MSMEs are the backbone of the Indian economy. Yet, a staggering credit gap of $530 billion hinders their growth potential. Traditional credit underwriting methods often exclude deserving borrowers due to limited credit history. This is where iServeU steps in, offering an advanced credit underwriting solution powered by alternative data.
Traditional credit scoring often feels like an exclusive club, with entry based on past performance and a shiny credit report. This leaves a massive 480 million adult Indians under 65 locked out – a significant portion of the population! Why? Because traditional methods rely heavily on credit bureau reports and past loan repayments.
Here’s where alternative data underwriting steps in. It’s like opening a new door to the credit club, one that considers a wider picture. This approach goes beyond credit scores and historical data to include:
Employment records & salary information
A steady income source indicates the ability to repay a loan.
E-commerce Transactions
Customers spending habits can reveal financial discipline.
Government Interactions
This can show signs of stability and responsible financial behavior & etc.
By incorporating this broader range of information, alternative data underwriting allows lenders to develop more accurate credit scoring models. This translates to finally opening the doors to credit for the underserved millions in India, fostering financial inclusion and growth for everyone.
Powering Faster, Smarter Lending Decisions
- Business Rule Engines (BREs) are the brains of the operation. Think of them as the conductor of an orchestra, directing the engine based on the lender’s specific lending rules and risk tolerances.
- Design thinking principles are key to maximizing efficiency. By ensuring flexibility in the decision-making process, lenders can adapt to changing market conditions and borrower needs.
However, choosing the right engine requires careful consideration:
- Data Source Integration: Seamlessly pulling data from various sources through a single API is crucial for a smooth analysis.
Customization Options: The engine should be adaptable to the lender’s unique needs and lending requirements.
User-Friendly Interface: A user-friendly interface empowers loan officers to navigate the process efficiently.
Cost-Effectiveness: Finding the right balance between the engine’s cost and the expected return is vital.
Technical Support and Training: Robust support and training ensure the lending team can utilize the engine effectively.
By carefully evaluating these factors, lenders can select an underwriting engine that fuels faster, smarter lending decisions, ultimately driving growth and profitability.
Dynamic, Real-Time Underwriting :
Currently, a significant portion (around 80%) of the data received by underwriters is unstructured, including emails and PDFs. This unstructured data can lead to inefficiencies and delays in the underwriting process. Dynamic, real-time underwriting powered by data analytics and AI addresses these challenges. It streamlines the process by converting unstructured data into a structured format, enabling faster and more informed credit decisions.
iServeU's comprehensive suite of features empowers lenders to leverage the power of real-time underwriting:
- New Borrower Segments: Leveraging non-traditional data unlocks untapped borrower potential, expanding access to formal credit facilities.
Credit Decision Modeling: A fully graphical drag-and-drop business rules editor allows for easy design, creation, and testing of business rules, scorecards, and decision strategies. Integration of existing machine learning models implemented in industry-standard languages and tools is seamless.
Model Deployment and Execution: New or updated risk models and decision microservices can be deployed effortlessly, integrated into existing workflows, and executed in real-time.
Data Integration: Internal data sources and systems are enriched with external data providers, including credit bureaus and fraud detection services.
Decision Simulation: Risk models and decision strategies can be simulated against historical data sets, allowing for optimized decision-making.
Customized Products and Services: Analyzing diverse data enhances understanding of creditworthiness, enabling personalized lending.
Risk-Based Pricing and Custom Models: Loan origination, behavioral, collection/propensity to pay, recovery, and more can be customized, with drag-and-drop functionalities and single-click deployment simplifying the process.
Benefits of Real-Time Underwriting
- Enhanced Customer Experience: Using a customer’s risk profile, lenders can customize credit products based on individual needs, improving closure rates and customer satisfaction.
Improved Risk Assessment: AI-driven underwriting converts unstructured data into structured data, automating analysis and reducing manual errors.
Automated Documentation: Essential information is extracted from unstructured documents, facilitating risk assessment and document management.
Benefits & Growth
- Faster Onboarding: Reduce processing times and expedite loan approvals. Get MSMEs the capital they need to thrive, faster than ever before.
- Deeper Insights: Gain a deeper understanding of your credit portfolio. Make strategic decisions with confidence, backed by data-driven insights.
- Competitive Edge: Stand out in the crowded marketplace. Offer innovative lending solutions that cater to the unique needs of MSMEs.
Conclusion:
By integrating alternative data sources into the credit assessment process, iServeU empowers lenders to make better-informed decisions, expand their customer reach, ensure fairness in lending practices, offer customized financial products, set risk-based pricing models, and improve collection efficiency. Ultimately, this approach fosters a more inclusive financial ecosystem, empowering MSMEs to grow and contribute significantly to the Indian economy. With iServeU‘s advanced solutions and Loan Origination Platform, the future of MSME lending looks brighter than ever, paving the way for sustainable economic growth and development.
