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.

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