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SmartData Collective > Analytics > Using Big Data in the Online Lending Process
AnalyticsBig DataBusiness IntelligenceExclusive

Using Big Data in the Online Lending Process

Kontomatik
Kontomatik
5 Min Read
Using Big Data in the Online Lending Process
Illustration generated with FLUX.2 [klein 4B] via Cloudflare Workers AI.
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Online lenders revolutionized the way of applying for and getting a loan. All these tedious KYC procedures and scoring got much simpler and enabled people to quickly receive needed money. But online lenders are mostly willing to give customers only small loans since they are less risky and don’t require thorough financial verification of a given client.

But what if the credit companies were empowered with tools, which automatically check all the available sources of information on loan applicants – their sources of income, total yearly incomes and spending, liabilities, real estates, movables, stability of employment, and so on? That would significantly raise the credibility of the scoring and reduce the potential fraud, thus enabling higher amounts of online loans with less costs. 

API Is The Key

Well, all of this is possible thanks to APIs and data analysis. A banking API can reach customer’s bank accounts not only for figures of incomes or spending, but also retrieve the detailed data on transactions to find spending patterns, determine the scale of everyday living costs and separate them from the lifestyle extravaganza.

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The problem is that the figures are only a hard proof of the past. Account history will only tell a part of the story—the one about credibility and financial potential of a client. What about the future? Is it possible to find out about what is going to happen in the nearest time? For more informtion about banking API – check 10 questions about banking API answered.

The Past Is The Future

Unfortunately, there is no way to see the future, although prophets and fortune tellers may say otherwise, yet there is a chance of seeing trends. For example, a LinkedIn API can retrieve the data from this professional social network in order to check the stability of the loan applicant’s employment: how long he/she is employed at the current work or how his/her career path looks like. Based on the history of the employment, it is also possible to determine the chance of changing a job. What’s more, by linking this information to the data retrieved from a banking API, it’s easy to find out the salary received on each job position. This gives a real big picture of financial status and trends of a customer with no need for any paper statements, tax declarations or similar documents.

Speaking of taxes, there could be APIs using the revenue service or social security databases for a quick verification of the applicant’s credibility. One who doesn’t pay taxes or social security premiums seems a bit shady. Not to mention the fact that this behavior can lead straight to jail—and prisoners are not good in paying debts.

Looking Everywhere

There can also be more APIs to other institutions, which keep valuable data related to the customer’s welfare or potential risks. If a client has an insurance policy, an API can take a peek into it and check the sum insured or the capital raised in case of unit-linked life insurance. With the public access to the land and mortgage register, another API can check the status of the client’s real estates, especially all the mortgages. Any entries done by unpaid creditors on mortgage will be a serious warning sign for an online lender. The same principles apply to vehicle and driver registers: using an API, the lender would be able to check cars owned by the applicant and learn any limitations imposed on him or her as a driver by the law enforcement bodies (cross-referencing this information with the facts on employment can be very important).

With more and more data available online, APIs can serve as a key to valuable information for online lenders. Without piles of paper documents and within seconds, the crediting companies can verify their clients to the extent they have never had before, which leads to lower risk, bigger loans and minimized operational costs.

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