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.
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.
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