Peer-to-peer (P2P) lending platforms facilitate digital, online loans by matching lenders with borrowers. Instead of a bank acting as the lender, the platform connects a multitude of lenders with numerous borrowers, such as consumers or small businesses.
The lenders who could be private individuals seeking to invest their money, rather than licensed creditor providers. For the process to work, P2P platforms naturally need to process a lot of data, and this is where big data holds considerable potential for P2P lending.
Big data, AI, and machine learning could further accelerate P2P lending, giving it a truly disruptive power and transforming the lending environment for borrowers and lenders. With this in mind, here are four main ways big data is already impacting P2P lending.
1. Complete, in-depth borrower profiles
P2P loans are by nature unsecured; hence the importance of a comprehensive approval process. Banks and other traditional lenders, given their bureaucratic approach, may be more likely to exclude certain types of borrowers, including safe ones who are actually likely to repay.
This offers an opportunity for non-traditional lenders, especially investors who lend through P2P platforms. Big data enables P2P lenders or investors to become more specific and local when assessing borrowers. Depending on the platform, lenders can have access to large, complex datasets about borrowers or rely on the platform to assess borrowers by simply opting to buy into different asset classes.
Instead of relying on standard, universal risk-weights, the platform is empowered to utilize granular criteria and work to the realities of local markets as well as catering to highly individualized historical credit profiles for supporting lenders in evaluating borrowers. Ultimately, all of this could help investors make better-investing decisions and enjoy generous returns.
2. Accurate underwriting decisions to minimize defaults
Big data can be an essential foundation of new underwriting technologies that support far greater accuracy in underwriting decisions. The greater computational capacity, processing bandwidth, and storage scale afforded by big data can be used to facilitate detailed credit analysis.
These analyses can be based on diverse sources like education, academic transcripts, labor profiles, bill-payment histories, and employment history. As opposed to relying on only a credit score, underwriting decisions via P2P platforms could take into account hundreds or even thousands of data points. The analysis could also involve a correlation of data to find patterns that would otherwise be undetected.
In contrast, platforms are unable to scale up at the same level by relying only on human manual input to shift through enormous volumes of data. P2P lenders and platforms can draw upon alternative, diverse data sources and apply this data in a meaningful way, including through powerful predictive tools, to audit prospective borrowers. In turn, this can improve underwriting outcomes; in other words, identify higher-risk borrowers and minimize the risk of defaults.
3. Personalized, fairer credit pricing
Related to more accurate underwriting is the issue of credit pricing. Again, big data provides P2P lending platforms with comprehensive data analytic processes and capabilities. Lenders and P2P platforms can assess prospective borrowers at a granular level and provide accurate, fairer pricing of credit accordingly.
The outcome could be certain high-risk borrowers could be rejected where they wouldn’t have been rejected with a less granular assessment system. Other borrowers might be able to access essential loans ” albeit, at a higher price ” when they otherwise wouldn’t have been approved. However, at the same time, these credit-pricing systems should be carefully designed to avoid algorithmic bias that reinforces social biases, stereotypes, or assumptions that don’t reflect the true risk of the individual applicant.
4. Process automation and faster decisions
Big data, AI, and machine learning can be used to help automate parts or even all of the process, from the credit-history check and applicant review to application approval. Speeding up the decision-making process makes for more efficient operations for the P2P lending platform.
It also supports better service for both the investor/lender and the borrower. Lenders can quickly find borrowers they’re comfortable lending to, while likewise borrowers can be rapidly matched to a prospective lender at the right interest rates (or credit price). With this could come enhanced fraud detection and improved application security for online applications.
Disrupting traditional banking
It’s been predicted that the fintech players will eventually disrupt traditional banking in the mainstream, and lending could be one of the first areas to experience this, through P2P lending.
In the coming years, the most competitive P2P players in the sector will probably be those that fully harness the power of available big data technologies. These technologies will likely make P2P lending more efficient, profitable, and responsive to both lenders and borrowers. Governments and regulators should act proactively to protect both borrowers and investors.
