Artificial Intelligence, fondly known as AI, is the approach and research field of creating machines that are able to replicate human intelligence. Although we aren’t yet teeming with robots as intelligent as humans, we have managed to build: Intelligent programs that can beat reigning Go world champs Virtual assistants like Siri and Google Assistant to […]
With the passing of time, more and more fields are benefiting from AI and machine learning (ML). The latter is a specific form of AI that deals with devising strategies and methods for allowing a machine to learn from the available and historical data, all to find better solutions for existing problems.
Among the various markets growing by leaps and bounds with machine learning is marketplace lending. AI and machine learning have enabled this sector to automate a variety of processes, shrink operating costs, and make life easier for both lenders and borrowers.
In the past two decades, there has been a tremendous surge in people interested in learning ML as a hobby as well as a professional career choice. Before diving deeper into the subject of how machine learning will transform P2P lending in the future, let’s first get ourselves a brief understanding of ML.
The Craft of Machine Learning a.k.a. ML
Machine learning, or ML, is a subfield of artificial intelligence that follows the notion that machines can learn and adapt better from experience rather than from extensive programming.
Although machine learning is a subset of AI, there are several notable differences between AI and ML. Hence, both fields are considered to be distinct. This analogy is followed by deep learning and machine learning too, where the former is the subfield of the latter.
Today, we are surrounded by many instances of ML, varying from Apple’s Face ID and facial recognition in general to navigational and shopping recommendations generated by virtual assistants. ML also had a profound impact on the financial industry, especially the lending sector, which we are going to discuss in the upcoming sections.
The Present Impact of Machine Learning on P2P Lending
The ongoing evolution of technology and the emergence of tech innovation is having a greater impact on the financial services industry. From designing impactful business models and enhancing the customer experience to cutting costs, the benefits are many.
Fintech firms and the Internet are providing platforms to even niche products to get interested financial supporters as well as potential customers to grab their side. Peer-to-peer financing isn’t only about crowdfunding, but also includes P2P lending.
A bank offers P2P lending by allocating capital from the lending party to the borrowing party while acting as the broker, as well as the risk mitigator. The higher the risk, the higher is the imposed interest rate. Fintech organizations have made this simpler and more effective.
Several Fintech P2P lending firms like LendingClub and Prosper offer a platform where individuals gain interest by lending money to the ones who require it. These firms make money by taking a small fee for making the connection among the two parties possible.
As an alternative to submitting a traditional application, borrowers present compelling stories about why they need the capital via fintech platforms.
Other than the aforementioned, here are some of the most important benefits presently enjoyed by the P2P lending industry thanks to adding the art of machine learning to its arsenal:
1. Easy Identification of Defaulters
As the financial sector grows, so does the number of defaulters and bad loans. This has led the financial institutions to be more careful than ever to spot and avoid defaulters.
Of course, lending is one of the most complex business processes. This is the prominent reason why it is a long and tiresome process to accomplish. Thanks to its intuitive analysis, machine learning can remove the redundant parts and, hence, speed up the whole process.
An automated workflow offers lenders a competitive edge against the competition as there is less space for human error. Thus, lenders can process mammoth workloads in shorter time periods. This helps them not only to maximize profit, but also to better serve their clientele.
3. Lessening/Eradicating Errors
Lending involves a lot of documentation. From the very early stage of loan origination to underwriting, most of the steps involved demand preparing and verifying several documents. Doing it manually not only increases the overall time required but also makes it prone to errors.
Machine learning comes in handy here by adding automation to the process, prompting for human intervention where required the most, and thus, streamlining the complete loan cycle.
4. Reducing Operating Costs by Automating Several Aspects of the Lending Process
There are several aspects contributing to the operating costs of a financial institution. Among them, the most notable aspect is loan decisioning. In most cases, the loan arrives at a positive lending decision irrespective of the loan amount.
Machine learning is able to automate the process, consolidate the entire data, and process the same by taking into account a number of factors. Moreover, ML performs a credit check and enhances the overall experience and quality of the process while reducing the overall costs.
5. Simplifying the Whole Process
The traditional way of lending is a very complex process. Hence, it is obvious for lenders to look for options that can simplify the conventional lending workflow (i.e. decreasing the total time required, lowering the overall cost involved, and smoothing out the whole process).
Machine learning helps in simplifying the whole lending process by:
Automating various aspects of the business process with customizable rules engine
Ensuring straightforward processing of loan applications
Standardizing the entire process
The Upcoming Impact of Machine Learning on P2P Lending
With time, artificial intelligence and machine learning are getting bigger and better. This is evident by the fact that there’s a continuous rise in candidates interested in learning AI in general, and ML in particular.
Although machine learning has already started revolutionizing the way P2P lending works, it will continue getting better and more beneficial for the craft. Following are some projections about how ML will impact lending in the near future:
Ability to self-modify with little to no requirement for human intervention
Better means of filtering irrelevant data from relevant data
Completely automating the underwriting process
Even faster application, approval, and funding processes
Helping lenders to see the bigger picture by insights drawn using efficient ML algorithms
Increasing the outreach of lenders and identifying potential lenders
Introducing younger people to the lending-borrowing culture
More stringent safety protocols to safeguard sensitive information
Offering insightful decisions with a higher probability of success
Providing capital to more and more honest borrowers
Recognize patterns in a specific dataset to help build effective business models
Although machine learning provides access to a variety of avenues for financial institutions, it still needs to do the heavy lifting in some areas to further improve. Three of the most important aspects of ML requiring effort are discussed as follows:
1. Concealed and Undesired Bias
Bias is an inseparable aspect of machine learning algorithms. No ML algorithm can exist bias-free. Although a small degree of bias can be easily managed, the bigger it becomes, the more difficult it gets to fix the same.
Chances for machine learning algorithms to develop concealed and undesired biases from the same data used to train them do exist. Hence, human intervention becomes absolutely necessary to ensure that apposite suggestions or predictions are made by the ML systems.
2. Undetected Errors
Machine learning aims to minimize human interference as much as possible. Hence, it kind of makes humans dependent on machines. Most times, they are free from errors. However, there is room for some errors to slip through.
In case of errors that the machines aren’t able to correct themselves, human intervention becomes mandatory. However, manually identifying and correcting complex algorithms can be arduous and time-consuming.
As lending is an important aspect of the economy, even the smallest of errors being unintentionally passed through can propagate through the system and lead to a huge, undesirable consequence.
3. Time Requirement for Developing Precise Predictions
Machine learning algorithms rely greatly on historical data. The accuracy of the predictions developed by ML algorithms is directly proportional to the total time they spend interacting with the data. Therefore, making precise predictions instantly isn’t possible.
It is mandatory to feed historical data as well as the new data to the ML system continuously for ensuring that the predictions produced by the same are accurate and reliable.
There is no denying the fact that the advent of artificial intelligence and machine learning has completely altered the way the lending sector works today. It has become faster, bigger, and better than ever with continuous improvement.
Borrowers and lenders, irrespective of the capital involved, aim to have as few formalities as possible. Though it seems unachievable with the traditional form of lending, the technology-backed lending strives to move as much closer to it as possible.
Fintech firms leverage the latest technological innovations to offer a quicker, smoother, and reliable experience to the lenders and borrowers. There are lots of challenges faced by the industry as of now. Developing better machine learning algorithms might be the key to solving them all.
News Comments Today’s main news: Virgin Money to launch a challenger bank. Equifax partners with Entersekt on digital ID authentication. 1st loan originator in UK joins Mintos. Citi drops $75M into Pagaya. IOU Financial extends Midcap credit facility. Today’s main analysis: Global fintech VC investment sets new record. Global marketplace lending investment in 2017. Today’s thought-provoking articles: Goldman Sachs’ plan […]
In a glass-walled tower in Utah’s capital, hundreds of Goldman employees are building what amounts to one of the world’s most ambitious consumer-finance startups.
Their address, 111 Main St., stands as a symbol of the changes afoot inside the firm, better known as an elite adviser to big companies and billionaires. Struggling to make money in the postcrisis world, Goldman is pushing into businesses it once dismissed as pedestrian and gimmicky, assembling a suite of banking products for the middle class it hopes will power growth.
Goldman 18 months ago began making online loans of a few thousand dollars under the brand Marcus, named after founder Marcus Goldman. Individuals once needed $10 million to get the attention of Goldman’s elite private bankers. Today, customers can open a Marcus savings account with as little as $1.
LendingTree said Sacramento ascended to the top of the list in a study that included data from more 80,000 queries submitted by new small business owners seeking loan offers through LendingTree’s small business loan marketplace to determine where businesses tend to do the best.
Sacramento was one of three California cities on the 10-best list, joining Fresno at ninth and Los Angeles at 10th. Following Sacramento on the list were Grand Rapids, Mich; Portland, Ore.; Knoxville, Tenn.; Denver; Seattle; Tulsa, Okla; Albuquerque, N.M.; Fresno; Los Angeles; and Oklahoma City, respectively. Los Angeles and Oklahoma City tied for 10th.
Cincinnati topped the list of the 10 worst cities to start a new small business. No California cities were on the 10-worst list.
For instance, four of the top five loan payment methods are now electronic, and 21 percent of millennial investors use a robo-advisor service to make investments.
Affluent Consumers and Financial Advice
Human interactions remain an important part of financial advice, especially for the 34 percent of consumers with at least $100,000 in household investable assets. Fifty-eight percent of these affluent consumers work with a financial advisor. Among those without an advisor, only 11 percent report high interest (8-10 on a scale of 0-10) in using one. At the same time, 32 percent of affluent consumers who invest their own money grade their knowledge and expertise as a “C” or lower, suggesting an opportunity to bridge the gap with a hybrid of human and digital advice.
Among all consumers who invest on their own, only 8 percent use a robo-advisor service. However, use of such a service is much more likely among millennials (21 percent) and urban consumers (18 percent).
Rates, Fees and Service Prevail
Topping the list of selection factors among those with at least one loan are interest rates (83 percent) and low fees/service charges (83 percent), followed by customer service (75 percent), company reputation (70 percent), and knowledge of staff (65 percent). Sixty-five percent of consumers say prior experience with a lender is important.
Many consumers expressed willingness to try new ways of interacting with their lender, if there’s a benefit. For instance, if it makes the loan process faster, more than half of consumers would be willing to use a mobile device to e-sign loan documents (56 percent), take and upload photos of loan documents (54 percent), and verify their identity with a photo (51 percent). Forty-two percent of consumers indicate they would be willing to provide access to their financial information by providing their credentials to other online banking applications, up from 32 percent in 2016.
Digital channels, especially mobile, are now leading ways of communicating with a lender, although context matters based on the interaction. A lender’s mobile app is the preferred way to check when a next loan payment is due (21 percent), check the balance term (20 percent) and request a payoff (17 percent), among consumers who have conducted each of these activities in the past six months. For account questions, consumers significantly favor speaking live with a representative via phone (21 percent) over using an automated voice response system (12 percent), e-chat (11 percent) or the mobile app (11 percent).
Marketplace lending as an industry is hitting its stride. Some platforms are becoming profitable, some are diversifying, new players are entering the market with new business models and the competition is heating up. But that means banks need to start strengthening ties with their online lending partners.
As more consumer-facing fintech companies are learning, that’s best done by building products that make people’s lives easier.
More than 75 percent of fintech executives surveyed in a new report said their primary business objective is to collaborate with traditional firms, such as banks and insurance companies. Only 18 percent said the main goal was to compete with the established players.
According to the World FinTech Report 2018 from consulting firm Capgemini and corporate networking website LinkedIn Corp., most of the startups are likely to fail if they don’t build partnerships, despite raising more than $110 billion since 2009. The survey, published Tuesday, was based on the responses of 110 global financial technology firms.
Varo Money has been targeting customers of big banks whose fees they’re tired of having to understand and pay. Despite its appeal to potential customers to switch to Varo, its ads don’t call out specific companies, as some of its peers do.
If you have a low balance and you’ve spent a lot of money, Erica might warn that you are in danger of overdrawing your checking account. Or she could share opportunities to save additional money.
Wells Fargo has made providing customers with advanced digital tools a top priority. In February, its 17 million mobile users with consumer deposit accounts found themselves with a new predictive banking feature.
Wells Fargo confirmed that these new mobile capabilities are powered by Personetics, a company providing banking solutions that anticipate what consumers might need in the future. Personetics also powers Royal Bank of Canada’s free automated savings tool, NOMI Find & Save, which gives mobile banking customers customized tips and alerts.
Companies like Saylent are trying to help banks make sense of their data resources by identifying the customers they should focus on. Saylent gives customers tools to target people that are shopping for a car loan or a mortgage. The platform will be used by institutions like BankFirst Financial Services, a community-based institution headquartered in Mississippi.
Bank of America plans to open more than 500 branches over the next four years as part of a large-scale investment in retail banking.
The $2.28 trillion-asset company said in a press release Monday that it will hire more than 5,400 employees as part of the expansion. The Charlotte, N.C., company did not specify where the new branches will be located, nor did it say how much the proposed brick-and-mortar expansion plan would cost.
Among federal bank regulators, the Office of the Comptroller of the Currency has been the most active on fintech chartering options. But another agency, the Federal Deposit Insurance Corp., may provide crucial guidance for fintechs in the shorter term.
The FDIC still has pending an application by Square for an industrial loan company, a limited-purpose bank typically chartered in Utah that receives deposit insurance.
A lot what’s being called crowdfunding is actually more like matching funds or subsidies for down payments. The growth of these options seems to be a sign of the times — so few people can afford to buy homes nowadays that the industry has gotten creative.
Unison Financial (formerly known as Rex Home Buyer) offers down payment subsidies in exchange for equity stakes in the home. The program requires that the home buyer put up a down payment of at least 10%.
HomeFundMe provides incentives for individuals to seek out grants that are actually matching funds on down payments. Although the match ratio is impressive, two-to-one, the total grant limited to $2,500 — do the math and you see that the buyer would need to come up with another $5,000 at that maximum amount.
With most residential mortgage lenders requiring minimum down payments of at least 5%, that limits the buyer to homes worth no more than $150,000. That’s well below the average home price in the U.S. — and even beneath affordable housing program prices in many cities.
PawnGuru, an online marketplace connecting pawn shops and consumers, today announces the close of a $2.5 million Series A. With this funding, PawnGuru intends to expand its network of shops within the US, as well as to international markets, giving consumers worldwide the power to buy directly from local pawn shops online.
Think Big/Start Big Syndrome – You are permitted to think big but start small to have adequate fund to invest in other areas of the business. When you don’t properly handle these areas, your business might join the 90% businesses that never survived after 5 years.
Lack of Financial Mentorship
Inability to Utilize Viable Loan Options – Bank loans, equipment loans, invoices financing, car title loans, peer-to-peer lending networks and more, are avenues small business owners can obtain loans. It’s however pertinent to get information and evaluate the cost implications of taking a loan to finance your business.
Under-utilization of Digital Technology – In terms of advertising, marketing, automation, time management, human resource functions, cloud computing, data management, blockchain technology etc. digital technology has infused speed and efficiency which has resulted in reduced cost to carryout daily business operations.
There are almost 1.2 million international students currently studying in the United States. They hail from countries all over the world with almost a third – more than 360,000 – coming from China and just over 205,000 coming from India. South Korea and Saudi Arabia follow behind dropping down to just over 70,000 and 55,000, respectively. With education costs often approaching six figures and beyond, an international student loan ecosystem has emerged both in the U.S. and abroad to serve the educational funding needs of this demographic.
From his home office in Fishtown, Temple University grad Mason Gallik, 23, is hoping his college debt calculator can help others from making bad choices.
“It’s about being realistic about your decisions,” said Gallik, the founder of LoanMajor. “Sometimes it’s smart to look at college from a financial side and not just an emotional one.”
Currently, the company’s source of income is through affiliate links with loan marketplace Credible. For every visitor that LoanMajor leads to Credible, they get a fee. Another source of revenue Gallik hopes to set up is through affiliate links to credit card companies and banks.
U.K.-based lender Virgin Money said it will offer current accounts and savings products.
In its earnings call today, Virgin Money said it will begin testing these products later in the year and has already spent £38.3 million ($53.3 million) over the past year developing this digital bank.
1pm PLC said Tuesday that it has entered into a cooperation agreement with AS Mintos Marketplace to be a loan originator on its online loan marketplace.
The AIM-listed financial services provider to UK businesses said that it is the first loan originator from the UK to join the Mintos marketplace, which already has about 30 other loan originators globally.
Unfortunately, in the current marketplace many opaque structures lead to charges that even a Finance degree can’t help unravel. But technology is here to help and most of the new Robo-Advisors have simple and transparent fee structures enabling savers to compare different product offerings quickly and easily.
Whilst many in Financial Services have been critical of the growing ‘regulatory burden’ the changes MiFiD II will bring should be net positive for end users and ultimately society. Although legacy providers are likely to see revenues and margins shrink.
Pagaya Investments, a Fintech company in the asset management space, has received $75 million in debt financing from Citi. Simultaneously, Pagaya announced the creation of the “Opportunity Fund” to meet growing institutional interest in consumer credit as an asset class.
Fintech financing rose 18 percent in 2017, to US$27.4 billion, with the value of deals in the U.S. jumping 31 percent, to $11.3 billion. Deal values almost quadrupled in the U.K., to US$3.4 billion, and soared nearly fivefold in India, to US$2.4 billion. The number of fintech deals also rose sharply, from just over 1,800 in 2016 to nearly 2,700 in 2017, underscoring continued appetite from investors scouring the globe for innovation in insurance, banking and capital markets startups.
“Much of the growth, particularly in the U.S. and UK, has been driven by big new investment flows from China, Russia, the Middle East and other emerging economies,” said Julian Skan, senior managing director in Accenture’s Financial Services practice.
“Much of the growth, particularly in the U.S. and UK, has been driven by big new investment flows from China, Russia, the Middle East and other emerging economies,” said Julian Skan, senior managing director in Accenture’s Financial Services practice.
India, US, UK drove global growth
Kabbage Inc, a U.S. online lender for small businesses, alone raised US$900 million in three separate rounds in 2017. Online lender Social Finance Inc, also known as SoFi, raised US$500 million in February, and LendingPoint raised US$500 million from a credit transaction in September. As startups grow and their businesses mature, funding rounds have increased in size, while some companies have opted to use credit facilities to speed up their expansion.
In the U.K., digital insurance distributor BGL Group raised US$900 million, pushing overall fintech investments in the country to an all-time high of US$3.4 billion. Payments venture TransferWise had the second-largest fundraising in the U.K., raising US$280 million.
India’s digital payments startup Paytm received US$1.4 billion in venture capital, helping drive fintech fundraising activity in the country to nearly five times the 2016 levels. The number of fintech deals in India increased 65 percent over 2016.
More deals in China, fewer megadeals
Mega fintech deals that had catapulted China to the top destination in the world for venture capital money in 2016 fell in 2017, as investors pulled back after pouring billions of dollars into giant-sized transactions. Fintech funding in the country declined 72 percent in 2017, to US$2.8 billion, from a record US$10 billion in 2016, when several companies – including Ant Financial and wealth management platform Lufax – had multi-billion-dollar financing rounds. The average deal size in China in 2017 was US$19 million, down from US$186 million in 2016, though the country still had large transactions, such as the US$440 million that real estate broker Homelink raised in April and the US$290 million that online finance firm Tuandai raised in June.
Deals in the sector slowed down in 2016 with a year on year decrease of 12.8 per cent, possibly as a result of Lending Club’s annus horribilis. Total amount invested fell from $8.6bn in 2015 to $7.5bn the next year.
However, investment rebounded in 2017 to reach $8.9bn, a year on year increase of 18.6 per cent. The top ten P2P and marketplace Lending deals in 2017 raised half of the total funding for the year,raising a combined total of $4.4bn. The largest deal in 2017 was the previously mentioned $1.2bn Series B round to Lufax, led by COFCO with co-investment from China Minsheng Bank and Guotai Junan Securities.
In response to this, two reputed fintech innovators, Gluwa and Aella Credita have joined forces to launch Creditcoin, an inter-blockchain P2P lending market that operates across distributed ledgers ensuring permanent record of transactions that cannot be alter or tampered with.
Financial technology startup C2FO raised $100 million in funding in a new round led by the investing arm of global insurance and asset management giant Allianz SE as well as Abu Dhabi’s Mubadala Investment Co.
The Australian Small Business and Family Enterprise Ombudsman, FinTech Australia and the Bank Doctor, an SME advocate, will drive start-ups to improve disclosures that will allow small business customers to compare total costs, understand obligations and penalties if payments are missed, and ensure disputes are dealt with quickly and fairly.
In its ‘Consultation Paper on Peer to Peer Lending’, the RBI highlighted how these web-based platforms are providing easier access of credit to small entrepreneurs by bringing prospective borrowers and lenders together. With more individuals lending to one another, interest rates for borrowers are going down, even as the increased availability of affordable credit stimulates greater financial activity and drives business growth. As a result, consumer segments such as MSMEs – until now either com ..
Borrowers from tier-2 and tier-3 cities comprised 20% and 17% of the total number of loans disbursed. New-to-credit borrowers comprised 35% of fulfilled borrowers on the platform, while those with poor credit ratings accounted for 10% of the overall number. Most strikingly, an analysis of credit bureau reports revealed how only 2.5% of the borrowers from tier-3 cities who received funds from the platform got any loans from other banks or financial institutions after the Faircent loan, underlining the major credit gap that the online platform is plugging within the economy.
Indonesian peer-to-peer lending platform UangTeman said it is set to raise a Series B financing round by mid-2018, claiming it would be one of the largest such rounds for a fintech firm in Southeast Asia.
IOU FINANCIAL INC. (“IOU” or “the Company”) (TSXV: IOU), online lender to small businesses (IOUFinancial.com), announced today that it has modified and extended its secured credit facility (the “Credit Facility”) with MidCap Financial, (“Midcap”) until December 31, 2020. The amount of the Credit Facility is USD $20 million, with a term portion equal to USD $15 million and a revolver amount of USD $5 million.
IOU and Midcap have further agreed to allocate USD $1 million from the Credit Facility amount of USD $20 million, to support Canadian loan originations. This will be formalized in a separate amendment to this facility.
Automation is the great equalizer when it comes to competing with giant lending companies. Predictive analytics isn’t new. Machine learning isn’t new. However, data science can be complex and not something the average non-scientist online lender can manage easily. That’s changing due to a new autonomous approach to predictive analytics using artificial intelligence as a […]
Automation is the great equalizer when it comes to competing with giant lending companies.
Predictive analytics isn’t new. Machine learning isn’t new. However, data science can be complex and not something the average non-scientist online lender can manage easily. That’s changing due to a new autonomous approach to predictive analytics using artificial intelligence as a core technology allowing lenders to reduce the development of actionable and valuable metrics from months to days.
DMway Analytics provides an autonomous predictive analytics solution powered by machine learning that enables subject matter experts without data science knowledge and experience to build their own predictive models in a fraction of the time it takes traditional models. Here’s how they do it.
Democratizing Predictive Analytics
“We started a couple of decades ago,” said Gil Nizri, CEO of DMway Analytics. “Back then, a lot of algorithms were created for the financial industry. Even then, many data scientists realized that one day we’ll be able to automate algorithms and create algorithms in a couple of clicks.” But it took a while before demand for the technology caught up with the scientific developments that make it possible. “Democratizing predictive analytics took a couple of decades because it’s complicated. Now, what was done by human data scientists can be done by a machine.”
DMway’s mission is to level the playing field for small lending companies by making predictive analysis easy and available to non-scientists who are subject matter experts in their business specialties. One of their verticals is alternative lending. They also serve the financial services, marketing, insurance, telecommunications, and utilities industries.
Some of the questions predictive analytics can answer for online lenders include “who is likely to default on their loan,” “how many people will default this year,” and “who is a good credit risk?”
“Not everyone who works at a lending company has knowledge of predictive analytics, data science, or algorithms,” Nizri said. “Some lending companies simply can’t afford to hire people who can build complicated algorithms and adjust them as needed. That puts smaller lending companies at a disadvantage when competing with larger lenders like CitiBank, Wells Fargo, and other legacy institutions.”
With that in mind, DMway Analytics created a solution that allows small lenders to compete with large lenders in an area that is increasingly more essential to successful lending. By equalizing the playing field, they are democratizing predictive analytics.
Problems Solved By Predictive Analytics
Nizri breaks machine learning for predictive analytics down to three key technological techniques:
Classification – When you want to classify a small population among a larger population. Who will likely pay on time? Who will likely default the loan? Classification involves any use case that fits into that family of problems.
Expected Value – When you want to predict the future, what is the expected value of the thing? For instance, the lifetime value of each customer and what interest rate should be charged for each individual. These can be solved by the DMway algorithm.
How Many Times an Event Occurs – Identify the event you want to count—for instance, loan defaults—and count the number of times it happens within a given time frame.
Once and algorithm is created based on a lending company’s criteria, it becomes automated so that loan application decisions can be made almost immediately, Nizri said. The process can also reduce fraud prevention. If a company can count how many times fraud occurs, and under what circumstances, they can devise a strategy to prevent it. These three predictive models can address 90 percent of the problems lending companies face, according to Nizri.
“By simplifying the creation of predictive models, any loan expert can do this without knowledge of data science complexities,” Nizri said.
DMway Origins and Business Model
DMway officially launched in January 2016 with $1 million in seed money from JVP Media Labs. Prior to that, the company bootstrapped itself from conception to funding. The funding allowed them to go to market with their first product, but there were alpha versions prior to 2015. Since the initial seed round, the Israeli Office of the Chief Scientist (now called Israel Innovation Authority) has invested a couple of hundred million dollars into the startup, as well, giving DMway a huge boost.
The predictive analytic solution is sold as a subscription. Companies pay an annual fee based on the number of users. While lenders are the primary target market, not all customers are lenders. The solution can predict other company data, as well, Nizri said.
Nevertheless, financial services startups were the first companies to adopt DMway’s technology, by design. Because they need to compensate for less manpower, the automated models wrapped up in the solution saves them money and makes them more competitive.
“As a startup you don’t have a lot of capital,” Nizri said. “That’s why fintech companies were the first to adopt.”
After one year, DMway has 10 lending company customers. Most of them are on the higher end of mid-size, Nizri said. Some of them turn over a couple of billion dollars per year. Among the list of clients Nizri mentioned are Direct Finance and Backed Inc. Companies use the platform to predict lending trends, loan default probabilities, and fraud. DMway also provides full underwriting automation and loan approval through its platform.
“When the entire process is done by machine learning algorithm, you can handle a lot more loan applications in a better and more secure way, then you can mitigate risk better than when loans are processed by human underwriters,” Nizri said.
DMway’s co-founders include Nizri, CEO; Professor Jacob Zahavi, chief analytics officer; and Dr. Ronen Meiri, chief technology officer. Zahavi was the first person to ever discuss machine learning algorithms and has been working with them for over twenty years. Nizri is a veteran evangelist of predictive analytics.
How to Be A Competitive Lender
Most predictive analytics tools are developer tools meant to be used by data scientists, but for small lenders who do not employ data scientists, predictive analysis may be out of touch.
“We are removing every barrier of entry for the world of predictive analytics,” Nizri said. “One of those barriers is the need for data science, machine learning, and predictive analytics knowledge. Users of our product do not need any of that knowledge.”
The DMway platform mimics the way a human scientist works and generates a state-of-the-art visual in about three minutes. Nizri said it’s as good as any human-made algorithm. The platform generates out-of-the-box reports and interfaces data science with business users so the non-scientist can better understand the root causes of problems and how to mitigate them. To prove his claims, Nizri benchmarked his company’s algorithm against human-made algorithms and found them to be as good as or better in every controlled situation.
“It’s more than automation,” he said. “It also includes intuitive, heuristic algorithms along with knowledge based on four or five decades of study by a large number of data science veterans. It would take any data scientist 10 or 15 years to reach that level of knowledge and experience. ”
It typically takes a human scientist three to 12 months to create a predictive analytics model and complete a project. By the time a company reaches a conclusion based on the data, it’s no longer relevant. By speeding up the process using machine learning algorithms, DMway levels the playing field and makes predictive analytics more relevant for everyday uses. Nizri believes that once his company has paved the way, other companies will enter the playing field to challenge them.
“If you currently provide loans and your algorithm is bad, it will take your data scientists 3-6 months to improve it, then you are in deep trouble,” he said. “You’ll underwrite bad loans everyday. With DMway, you can have a state-of-the-art algorithm and provide great loans to the marketplace and start profiting within two to three days.”
The Future Belongs to Machine Learning
While machine learning isn’t new, what is new is the rapid pace at which it is forcing innovation in financial services. More and more alternative lenders are implementing machine learning technology into every part of the process, from loan application review to underwriting. As more companies adopt machine learning technology, the more necessary the technology becomes to remain competitive. It is even more important for small lenders because every minute and dollar they can save on the process makes them more level with lending giants and ensures they remain alive in the marketplace.
“Even Lending Club is small compared to the giants,” Nizri said. “The difference they have is the ability to be agile, flexible, and creative. This is what machine learning algorithms give you.”
Nizri sees an effective machine learning algorithm as the difference between alternative lending leaders and run-of-the-mill players. In order to survive, lenders will have to have the best tools available. It’s more important than human talent, which will likely go to the larger companies that can afford to pay their salaries.
For Nizri, augmented analytics is the future, and he is proud to be the head of a company on the forefront of the avant garde. To remain competitive and grow as he sees the company doing, he’d like to see more startup funding and a strategic partner.
“That will help us boost our global sales and offer more value as an industry leader,” he said.