Artificial Intelligence and Big Data is Revolutionising Online Lending

Artificial Intelligence and Big Data is Revolutionsing Online Lending
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The continuous growth of digital lending has resulted in companies looking for the means to increase the profitability of their services. Big Data and Artificial Intelligence seem to be the solution.

Credit scores are the basis for which a lender considers loan applications. Credit bureaus are responsible for issuing this three-digit number. The score is obtained using payment history, credit line amounts, and credit history length, to know the likelihood of a loan being repaid.
On the other hand, Digital lending platforms think this method does not give comprehensive information on an applicant. The method used by these platforms includes other data and does not necessarily have to do with your finances.

The analysis of these additional data can make the decision-making process more accurate, give a more detailed result of an applicant and also increases the probability of making the entire process automated.

How Lending Startups are Leveraging AI
Upstart is a startup which uses machine learning algorithms in its peer-to-peer online lending. The aim is to filter through a large number of data to find patterns which human analysts may not notice.

The company aims to reduce the interest rates used for low-income earners and borrowers who are still young. They hope to provide this technology to retailers, credit unions, and banks.

Another startup, Avant, uses machine learning to analyze 10,000 data points and also identify fraud.

The Challenges
Currently, 10 percent of loans in the US and Europe are given through Digital lending, and these numbers are expected to rise. CB Insights also reports that there are efficient methods of loan applications currently in use in tech start-ups and this shows that there are advantages in the use of machine learning and analytics.

While it is a welcome development, some people do not think that the issues with online loans can be solved through machine learning. A valid argument is the exposure of a customer to risk as there is the possibility of hackers gaining access to personal information uploaded to the apps required for loan applications.

Algorithmic bias is another problem. It is possible that the decisions from a machine learning algorithm will be a reflection of the test data used to run simulations. This means that it may not produce accurate results for each applicant.

For those in support of machine learning–based loans, they still believe that it will become a key factor in online lending.

Original source CoinTelegraph

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