How Machine Learning Will Transform P2P Lending

artificial intelligence machine learning p2p lending

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 […]

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artificial intelligence machine learning p2p lending

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:

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.

Machine learning, with its predictive analysis, can help lenders identify early the borrowers who are highly likely to default later. An early deduction of defaulters can significantly lessen the credit risks, which, in turn, reduces the total number of actual defaulter cases.

2. Hastening the Loaning Process

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
  • Easing the borrowing process
  • 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
  • Using more number of data points to better check the likelihood of the borrower repayment and estimating the time period of the lender getting the amount back

Challenges Posed by Machine Learning Algorithms

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.

Afternote

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.

The post How Machine Learning Will Transform P2P Lending appeared first on Lending Times.

Implementing an Online Lending Program: It’s Easy, It’s Quick—and It’s Necessary

online lending

We’ve all been there:  Wasting too much time navigating crowded malls and waiting on countless lines in search of that perfect gift. When online shopping became mainstream, many shoppers waved goodbye to the inconvenience associated with traditional brick-and-mortar retail, and elected to make purchases over the internet. Savvy shoppers found that they can buy the […]

online lending

We’ve all been there:  Wasting too much time navigating crowded malls and waiting on countless lines in search of that perfect gift. When online shopping became mainstream, many shoppers waved goodbye to the inconvenience associated with traditional brick-and-mortar retail, and elected to make purchases over the internet. Savvy shoppers found that they can buy the items they want—usually at better prices—without ever leaving the comfort of their home.

The dynamics that make online shopping a much more convenient and pleasurable experience are also impacting the way consumers apply for loans. Technology-savvy customers would prefer to avoid the hassle of driving to the branch and completing mountains of paper work when all these processes can easily be performed through a laptop, smartphone or tablet. New technology has inspired a whole universe of online lenders that are successfully tapping into consumer trends by offering easy access to consumer loans at attractive rates—all without the overhead of maintaining branches. It’s a reality that only continues to gain momentum. For example, American Banker reports that online lending grew a staggering 700 percent over the past five years. And, Chase Auto Finance just reported that 20 percent of consumers have already secured an auto loan online, and 47 percent indicated they would do so if the technology was available.

The choice is that stark. Online lending is not a trend, it is mainstream. Banks, credit unions, and finance companies face a simple choice in dealing with this competitive threat:  launch their own branded online lending solution to satisfy the changing demands of customers, or don’t, and watch their customer base evaporate.

Adding online lending capabilities—and creating an optimal experience for consumers—is a necessity for any traditional lender. Implementing a program is not complicated or expensive—if you chose the right solution. It shouldn’t detract from branch activities; in fact, online lending allows financial institutions to extend core value propositions into the digital marketplace. There are just three simple rules to follow:  flexibility, convenience, and security. For lenders, flexibility is essential. The freedom to change lending programs and rules in short order gives the financial institution the opportunity to aggressively compete in a crowded marketplace. Consumers, for their part, want convenience. This means expediency, simplicity, and accessibility from any device and any location. In their mind, the application is the means to an end. So, creating an online experience that enables customers to complete the application quickly and securely helps them achieve their objectives. And, all parties in the lending ecosystem require strong authentication and security tools to verify online applicants and to keep their data safe.

Lenders looking to successfully implement online lending programs must evaluate several criteria to choose the loan origination system that works best for their needs. These attributes include:

  • A configurable platform that allows the lender to create, change and manage all lending forms, rules, decisioning parameters and workflows, without having to resort to time-delaying IT resources
  • Auto-decisioning capabilities that expedite loan approval
  • Robust authentication technology (ID) that utilizes advanced algorithms and big data
    A responsive consumer portal that is optimized for mobile devices, and securely stores customer data
  • Auto-save features that securely store customer data so that customers can start an application on one device, save it, and complete it on another device when they’re ready
  • A single sign-on that is integrated with the lender’s database, enabling all pertinent personal information to automatically transfer directly into the loan application
    The ability to cross sell ancillary loan products during the approval process
  • Enabling consumers to open new accounts, such as a checking, within the loan application process
  • Facilitating upload of documents, images and other loan stipulations from scanners and camera phones

All these features and capabilities represent the bare minimum that a robust LOS platform should deliver—anything short of these will put the institution at a distinct disadvantage. On top of that, the LOS vendor should have a strong support team that is experienced in helping lenders make the transition into online lending.

There’s no better time than now for traditional financial institutions to offer their own online lending programs. The market trends are obvious; it’s time to regain market share. Online lending is straightforward, profitable, and most importantly, what the market demands.

This article was first published at TCI Credit.

Author:

As co-founder and president of Teledata Communications, Inc., Bill Nass is responsible for the company’s sales, marketing, product development and strategic third-party relationships. Bill has over 25 years of experience in the lending sector, and is actively involved with several industry associations. In addition, Bill serves on the American Financial Services Association’s (AFSA) Associate Advisory and E-Commerce Committee. Bill holds a B.A. from the University of Maryland.

Taking Mortgage Lending, Jumbo Loans Where They’ve Never Gone Before

mortgage lending jumbo loans

Alternative lending has created a new benchmark in borrower experience, especially in the consumer lending space. The fintech lending industry seems to be lagging behind in the mortgage industry, and especially jumbo loans (mortgage loan with strong credit quality where the amount exceeds conventional conforming loan limits), due to their nonconformity with set income and […]

mortgage lending jumbo loans

Alternative lending has created a new benchmark in borrower experience, especially in the consumer lending space. The fintech lending industry seems to be lagging behind in the mortgage industry, and especially jumbo loans (mortgage loan with strong credit quality where the amount exceeds conventional conforming loan limits), due to their nonconformity with set income and credit patterns. Neat Capital is a Boulder, Colorado-based alternative mortgage lender that understands the massive market opportunity the above issues represent. It is focused on creating a digital lending platform for mortgages that is fast, reliable, paperless, and value-accretive for borrowers.

Streamlining Mortgage Lending Underwriting

Founded in 2015 by Luke Johnson, Chad Lewkowski, Christin Price, Ryan Brennan, and Steve Herschleb, the company wanted to deliver a modern approach to mortgage lending centered on making it simple, unique, and transparent.

Underwriting and loan documentation is considered a back office process in traditional banking. Neat Capital is trying to bring this core activity online and looking to capture the loan documentation, underwriting, and loan selection process in one single online session for the client. This facilitates a hassle-free experience for the client and better conversion rate for the startup. It has been able to bring down the entire cycle to 13 days as compared to the 30-60 day norm in the jumbo loan industry.

The company has raised a total of $4.2 million in two funding rounds. Its angel round saw an investment of $2 million. But the company had to face a major crisis in December 2016 and was on the brink of shutdown before it could recover. At the same time, Luke Johnson, CEO and founder, faced a personal tragedy with his wife battling brain cancer. But the employees, investors and other stakeholders stuck together and fought hard to become the fastest lender in the U.S. for jumbo loans.

How Neat Capital is Different From Most Mortgage Lenders

The traditional mortgage lending process is recursive in nature. It involves sending information and documents to underwriting, following up with clients for clarifications, and if the underwriter does not like it, it results in rejection or a change in terms. The whole process is susceptible to getting bogged down on a regular basis, which leads to delays and surprises. So the secret sauce for Neat Capital is to break down this unproductive cycle and provide certainty at the outset in a single online session. The company is unique because it can evaluate a loan in real-time according to a very detailed underwriting guideline and with a high degree of accuracy due to its proprietary artificial intelligence algorithms.

Another USP is its ability to handle borrowers with complicated income streams, net worth, and credit who can’t be analyzed on normal mortgage parameters. The company funds from its balance sheet, but it resells loan into the market almost immediately to free its balance sheet for expansion.

Neat Capital is focused on high quality credit with a weighted average FICO (Fair Isaac Credit Organization) score of 766 and weighted average LTV (Loan To Value Ratio) of 72%.

Neat Competitors and Customers

Alternative lending has seen traction with players like Better Mortgage and SoFi targeting the same clientele. But Neat Capital believes there is a huge addressable market, and it is incumbents like Wells Fargo that are its biggest competitors.

Its typical customer usually has an owner-occupied unit in San Francisco. It also has clients looking to buy second homes or investors looking to buy houses as a real estate play. But it is not restricted to any particular category and covers all conventional mortgage options, as well.

The Future of Mortgage Lending

The mortgage industry has gone online and the application process has moved entirely onto digital platforms. The winner of the market will be the player who can execute the entire loan application process in one single session versus the current scenario of requiring multiple sessions for loan application closure. Also, the industry needs to be ready for a smartphone future where the first and only point of contact between the platform and the borrower would be a smartphone. The application engine needs to be smartphone-powered so that the platform is not losing clients to other smartphone-ready peers.

The company’s future plans are to cover the entire spectrum of conventional Fannie Mae loans to jumbo loans. Instead of focusing on yield expansion or going down the credit quality ladder, the company will aim to concentrate its bets in niches where it believes that other lenders have mis-priced the risk.

Neat Capital also needs to grow while educating clients and referral partners, wealth managers, real estate agents, and employers about why they are different and what is their unique selling proposition. Currently, the company operates in nine states, but it is planning to double that number in 2018.

Conclusion

Neat Capital has focused on a market gap in mortgage lending that has been overlooked by the alternative lending industry. The challenges due to non-conforming loans and income streams & net worth not falling under typical lending patterns made it difficult for players to successfully compete with traditional banks. Neat Capital seems to have solved this problem. A 13-day turnaround for an industry that usually sees transactions taking months to close will definitely revolutionize the market.

Author:

Written by Heena Dhir.

Lending Times Survey Results

digital lending services

Lending-Times recently conducted a survey of our readers to find out more about the types of lending services they offer and how they relate to their customers. The following are the results of the survey. What type of lending services do you provide? (select all that apply) The majority of readers (55.88%) are in the […]

digital lending services

Lending-Times recently conducted a survey of our readers to find out more about the types of lending services they offer and how they relate to their customers. The following are the results of the survey.

What type of lending services do you provide? (select all that apply)

The majority of readers (55.88%) are in the consumer loan business followed by 36.76% involved in business lending. 17.65% are in mortgage lending while 16.18% are in student lending 10.29% are involved in auto lending. Another 25% identify as alternative lenders, a broad category of lending that includes many types of non-bank loans. Because readers could choose more than one category for this question, the survey results do not add up to 100%.

What is your role within the organization?

The largest percentage of survey takers (25%) fall into the digital sales, marketing, and acquisition category. 11.76% fall into risk, fraud, and compliance occupations, and another 10.29% consider themselves a part of product and technology. The majority, 52.94%, chose “other.”

How do you verify the identity of your borrowers?

When it comes to identifying borrower identities, 37.31% said they do so through data bureau checks. Digital identity verification checks are used by 32.84% of those who took our survey, and 23.88% said they verify borrower identities with a manual review of identity documents. Only 5.97% said “other.”

How do you collect supporting documents for underwriting (for example, utility bills for proof of address, W2s for proof of income, etc.)?

Regarding underwriting practices, 69.74% of survey takers said they collect documents through electronic capture and upload, 25% by email, and 5.26% have borrowers deliver to a physical location. No respondents said they receive documents by fax.

Do you think your current process for onboarding new applicants could be improved?

A simple yes or no response on this question revealed that 93.24% of survey takers believe their new applicant onboarding processes can be improved while only 6.58% responded in the negative.

What stage of the digital transformation journey is your organization at today?

Almost half, 42.11%, of survey respondents said they are a fully digital organization, and the same percentage said they are on track to becoming a fully digital lender. Those just starting out represent 19.74% of our readership who said they are working on a full-digital strategy and evaluating vendors. None of the respondents said they have no plans to become a fully digital lender.

What do you think are the main barriers to offering fully digital lending services? (select all that apply)?

The majority of survey takers (53.42%) said the biggest barrier to offering fully digital lending service is mitigating risk while avoid loan application abondonment. Another 52.05% said meeting compliance without compromising the user experience is the main barrier. Almost one-third of survey takeres (27.40%) said they lack the skills, resources, and budget to offer fully digital lending services. Respondents who said they do not see the value of shifting their loan origination practices to digital channels registered at 9.59%, and those unsure of where to begin came in at 5.48%.

Rank on a scale from 1-5, the value of each benefit in the digital lending process (1 being very valuable, 5 being not valuable).

Our readers seem to value regulatory compliance more than any other digital lending benefit. Risk mitigation followed closely behind followed by improvements in operational efficiency. Cycle time and user experience pulled up the rear.

Do you feel your lending user experience is a competitive differentiator?

84% of survey takers said the user experience on their lending platforms are a key competitive differentiator while 16% said it wasn’t.

If you already offer digital loans, which of the following options do you provide?

Among survey takers, the digital lending options provided the most include desktop/laptop (52.11%), mobile-optimized website (50.70%), and native app (15.49%). Over one-third (39.44%) said they offer all three options.

Authors:

Allen Taylor