The risk that your borrower brings

Over the past few years, artificial intelligence started making inroads into financial sector be it lending or trading. 
According to CB Insights, financial technology firms closed funding 496 deals worth $8 billion in 2016, a record high.
And needless to say many of these firms are using some form of data analytics to provide the solutions that they provide — be it general purpose companies such as Opera and Kensho or credit research firms such as Affirm and Avant or insurance sector start-ups such as Lemonande and Cyence..
In the present case, we will focus on lending segment. 
Through artificial intelligence it is possible to make the process of judging whether a borrower is going to honour the loan significantly insightful and easy.
This will be done by letting the machine, in our case a computer or a web server, read all data that is available on past borrowers. 
Our algorithm will then learn how to segregate good and bad borrowers on the basis of their repaying habits. 
It will also glean out the most important characteristics that affect whether or not the borrower pays back on time.
This algorithm takes these fields as input:

  • Employment length
  • Grade of the loan
  • Type of home ownership
  • Term of the loan

… and tells you whether the borrower is worth lending too in terms. 
Our algorithm has been trained on the Lending Club dataset, now widely used by people around the world to benchmark their solution.
In our case the performance is given by the graph below. Though it may seem gibberish to many it essentially tells how good are we in predicting whether a loan will bad or not, a metric called the non-performing asset in India.
The key characteristic that this graph depicts is that the test and training error stabilises after fewer number of iterations.

The accuracy of our algorithm is 50.3% in a limited dataset i.e. 35,000 rows of data

Similarly, using this algorithm can help banks and non-banking finance companies to monitor the risk emanating from present borrowers and gauge the repaying ability of thin- and no-file customers, who have come to the bank for their first loans.
Although this sounds tailored for the lending segment, the algorithm in its foundation is a classification process that is able to tell the good from the bad, the better from the best and so on.

Therefore, it can deployed in other sectors also such as insurance and telecom.
In case of the former, by analysing consumer data and policy features for an insurance aggregator we can help them recommend the best policies personalised according to the needs of the consumer. This will help reduce cost of lead generation and significantly improve the probability of customer acquisition.
Take for instance a company called Coverwolf, which is an insurance aggregator, we can recommend the best policy for a user by matching his requirements with what the policy can offer.
A person say in living in the south-Delhi neighbourhood of Saket earning 600,000 rupees a year wants to buy a health insurance policy. 
We can match this person with a policy on Coverwolf that includes hospitals in his neighbourhood and has a premium outgo that is light on his pocket.
But this will only be the start. We can also optimise the recommendation further on the basis of whatever data the user is keen on providing. The more data sources, the better. 
If you are checking to much into bars on Facebook, we can push policies that covers illnesses related to alcohol consumption to the top.
This will, however, also push up the premium price because of the risk increases.

​In the telecom sector, classification can be used to detect frauds on the network. Our algorithm can be used to learn from call detail records where and why there are network glitches. This can help telecom companies detect, localise and isolate problems on their networks.

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