Blog Article BACK Digital Lending for Users and Businesses: Trends, Challenges, and Forecasts Andrew Zola Senior Contributor Digital nomad and storyteller July 23, 2019 One study aiming to explore where U.S. citizens take out personal loans has found that traditional banks issue fewer than 40% of all loans in the country. Another 40% of all issued loans account for fintech companies. Back in 2010, they held less than 1% of the market share. As more fintech startups tend to build their products and value propositions around crediting solutions today, more VCs and angels are ready to invest and support their ideas financially. Last year, startups that work at the intersection of technology and lending accounted for at least a quarter of all venture capital investments in the financial sector. Let’s see what digital lending solutions are available on the market in 2019 and how they disrupt traditional lending. Peer-to-peer (P2P) lending Today, there are dozens of P2P platforms that help connect lenders and borrowers. Some of them show an annual growth rate of 100% or more. There are already established leaders among these platforms including both good-ol’ companies like Lending Club and relatively new players like Lufax. P2P lending has several growth points. First, there are separate markets like China, Indonesia, and other Asian countries, where access to bank capital is highly limited for citizens. Second, it is P2P as a business model (aka crowdlending or crowdfunding). It allows businesses to avoid lengthy banking approval and creditworthiness assessment/credit scoring procedures and to obtain money as quickly as possible. An example of such solutions is Funding Circle, a platform that was used to fund dozens of startups and small enterprises in 2018 alone. 8 Things To Consider Before Jumping On Fintech App Development Bandwagon There are several technological trends in P2P lending. The primary ones are increased transparency and risk reduction for lenders. For example, the Ripio Credit Network’s blockchain platform that operates in Latin America allows for the seamless signature of smart contracts between borrowers, lenders, and guarantors. Smart contracts ensure two mechanisms of creditor protection–the liability of the guarantor and the pledge. The borrower loses them in case of violation of loan repayment terms. P2P platforms also take on the function of a more accurate assessment of the creditworthiness of borrowers. A California-based Upstart uses machine learning algorithms that analyze and compare customer data. This makes it possible to determine the reliability of the borrower’s information and the ability to recover funds based on customer data with a similar profile. Machine Learning In FinTech: From Manipulation Detection to Stock Market Price Predictions The technology also eliminates extra fees when making a loan using the P2P model. Some startups, such as BTCPOP and Credible Friends, allow people from anywhere in the world to borrow in cryptocurrencies without paying additional fees for conversion and international transfers. Some banking institutions have already started offering P2P lending solutions. German Commerzbank, Ukrainian PrivatBank and American Goldman Sachs have launched their P2P services. Also, South African Lebashe Financial Services invested in building a proprietary platform to offer lending solutions. Such products allow banking institutions to retain their share in the personal loan market. Check out 8allocate’s custom FinTech and crypto solutions development cases: Our Work Crypto loans Blockchain and cryptocurrencies are an important trend in fintech today. According to Forbes, 20% of the top fintech startups develop solutions in this area. They use digital assets such as cryptocurrencies and tokens as payment and investment tools such as Ripple and Xapo. Some digital lending platforms act as creditors themselves. Among the best-known ones is Salt. It connects lenders and borrowers, but, unlike fiat lending, it uses cryptocurrency as a pawn. Upon fulfillment of the contract between the lender and the borrower, the latter receives its assets back. Read how Skycoin built a dedicated FinTech development team in Ukraine (Golang) for their ongoing blockchain development project. In July 2019, BlockFi raised $50 million, which will be used to issue crypto loans. The platform accepts BTC and ETH and issues fiat money for those who urgently need to withdraw fiat, but are not ready to part with their “cyber-storage”. Blockchain Development: Epic Talent Shortages Despite Unprecedented Demand Digital assets can serve not only as a pawn but also as a loan. Some startups create new business models for lending in cryptocurrencies. A German service Bitbond allows the borrower to get a loan in bitcoins regardless of their physical location. Thanks to this approach, the startup was gradually turned into a full-fledged neobank. Automated lending and funding solutions Many fintech startups focus on underwriting automation. If we go back to the Forbes list of top startups, we can find several major players developing this technology. One of them is Kabbage. This startup specializes in providing automated lending solutions to small businesses. To assess the creditworthiness of applicants, it uses the same data as banks but does so with the help of automated systems that accelerate and streamline the process. The clients themselves provide the company with access to the necessary financial information. Kabbage analyzes this data and makes an informed decision. Another example is Tala. Thanks to it, the unbanked people in developing countries can rely on microloans ($100-$500). The primary tool of Tala is its mobile application. Once installed on a smartphone, it analyzes various user data upon their permission, from financial transactions to mobile gaming activity. Based on this analysis, an automated assessment system decides whether to provide an applicant with a loan or reject an application. Users typically get an answer from Tala within just 10 minutes. Modern financial technologies also allow for the automation of other credit processes such as debt collection. This is what startup TrueAccord does. With the help of machine learning technologies, it compares the profile of each and every debtor with the profile of thousands of other users and predicts how each particular debtor will react to notification messages received through a particular channel. The startup takes the whole interaction between debtor and lender to the next maturity level, as it allows choosing the best way of communication between them and defining what notification messages will be most encouraging for each user to pay the loan on time. Banks, in general, are also interested in automation. According to McKinsey, most of them are only at the beginning of their fintech journey and don’t know yet that up to 30% of all manual transactions can be automated right now. If we look at the agenda of most of today’s fintech events, automation of everything from underwriting and user creditworthiness assessment to documentation flow takes center stage. The future of digital lending There are various forecasts for the future of digital lending. Some believe that in the future, we’ll be able to apply for a loan or mortgage with a selfie or fingerprint, while others believe there’ll be no brick-and-mortar banks at all. While many different scenarios are possible, one thing is clear–there are so many narrow niches which can be disrupted with digitalization and automation that fintech startups will have what to focus on in the months to come. One such prospective area is the so-called E2E lending, i.e., when employers provide their employees with personal loans on more favorable terms than banks. It becomes possible thanks to the integration of corporate payment systems with the payroll systems. Detailed profiling is another big trend for the future. And that’s where financial technology will come in handy. Loans are becoming available to a growing number of users and businesses of different backgrounds and size, so the ability to first collect required user or business data and then translate it into informed lending decisions will become crucial very soon. The assumption is that most of the investor dollars will go to this area shortly. And what’s your take on this? 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