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How a Credit Scoring Model is Built using AI, ML & Big Data

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One of the marvel of technology is credit-worthiness is now being based on Artificial Intelligence, Machine Learning and Big Data. Read on to know more…

If you are worried about your low credit score due to missing the payment of your last credit card bill or when your EMI cheques have bounced — you don’t have to worry. You can still get a loan from one of several new-age lenders who have devised their own Artificial Intelligence or AI-powered algorithms to assess customers’ credit-worthiness.

With due credit to the Information Technology, credit-worthiness is now not being based on traditional metrics such as the credit score of a professional or organization sourced from rating agencies like Cibil, Equifax or Experian, bank transactions and assets. Instead, the scores of other unconventional parameters are being looked at through technologies based on Artificial Intelligence (AI), Machine Learning (ML) and Big Data. Through this process, technology is bringing about a sea change in the lending business.

Assessment Factors
The various factors for the assessment of credit score could be what an applicant spends on alcohol, the responsiveness in paying insurance premiums on time or the frequency at which he or she has changed the jobs. Even something like how fast a person fills up the 12-question online application form (the platform automatically records the time) has a bearing on whether or not the loan will be approved. The speed of form-filling shows how desperate a borrower is to get the loan.

The credit score assessment developers have actually coded in rules that review how frequently the borrower spends on alcohol, whether he or she is on a gambling website, or if he or she pays health insurance premiums on time. All these factors are assessed through AI, ML and big data to determine the credit profile of a borrower. Currently, India has a dozen of “digital” lenders that is fastly becoming a trend and laying the foundation of tech-enabled banking.

Big Data
The urgency to offer loans to customers who do not have any collateral to offer has prompted innovators to look at new sources of data on them. ‘Unprecedented amounts of Indian consumer data is being generated through web search, social media, e-commerce and banking’. The BCG report points out ‘For enterprises, more than 25 data points such as corporate filings, tax data, legal records and directors’ details are now electronically available,’

The report adds that lenders are leveraging big data to reduce the cost of consumer acquisition, improve underwriting models and establish early-warning systems. With financial services increasingly moving in this direction, big data is likely to be one of the key pillars of the digital lending boom in India. This has led to the proliferation of proprietary credit risk assessment and underwriting platforms.

Challenges
The use of Artificial Intelligence, Machine Learning to analyze alternative data in loans and credit rating is going to raise some privacy, ethical, and legal concerns. Many people might not feel comfortable with a company having access to all of this sensitive information about their life. Even if all these companies behave ethically, the more data they hold the more that can be stolen by malicious hackers in a data breach.

Conclusion
The era of basing decisions solely on credit scores from bureaus are over. Today custom models work better and more accurately since they use data from a number of sources both internal and external to assess creditworthiness.

The insights, AI, ML and big data have helped create scoring models for businesses, lenders, and organizations. While traditional credit bureau reports are also crucial, AI and ML can go further with scoring models helping them add insights and provide newer business points of view. On big data, more the data the more accurate and efficient the scoring model becomes.

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