Here’s How Alternative Credit Scoring Can Make It Easier for the Poor to Get a Loan

Credit access allows people and firms to invest in their human capital and businesses, which has the potential to alleviate social inequality and boost economic growth get Consolidation Now it today.

On the other hand, commercial banks frequently have limited enthusiasm for lending to vulnerable communities.

Due to a lack of consumer data or history to assess creditworthiness, such as the poor, women, and small enterprises. Due to a lack of alternatives, these people frequently turn to unregulated channels, despite the high-interest rates and risk of being exploited.

According to Mohan Jayaraman, managing director of Experian Asia Pacific’s innovation and strategy, a lack of data has historically been a barrier for banks and financial institutions in extending credit to the unbanked, and hence an impediment to attaining financial inclusion. He emphasizes using alternative data as a tool for lenders to conduct credit assessments and tap into opportunities at the bottom of the pyramid.

What is alternative credit scoring, and how does it work?

Alternative credit scoring refers to using data from digital platforms and applications to measure credit risk. Before the advent of credit bureaus, lending firms relied only on them for consumer credit data. The usefulness of merging data from many sources such as airtime, mobile money, geolocation, bill payment history, and social media usage is highlighted.

The most prevalent alternative data sources are telecommunications companies (telcos) and utilities. Travel, payments, e-commerce, government activities, and asset holdings are some of the other sources. Alternate data can comprehensively evaluate a borrower’s credit risk profile by displaying an individual’s preferences and behaviors.

How does machine learning go about analyzing different types of data?

Using machine learning to analyze alternative data differs from using static data to analyze specific credit-related data from a bank. For credit scoring, machine learning offers a higher predictive potential. Micro-segmentation from thousands of segments can be accommodated, and micro patterns can be determined regularly. Machine learning improves acceptance rates and reduces credit losses by using more nonstandard data points.

What is telecom data grading for cash loans, and how does it work?

Telco data is highly beneficial in estimating a person’s risk level without a credit history. Telcos can potentially gather a vast amount of data, including:

  • Geolocation 
  • Data Use
  • Top-Up History
  • SMS Patterns
  • Demographics

To detect tiny patterns, machine learning can generate telecom scores. Raw call detail records, for example, can be translated into behavioral patterns to correlate with risk, resulting in lead creation for lending firms. The use of telco data and machine learning can help banks better understand their customers. Improved credit acceptance rates can increase sales by 15%, bad debt can be reduced by 5% with better exposure management, and processing time can be cut in half thanks to automated judgments.

What are the components of alternative data?

To bring other data models to life, several components are required. Consumer permission and collaboration-based models will be the de-facto standard in the new world.

Financing companies that wish to target “no formal credit” customers should use deeper understanding and data points to make faster, more reliable decisions. Formal credit scoring models use eight to ten variables on average. On the other hand, alternative data credit scoring can use more than 500 data points.

Alternate data is made up of the following components:

  • Data
  • Analytical method
  • Platforms
  • Business models that work together

What role does alternative data play in the future?

With the proliferation of digital interfaces or digital interface points with consumers, alternate data is on the rise. The considerable cost reduction in computer power and data storage is another driving factor.

Only roughly one billion people in Asia have access to formal credit out of a total population of 4.6 billion. The sharing of financial information through open banking allows the digital footprints of more than three billion people without credit histories to be followed, thanks to the high market penetration of mobile phones and the growing expansion of e-wallets.

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