Alternative Credit Scoring for Financial Inclusion

In India, a significant portion of society falls into the credit-invisible category, hindering their access to traditional financial services. This is primarily due to factors such as a lack of bank accounts, cash-based wages, and an absence of credit history. The traditional credit scoring system, relying on conventional records, often excludes deserving individuals from accessing loans. This exclusion forces many to turn to alternative, often exploitative, lending options. To bridge this information gap, there is a growing need for alternative credit scoring, utilizing diverse data sets and advanced analytics.

The Need for Alternative Credit Scoring:

Example: Mr. Ranu's Dilemma

Consider the case of Mr. Ranu, a hardworking laborer in Delhi with no traditional proof of income, credit history, or collateral. Traditional lenders may deny him a loan based on these factors, ignoring his sincerity and steady employment record. Alternative credit scoring can address this gap by leveraging diverse data sources to create a more comprehensive and inclusive credit assessment.

Utilizing Alternative Data:

1. Diversifying Data Sources:

• Alternative credit scoring incorporates data from digital platforms, mobile wallets, geo-location, bill payment history, and social media usage.
• By considering non-traditional data points, lenders can gain insights into an individual's financial reliability beyond what is reflected in a credit report.

2. Machine Learning for Predictive Analytics:

• Machine learning algorithms enhance predictive analytics in banking by analyzing alternative data.
• Using ML, lenders can assess employability indexes, analyze spending patterns, and scrutinize borrower behavior to determine creditworthiness.

3. Geo-Data Integration:

• Integration with platforms like Google's API allows the collection of geo-data to understand an individual's residence, movement patterns, and potential job opportunities.

Benefits of Alternative Credit Scoring:

1. Financial Inclusion:

• Alternative credit scoring models aim to include the nearly 50 million consumers excluded from traditional credit scores, fostering financial inclusion.

2. Access to New Borrowers:

• Lenders can tap into new borrower segments by utilizing alternative credit scores, expanding their lending universe without significantly increasing risk.

3. Precise Credit Risk Assessment:

• Alternative credit data provides a more holistic view of a borrower, allowing lenders to make more accurate credit risk assessments and automate decision-making processes.

4. Customization and Automation:

• Lenders can customize offers based on alternative credit scores, improving automation and efficiency throughout the customer lifecycle.

Future Trends in Alternative Credit Scoring:

1. Continued Investment in Alternative Data:

• As digital touchpoints expand, the use of alternative data is expected to rise, with ongoing investments in diverse data sources and advanced analytics.

2. Machine Learning Advancements:

• Advances in machine learning will contribute to more sophisticated credit scoring models, improving accuracy and adaptability to changing consumer behaviors.

3. Collaboration with Credit Bureaus:

• Collaboration between alternative credit scoring providers and traditional credit bureaus can lead to comprehensive credit assessments that benefit both lenders and consumers.

FinTech Opportunity: Redefining Credit Scoring for the Future:

1. Overcoming Traditional Limitations:

• Traditional credit scoring models are outdated and pose limitations, especially for underbanked regions and younger generations. FinTech has the opportunity to redefine these models.

2. Embracing New Data Sources:

• FinTech companies can leverage alternative data sources, including social media profiles, online behavior, and utility payment history, to build more inclusive credit scoring models.

3. Addressing Fraud Risk:

• FinTech solutions can incorporate advanced APIs, machine learning, and real-time data to address fraud risk, especially in the growing digital lending landscape.

4. Customized Models for Specific Populations:

• FinTech firms can develop customized credit scoring models tailored to specific populations, such as self-employed individuals, millennials, or those with limited credit history.

The landscape of credit scoring is evolving, and FinTech holds the key to unlocking new opportunities. Alternative credit scoring, powered by diverse data sources and advanced analytics, addresses the shortcomings of traditional models and promotes financial inclusion. As FinTech continues to innovate, the future of credit scoring looks promising, offering tailored solutions, automation, and a more accurate assessment of creditworthiness for a diverse range of borrowers.