Cutting-edge technologies and valuable data insights enable financial service providers to provide customized financial services to their clientele, for example, credit availability at the touch of a finger, digital IDs, biometrics, facial recognition, etc.
Fintech companies have brought significant innovation to credit systems with innovative forms of lending like microlending and peer-to-peer (P2P), easy accessibility, and customer-centric approaches.
Digitized services have already boosted financial inclusion in the economy. With the increasing digital footprint, fintech can cover a broader market to access. They can serve the individuals with lower income and employees in the unorganized sectors through digitized tie-ups with their employers.
There is another aspect also to consider for the reshaped credit landscape – augmented credit scoring scope. Let us look at it:
Credit models in a financial system have specific metrics to consider while determining a client’s credibility to repay different types of loans (mortgage, personal loan, car loan, etc.). These metrics give a credit score to the individuals. Based on this FICO (Fair Isaac Corporation) score, lending institutions determine the eligibility of an individual for a loan.
- FICO score is a three-digit number that estimates a person’s creditworthiness by correlating financial behaviors to credit risk.
Majorly considered metrics for FICO scores (range from 300 – 850) with weightage are as follows:
- Repayment history: An immense weightage of your credit score, 35%, is assigned to your debt treatment – how you have paid your bills or EMIs.
- Repayment burden: 30% of your credit score depends on your total debts. It shows your spending practice and if you can afford to repay a debt.
- Length of credit history: A borrower with a long credit history can add up to 15% to their credit score. Most lenders prefer longer credit history. No credit history decreases the chance of getting any credit from financial institutions.
- Types of credit: A secured loan, student loan, car loan, etc., support your credit score up to 10% compared to a huge credit card loan only.
- Credit frequency: Borrowers are suggested to apply for new credit only if they need urgent finances. A lender determines whether you need funds or you are a habitual credit seeker with the loan frequency. A frequent borrower may lose 10% of their credit score with multiple loans.
The current credit model based on these metrics has some limitations. It restricts the system from the bigger picture. It is not very objective and is pretty biased in favor of people who have maintained excellent and long credit history. An individual involved in cash transactions only, a student with no credit history, an individual who has missed just one EMI, or an immigrant from another country with no such credit records can not be a part of the system. There is a need for authentic data analysis with a 360-degree view of an individual’s profile.
This is also where the integration of finance with technology can play a key role. Technology can make things possible with less friction and hassle.
- Combining all the financial data available about a borrower and then deciding on approving or declining the loan application.
Big data and artificial intelligence can help in conducting credit research differently and reshaping the finance ecosystem by developing unbiased assessment metrics.
Credit history is an important set of data, but it is not the only data that helps decide on credit repayments.
- Trended credit data can provide up to a 20% improvement in predictive performance as compared to a generic credit scoring model. Trended data refers to expanded criteria by leveraging 24 months of an individual’s past financial status, payment habits, and credit utilization.
Instead of viewing a snapshot of credit data within a specified period, the use of trended data makes it possible to see the big picture as it emerges. Thus, analyzing this brings the opportunity in the changing credit landscape to extract more meaningful statistics to foretell future values.
Knowledge Partners is one of the leading market research firms serving financial institutions. As the rise of alternative data has brought a paradigm shift in the capital markets ecosystem, you can re-imagine the credit analysis process with our technology and institutionalized credit knowledge, gained in more than 18 years. We assist financial institutions in centralizing, standardizing, and optimizing loan and treasury operations so that our clients can formulate high-quality business operations frameworks in the changing credit landscape.
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