Fintech Can’t Stop the Coronavirus, But It Can Help Contain It – Here’s How.
It’s a notorious fact that markets hate uncertainty. Right now, the main source of uncertainty is the novel coronavirus (alias Covid-19), which has infected an estimated 122,000 people worldwide and resulted in the deaths of about 4,300 — for a mortality rate of around 3.4%, according to the World Health Organization. That’s sobering news, and it has engendered some newfound habits, among them: Avoiding public transportation in big cities Keeping hands clean, and, where handkerchiefs are absent Coughing into the crook of one’s arm rather than a bare hand The outbreak has also triggered a run on hand sanitizer. In some places, staples like pasta, canned vegetables, and long-shelf-life milk are in short supply. The US Surgeon General has even asked people to stop buying up surgical masks so they’re readily available to healthcare professionals who actually need them. Technologies That Promote Efficiency Are Often Low-Touch Meanwhile, sports seasons and cultural events have been put on hold, travel plans have been scrapped, and in some places, workers and students have either been encouraged or ordered to work from home. Some call these reactions prudent; for others they’re hysterical. Whatever your view on the matter, it’s undeniable Covid-19 is taking a toll, on lives, on energy, and on our finances. But if it’s possible to discern a silver lining, Covid-19 has already had the virtue of forcing us to think about the power of technology to limit person-to-person contact as we continue to conduct business. In an effort to reduce exposure, Sam’s Club, a members-only retailer owned by Walmart, is urging customers to scan and pay for purchases using a smartphone app instead of clustering together at checkout counters. We’ll come back to this point as it relates to the financial-service industry. SARS, Another Coronavirus, Took a Similar Toll on Markets Another immediate financial impact of Covid-19 is evident in stock markets around the world. The S&P 500, a broad gauge for large-cap US listings, has seen an 19% decline since hitting all-time highs in mid February 2020, slipping to levels it last saw in March 2019. But it’s worth remembering we’ve seen similar reactions to epidemics before. Covid-19 is just the newest strain of coronavirus, a family of viruses that can be passed from animals to people (and from person to person). Other coronaviruses cause a range of illnesses from the common cold to SARS, which in 2003 claimed 774 lives in 8,098 cases (for a mortality rate of 9.6%), according to the US Center for Disease Control. The SARS outbreak weighed on US stocks when prices had yet to recover from 9/11. But within months of SARS dominating the headlines, the Rolling Stones were performing a free concert in Toronto to celebrate the demise of the pandemic, and the US stock market had already rebounded by 19%. Of course stock markets aren’t economies, and Covid-19’s impact on the world’s economy is tough to call at this stage. Still, some important players are making educated guesses. Central Banks, Analysts, and Trade Associations Snap into Action On March 5, the US Federal Reserve signaled a hunch the widening epidemic could dent the US economy. It trimmed the fed funds target rate by 50 basis points to a range between 1.00% and 1.25%. The Bank of England also cut rates, and central banks in Japan and the European Union are expected to take some sort of action, even if rate cuts are off the table. Then, prompted in part by the Fed’s reaction to the health crisis, Wells Fargo cut its outlook for the securities brokerage business. The bank says firms should expect to see per-share earnings shrink by 12% for 2020, and an average share-price decline for brokerages in the neighborhood of 22%. Meanwhile banking associations are rushing to provide members with information to help them cope with the outbreak. UK Finance, a British trade group, recognizes the strain put on small and midsize businesses by Covid-19 and urges these enterprises “to contact their finance providers early to discuss how they can help support their companies through the coming weeks.” The American Banking Association promises two upcoming webinars. One is on “coronavirus preparedness,” and the other will “give institutions an opportunity to practice and adapt their technology and cybersecurity resiliency plans for large-scale, work-from-home scenarios in the event they are quickly needed.” Digital-Based Fintech Means Never Having to Say “Gesundheit” While governments, NGOs and business groups scramble to calm citizens, entrepreneurs and customers, another possible effect of the Covid-19 is a function not of its virulence, nor of its severity, but of its endurance. In this view, the health crisis could pose significant challenges to client-centric businesses, including lenders, and show these enterprises whether their technology is up to scratch. After all, a significant benefit of digital lending technology is in its ability to equip businesses to continue running in an emergency. Just as fintech can equip customers to engage with financial-service providers remotely — so that, for instance, a retailer can apply for a loan, secure approvals, sign documents, and make repayments entirely online — so could a bank’s lending-department personnel work remotely to keep the bank itself, and the credit-worthy enterprises that rely on it, running smoothly in a setting where it doesn’t matter if someone sneezes. And if problems crop up that can’t be solved on the phone, via text, instant messaging or email? Well, there’s always Skype or a dozen other conferencing apps for real-time, face-to-face interaction. TurnKey Lender Can and Will Help You in This Health Crisis Here’s the big point. A lot of public- and private-sector effort is being expended right now to cushion the impact of Covid-19, a virulent and sometimes deadly disease. But no matter what anyone else does, your clients’ are still going to be feeling depressed and fearful in a period of financial uncertainty — largely because the crisis is, now at least, of equally uncertain length. Just imagine how much you could do to calm those fears simply by
How Machine Learning is Used in the Lending Industry
Together, artificial intelligence, machine learning, and deep neural networks are a boon to businesses awash in data. Machine learning has flipped the script on traditional lending, allowing for more accurate and faster decisions by shifting traditional decision-making from analysis of individuals to analysis of trends and patterns. The result for lenders? More repeat business, and lower operational costs. These outcomes matter in a world where technology-enabled financial service is shaking traditionalists to the core. Echoing internet pioneer and fintech investor Mark Andreessen’s pronouncement that “software is eating the world,” Goldman Sach says fintech is poised to gobble a good third of the annual revenue of traditional financial-service companies. Reflecting this transformation, the global digital-lending platform market expected to approach $20 billion by 2026 for a compound annual growth rate of 19.6% through the seven years prior. In the wake of mission-critical benefits such as faster decision making, happier customers, and overall cost savings, machine learning also confers on lenders a range of ancillary leverage points including: Higher processing efficiency Streamlined compliance Efficient analysis of data in large volumes Enhanced accuracy But how does machine learning work? — and how do lenders actually use it? Let’s start on the road to answering those questions by defining some terms. Machine Learning and Artificial Intelligence Machine learning is a subset of artificial intelligence, which is a functionality (some call it a “device,” others a “process”) that takes into account aspects of its environment to make decisions or predictions, mimicking human cognition. Machine learning supports artificial intelligence. It does so by using algorithms and statistical models to perform many specific if-this-then-that type tasks virtually at once, drawing on patterns and inferences rather than explicit case-by-case instructions. In other words, machine learning takes relevant inputs (AKA “training data”) and constructs mathematical models that bring “thinking” to nuanced processes requiring multiple inputs from vast datasets — such as determining a would-be borrower’s suitability for a loan of a specific size, type, and duration A deep neural network is a method of machine learning. It functions in the realm of artificial intelligence like a dating app for data. Here’s the idea. A neural network sits between multiple datasets coming in (inputs) and decisions or predictions based on that data and the underlying training data (outputs). Its function is to apply the correct formula to the type of data in question. Cars, Motorcycles, and the Cost of Bad Decisions For example, if the task at hand is to categorize, in separate groupings, types of cars and types of motorcycles based on photographs of these vehicles, the neural network is what makes sure car pictures are scanned for the physical characteristics of cars rather than motorcycles, and vice versa. Typically neural networks process many input types at the same time (not just two, as in the example above) — which is one reason these brainy systems are so “deep.” Together, artificial intelligence, machine learning, and deep neural networks are a boon to businesses awash in data — such as lenders — because they help these enterprises identify, sort, and make accurate decisions based on multiple data points from multiple datasets, rapidly and often simultaneously. And of course, this is beneficial to businesses that are looking to distinguish themselves from competitors, digital and not, with the speed and (above all) accuracy of their loan decisions. As far back as 2015, research firm Javelin Strategy found that “false declines” — loans not granted for due to faulty data interpretation — impacts as much as 15% of US consumers and costs lenders a cool $118 billion a year. Machine Learning Takes the Guesswork Out of Lending So how are artificial intelligence, machine learning, and related concepts changing how lenders make loans? Well, let’s answer that by comparing traditional lending practices with more up-to-date approaches. Traditionally, the credit-worthiness of prospective borrowers was determined by scorecards. This approach, characterized by elevated levels of interpretability, and guided by a combination of economic theory and subjective business intuition, has several advantages, including accuracy and ease of oversight. On the downside, scorecard methodologies simply can’t handle big-data inputs. That was OK when lenders were content to sort through a limited number of data sources for information on loan applicants — things like loan applications, the lender’s internal databases, and credit-bureau scores. But now there’s a flood of additional data sources on prospective borrowers, including social networks, mobile devices, payment systems, and web activity. Using Machine Learning to meet the Challenges of Big Data “These sources are highly relevant to lenders determined to gauge the credit-worthiness of would-be borrowers with a higher degree of accuracy, but the information they want would remain locked away in datasets too vast and unwieldy to get at without help from machine learning,” says Boris Teplitskiy, head of risk at TurnKey Lender, a financial technology company that specializes in loan-servicing software that prominently features artificial intelligence. “The lending industry has a big-data problem — and machine learning, quite simply, is the solution.” And there’s more innovation in store for lenders as fintech continues to crush barriers. Augmenting Andreessen’s 2011 view that “software is eating the world,” his colleague Angela Strange, a general partner at venture-capital firm Andreessen Horowitz, recently added that fintech is doing the same, as innovative tech companies transform how companies devise and distribute financial products and services. One effect of the fintech revolution, writes Strange, is that “People who were previously hard to gauge now become new customers.” Are you currently using machine learning in your lending processes? We would love to hear how!
Retailers Face Choices When It Comes to Point of Sale Lending
In outline, POS installment loans harken back to the store-credit and “layaway” plans that householders commonly used to fund big and not-so-big purchases in the 1950s and 1960s. Unsecured lending is booming, with point-of-sale, or POS, installment-financing at the forefront of this retail revolution. In turn, POS financing — which comprises a market worth more than $400 billion annually — is being fueled by increasingly attractive eligibility criteria, disenchantment with credit cards, enhanced consumer awareness, and innovations in financial technology. Traditional retailers face eroding sales as bargain-hungry shoppers flock to the internet-based outlets and discounters they view as cheaper alternatives. But with POS lending capabilities brick-and-mortar stores are fighting back, armed with smart, user-friendly technologies backed by improved underwriting algorithms that let them close more sales on big- and medium-ticket items. In fact, a study by Hitachi Capital suggests as many as 4 in 10 consumers might not patronize retailers that don’t provide POS credit options. Golden Age POS financing, an agreement for a buyer to pay off a purchase in installments, is especially attractive to younger consumers. Millennials — with harrowing recollections of their parents’ experiences in the Financial Crisis of 2008, and a pronounced (and apparently growing) fear of compounding their student-loan debt — aren’t as likely to own or use credit cards as their elders. As a means of obtaining unsecured financing, Millennials and their Gen Z juniors view credit cards as murky, restrictive, and unduly usurous, according to the online publication PaymentsSource. Meanwhile though, these young consumers have become big fans of POS installment loans, which come with a hint of the homespun nostalgia that appeals to this demographic. In outline, POS installment loans harken back to the store-credit and “layaway” plans that householders commonly used to fund big and not-so-big purchases in the 1950s and 1960s, a period widely viewed as a Golden Age for the middle class, especially in the US. Taken together, Millennials (40.6%) and members of Gen Z (35.1%), account for more than three-quarters of POS installment borrowers in Australia, says market-research firm Roy Morgan. At the same time, POS financing is also catching on with older consumers worldwide. In this light, providing POS financing as a credit option for consumers looks like an obvious choice for retailers that plan to stay in business for a while. That said, retailers are left with the mission-critical choice of how to go about providing this dynamic financing alternative. Essentially, they have to decide whether to hire an outsourcer or do it themselves. Brass Tacks Among POS outsourcers are Affirm, Bread, Lightspeed and, most famously perhaps, Square. The go-it-alone option sounds daunting until you realize fintech companies like TurnKey Lender and nCino cater to this growing market with advanced, cloud-based lending functionality for POS financing — and in TurnKey Lender’s case, this proposition extends to small and midsize retailers of all types, not just the big guys. Backed by best-practice workflows along with advanced credit-scoring and decision analytics, Turnkey Lender offers a number of advantages over other POS solutions, including: Improved portfolio yield from technology that lets retailers optimize portfolio yield by working only with the most profitable customers along with predictive models to pinpoint optimal rates and terms. Increased operational efficiency that’s supported by artificial intelligence to enable fast and smart decisions. 24/7 IT support and customer service to answer your questions in real-time. Mobile-lending capabilities via secure web app for on-the-spot customer service whether you’re at a cash register or at the far end of a vast showroom. Affordability due to its modular structure. With TurnKey Lender, you can start small and add functionality as needed. Scalability that lets your POS-financing program grow along with your business. Another reason many retailers prefer fintech providers like TurnKey Lender over outsourcers is flexibility. Sign up with an outsourcer and in most cases, they make the rules around loan durations, financing types — loans, leases, or lines of credit — and interest rates. That makes it harder for retailers to tie purchasing incentives such as lower rates, grace periods, and promotions to their financing programs. Last but by no means least, there is of course money to be made from lending. Retailers that choose outsourcers as their partners in POS financing share the fees borrowers pay with these providers. Those that opt for a technology provider typically pay a subscription for the software-and-service package but keep the fees for themselves.
TurnKey Lender 2019 Year In Review: Achievements, Recognition, Events, and the Best AI Software in the Industry.
TurnKey Lender’s Elena Ionenko and Dmitry Voronenko on 2018 SBR’s 20 Hottest Startups Briefing
The annual Singapore Business Review’s 20 Hottest Startups Panel Briefing is one of the most representative and influential business gatherings in Asia.
