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From managing credit risk to algo-based underwriting: Tech is changing lending

new-age financial companies are leveraging a host of algorithm-based models for optimizing lending and underwriting decisions

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CIOL Bureau
New Update
AI in making lending easier and profitable

From managing credit risk to algorithm based underwriting, there are numerous factors that lie beyond a conventional lending and loan application process. Underwriters must verify various aspects to handle credit risk optimally. Let’s analyze these factors more closely:

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Credit History- The credit history of an applicant is one of the most pivotal features that are verified by underwriters before managing credit risk. Usually, it is desirable to have a credit score between 700 and 800 as it is considered positive. This increases your probability of being passed as a safe applicant with a clean history without any repayment issues or evasions. 

Work Experience- This is another important parameter that is considered before loan underwriting entities pass a loan application. Banks and NBFCs often analyze an applicant’s employment history to make sure that he or she possesses a dependable source of income. 

Income- This is another aspect upon which financial companies tend to deliberate. As previously noted, banks evaluate one’s income capacity with relation to present debt obligations, dependents, source, and the time period. Therefore, one of the foremost factors cogitated by banks and finance companies is whether an applicant exhibits an adequate surplus after paying off their EMI dues. 

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Repayment- As an applicant your preference matters. If you select a shorter repayment period, your chances of getting loan approval are much greater. This is because banks tend to favor loan applicants with short-term repayment terms. 

Collateral- When you apply for any loan, the collateral that you offer can play a major role in achieving fast and hassle-free loan approval. With the loan amount merely being a percentage of the evaluated worth of the collateral, a high-worth asset could imply an increased credit sanction at the applicant’s disposal. 

Margin Money- Usually, banks are ready to finance almost 80% of the cost of the loan intention and the borrower is expected to arrange the remainder sum. The downpayment that you deposit plays a significant part in deciding your eligibility for any loan, whether home, education, car, or a business loan. 

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In majority cases, the loan value is mostly based on the probability of an individual or a company to repay back the amount. This helps in predicting the chances of an individual committing loan default. This cutting-edge predictive analysis is highly significant for the entire financial sector. However, even with substantial data at hand as there is always a chance that the data presented may be inaccurate. 

There is always the likelihood of an individual or firm being fraudulent. Therefore, a plethora of financial players are optimising new-age technologies such as Artificial Intelligence and Machine Language for predictive risk assessment to detect potential loan defaulters. Several new-age financial companies are leveraging a host of algorithm-based models for optimizing lending and underwriting decisions. This next-gen financial model relies on the utilization of both real data and simulated data to create a realistic view of an applicant’s loan repayment credibility and credit risk management. The exponential advancement of AI and Big Data have catalyzed the rise of a novel financing module that calls upon these emergent technologies to calculate the creditworthiness and the digital footprints emanated by a prospective applicant.  

So much so, a bulk of the Companies/Banks/NBFCs operating across the financial spectrum have begun assessing a potential applicant’s entire lifestyle pattern along with the comprehensive digital footprint left by the applicant. This helps in corroborating beforehand if an applicant is likely to default or not. This has led the emergence of a new breed of “alternative data” regarding potential borrowers. While the additional data not only offers extensive insight into individuals with proven CIBIL scores, it can also be utilized for evaluating the creditworthiness of people without traditional credit history due to inhibited financial access.

The article is authored by Rohit Garg, Co-founder and CEO, SmartCoin

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