WHY WE NEED ALTERNATE CREDIT SCORING IN GROWING MARKETS?
The race to attract new fintech customers is highly competitive, especially when considering the 1.4 billion unbanked individuals globally, as estimated by the World Bank. This is where alternative data scoring becomes relevant, offering a way to validate borrower credibility using data beyond traditional credit scores or rental payment histories.
Alternative credit scoring allows lenders to assess the financial behavior of potential borrowers through their digital activities. Without relying on traditional credit score data, lenders can reach unbanked customers, thereby increasing profits, especially in the face of the upcoming economic downturn.
From my experience and market knowledge, I see alternative data scoring as one of the most crucial fintech trends of 2024. For instance, it played a vital role in Django Stars’ creation of Molo Finance, the leading digital mortgage broker in the UK, and Money Park, Switzerland’s largest digital mortgage lender.
What is Alternative Credit Scoring?
People today leave digital footprints through various activities like mobile data usage, utility bill payments, social media interactions, and mobile app transactions. Timely payment of utility bills, for example, often goes unnoticed by credit bureaus but is a critical factor in alternative credit scoring as it shows financial discipline.
These alternative credit data sources help businesses create detailed digital profiles of customers, including those with low traditional credit scores, such as immigrants, students, new workforce entrants, first-time homebuyers, and rural farmers.
Alternative data credit scoring enables lenders to verify applicants and serve the unbanked population, helping them start building successful credit profiles.
Implementing fintech solutions with alternative credit scoring involves collecting data from multiple sources and formats, standardizing it, and processing it with AI algorithms. Though challenging, the benefits of using alternative data sources for credit scoring outweigh the difficulties of implementation.
Traditional vs. Alternative Credit Scoring
Traditional credit scoring relies on past banking, credit card, and loan transaction data. Credit bureaus like TransUnion, Experian, and Equifax record and analyze this data and provide it to lenders for a fee. First-time borrowers like students, recent graduates, and many immigrants with thin credit files often struggle to get their first loan approved.
While factors like homeownership and significant bank savings are good indicators of loan feasibility, many people do not meet these criteria despite being capable of repaying loans.
Alternative credit scoring models evaluate individuals based on additional parameters, such as:
Using alternative data for credit scoring offers better insights into the status of borrowers with short credit histories, allowing lenders to offer them better rates than competitors and still make a profit.
How Does Alternative Credit Scoring Work, And What Data Does It Use?
Lending involves three key aspects: stability, ability, and willingness. Applicants must demonstrate they are financially stable, able, and willing to repay loans. When there’s no traditional credit history, alternative data scoring can help.
Positive spending habits, like paying for a mobile carrier, short-term rentals, online subscriptions, and utility bills, indicate financial responsibility. For instance, Millennials and Zoomers often avoid credit cards and long-term loans due to the 2008 financial crisis, resulting in short credit histories despite significant earnings and spending.
Lenders can use contact information from loan applications to access customers’ social media profiles and other digital accounts. This data, gathered through API calls, helps validate a customer’s credibility and can replace traditional credit history.
Digital footprints provide an accurate picture of a customer's spending patterns. When traditional credit data is lacking, alternative data scoring can help onboard customers previously overlooked by traditional banks.
Processing alternative data involves gathering and standardizing unstructured data for analysis, requiring investment in data analytics, risk management, ML model training, and AI integration.
How to Collect Alternative Credit Data
There are two main methods to collect alternative credit data:
1. Custom-built solutions by an IT service provider.
2. Integration with existing data providers like Experian via API.
Django Stars has used both methods, noting that while integrating an existing solution via API is initially cheaper, custom solutions may be more cost-effective in the long run. Some fintech products cannot integrate with data aggregator platforms, necessitating custom solutions using AI to handle unstructured data.
Compliance with data privacy and security regulations, such as GDPR, CCPA, PSD2, open banking, and certain FTC regulations, is crucial when handling alternative credit data.
Processing Alternative Credit Data
Manually reviewing vast amounts of alternative credit data is impossible, so AI algorithms and ML models are essential. AI can quickly analyze large datasets and identify patterns, even in unstructured data. Combining traditional and alternative data sources creates a more comprehensive understanding of an applicant’s financial behavior.
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According to FICO’s research, combining traditional and alternative data enhances the accuracy of credit risk models, enabling lenders to validate credit-invisible individuals and clients with thin or no credit files.
AI and ML for Data Processing
AI and ML allow lenders to analyze traditional consumer credit files and enrich them with real-time insights from social media, mobile payments, online purchase histories, and more. API integrations access customer data from credit bureaus and other providers, forming cloud-based systems that include features from AWS, Google Cloud, Microsoft Azure, and third-party data aggregators.
BENEFITS OF ALTERNATE CREDIT SCORING FOR BORROWERS AND LENDERS
The primary benefit of alternative credit scoring is accessing the pool of credit-invisible customers, expanding lenders’ customer base, which is crucial during a recession. It also helps first-time borrowers overcome thin credit files.
Alternative credit scoring looks at real-time payment patterns, supplementing consumer credit scores with up-to-date information. This includes recent rent payments, subscriptions, telecom data, eCommerce spending, mobile purchases, and bookings.
Additional benefits include offering better interest rates than competitors, faster and cheaper credit underwriting using AI, and improved customer experience.
More Accurate Creditworthiness Assessment
Assessing creditworthiness at the time of loan application is important for accurate risk profiling. Alternative data provides real-time insights, helping lenders form a holistic view of a customer’s credit profile and identify fraud faster.
Improved Customer Experience
Automated alternative credit scoring models reduce loan origination time and cost, allowing lenders to offer more competitive rates. This creates a positive customer experience and helps attract competitors’ clients.
Increased Market Reach
Reaching the 1.4 billion under- and unbanked individuals offers a significant opportunity. Alternative credit scoring can reveal creditworthy customers among first-time borrowers, opening new revenue streams for lenders.
REAL WORLD EXAMPLES OF SUCCESSFUL ALTERNATE CREDIT SCORING IMPLEMENTATION
A report from the Hong Kong Monetary Authority shows a steady increase in alternative credit scoring usage across the US, Europe, and Asia. Examples include community development financial institutions (CDFIs), the EU-based CRIF (Credit Information and Finances), and the US-based Small Business Financial Exchange (SBFE).
CDFIs
Community development financial institutions in the US and UK serve smaller customers outside the scope of traditional banks. Advances in technology have enabled CDFIs to increase outreach and underwriting speed, and they can act as third-party data providers for loan assessments. Django Stars used this approach in developing Molo Finance and Money Park, integrating with third-party services like CDFIs to validate customer credibility.
CRIF
CRIF is an EU-based company specializing in credit information solutions. After implementing PSD2 compliance, CRIF used alternative credit scoring methods to engage new market segments. This helped an Italian banking group evaluate creditworthiness using financial and alternative data, leading to improved up-selling and cross-selling of banking products.
SBFE
The Small Business Financial Exchange, an independent US financial body, provides credit reports to small lenders using a combination of internal and external data. This helps lenders build accurate credit risk models and quickly assess applicants’ creditworthiness at a low cost.
The Future of Alternative Credit Scoring
Alternative credit scoring is growing, providing a way for lenders to reach under- and unbanked populations. It will be especially vital during the upcoming recession, as attracting and retaining customers is crucial for survival and growth.
Combining traditional and alternative credit data allows lenders to make informed loan decisions based on real-time spending habits. This benefits both B2B and B2C segments, creating new revenue streams for fintech businesses and providing financing access for customers without traditional credit scores.
Implementing alternative credit scoring features gives fintech companies a competitive edge by serving unbanked populations. However, it requires significant investments in risk assessment, AI, and ML, making it crucial to evaluate the feasibility based on company strategy, market landscape, customer expectations, and resources.
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