scored in grouped buckets. On a loan-by-loan basis, this is inefficient given each loan's uniqueness and the sheer amount of data collected from borrowers. Scoring risk on a more granular, continuous basis is not only possible but preferable over discrete, grouped buckets.
Executives are excited about artificial intelligence. According to Venture Beat, 90 percent of C-suite executives say that AI is the next technological revolution. Financial services firms have already embraced AI for risk assessment, customer care and cognitive digitization, but Forbes notes that 51 percent of companies cite cost reduction as the primary benefit. How do financial firms leverage AI in banking to create significant cost takeout? Big data poses big challenges for financial firms.
Today, terms like Gen-Z and Millennials are the craze words for most businesses world over as they account for more than 60% of the demography. Unsurprisingly, everyone wants to understand what moves them emotionally, physically and financially. Moreover, their communication and consumption behavior is shifting trends and in many cases, causing trends to emerge. One of these trends is that of the Digital lending marketplace where one can avail any kind of loans at the click of a button! Notwithstanding the higher cost of capital, the enticing factors of this digital lending marketplace are bespoke lending products, convenient & quick disbursement and completely paperless transactions.
With plenty of post-recession anti-banking sentiment still lingering, it's common to see fintech and traditional banks framed in oppositional terms. There's some truth to that, especially with disruption-minded digital-only banks, but technological innovations have transformed banking of all stripes -- and nowhere is that clearer than with artificial intelligence. AI has impacted every banking "office" -- front, middle and back. That means even if you know nothing about the way your financial institution uses, say, complex machine learning to fend off money launderers or sift through mountains of data for fraud-related anomalies, you've probably at least interacted with its customer service chatbot, which runs on AI. Read on to learn how else AI is transforming the way banks operate, from investment assistance and consumer lending to credit scoring, smart contracts and more.
The online lender Enova, which has been using artificial intelligence to make credit decisions for years, has lately been expanding its use of AI to handle additional tasks: spotting fraud, determining who should receive product offers, and projecting possible losses once a loan is booked. Most recently, it has also been been having AI scour paper documents to uncover false information, verify income and employment, and conduct know-your-customer checks. It also sponsored a survey, conducted by Harvard Business Review and set to be released Thursday, that benchmarks how businesses are using AI. Generally speaking, it finds companies are adopting AI slowly: though 68% of executives say AI will be a competitive differentiator within the next year and 64% are investigating or piloting AI projects, only 15% of organizations have AI-powered analytics in place due to technical and cultural challenges. In its back office, Enova has been training AI engines to review documents such as bank statements and pay stubs and automating decisions based on the information in those documents.