Superflow Credit Underwriting: A Look Through
In recent years, there has been a significant transformation in the lending industry. Fintech companies have gained more customers while traditional lenders have lost some. Despite having higher interest rates compared to regulated lenders such as banks and NBFCs, fintechs have been more successful in expanding their loan portfolios.
The main contrast between traditional lenders and Fintech companies lies in the speed at which loans are disbursed. With digital underwriting, Fintechs have managed to reduce the turnaround time from hours or days to just seconds in some cases. In contrast, traditional lenders often take several days to a few weeks to approve and disburse loans. This means that Fintechs are able to offer much faster turnaround times, which can be measured in hours or even minutes.
Fintechs have been able to achieve faster turnaround times by adopting decision engines instead of relying on traditional risk analysts. By leveraging the power of digitization, Fintechs have been able to expand their market reach and eliminate paperwork, making the loan application process quicker and more convenient for customers. Essentially, Fintechs have adapted to changing times and embraced technology to provide a faster and more efficient loan application process.
How does a Decision Engine affect turnaround time?
Assessing the creditworthiness of a borrower in a traditional lending business involves a time-consuming and laborious paper-based process. First, the borrower must apply for credit and provide various documents such as PAN and Aadhar card copies, bank statements, and cash flow statements. These documents must then be manually reviewed by loan officers, a process that can be lengthy and tedious. The loan officers must assess the applicant's creditworthiness by considering various factors, including their income, credit history, and financial statements. All of this manual processing can result in delays in loan approvals and disbursements, making it a cumbersome process for both the lender and the borrower.
Acquiring necessary loan information can take days or weeks, leading to borrower dissatisfaction. The lender then verifies data against databases, gathering credit history and credit scores for assessing credit risk. This step can add more weeks, straining lender-borrower relationships.
A decision engine digitizes the loan application process, eliminating paperwork and communicating directly with relevant parties to obtain data. This reduces acquisition time from weeks to seconds. The engine assesses creditworthiness and risk based on lender-set rules, arriving at a decision within minutes.
A powerful decision engine can cut down lender turnaround time from weeks to minutes.
"Understanding the Components and Functioning of a Decision Engine in Lending"
A decision engine is a rule-based system that allows lenders to configure underwriting parameters and apply them instantly to every borrower.
In addition to standard rules like pin codes, age, and bureau scores, a decision engine can accommodate complex data points, including:
- Days Past Due (DPD) buckets
- Politically Exposed Person (PEP) checks
- Enquiry ratios
- Average bank balances
- Credit utilization ratios
- And many others.
The factors and rules used in a decision engine can be fully customized to align with the lender's policies. With Subspace's proprietary decision engine, changes to these rules can be made, deployed, and tested in real time, ensuring greater flexibility and adaptability for the lender.
To acquire the necessary data, a decision engine requests the information from relevant sources, including credit bureaus, UIDAI, and account aggregators. These sources have readily available API integrations within the Subspace ecosystem, enabling lenders to obtain the required data within seconds.
| Traditional DE | Subspace DE | |
| Custom Variables | Limited variables | 600 out of box + support for custom variables |
| Data Integration | Separate Integration required (eg. Bureau, GST, KYC, etc) | Pre-integrated, along with analysed models and unique scoring models |
| Real Time Testing | Does not support real time back testing | Gives real time estimate of disbursal rate and default rate |
| Effect on Underwriting | Does not improve underwriting | Automatically suggests improvements in policy |
| Modularity | Non modular | Completely modular with support provided for LMS integration |
| Additional Capability | Only supports decision making | Allows for creation of user journey with configurable user nudges and webhooks |
"Impact of a Decision Engine on Lender Profitability"
A decision engine bypasses intermediaries and communicates directly with borrowers and documentation authorities. This enables unbiased lending decisions based on accurate information and reduces the risk of bad loans, potentially improving lender profitability.
Another factor that can contribute to increased profitability is the reduction of risk officers required to assess a borrower. With the support of a decision engine, as few as 2-3 professionals may be needed to evaluate loan applications. This streamlined process reduces staffing costs and enables lenders to handle a higher volume of loan applications.
Conclusion
A decision engine is crucial for ensuring growth in today's market conditions. Integrating Subspace's powerful and customizable decision engine into your lending platform can enhance customer satisfaction by reducing turnaround time and improving decision-making efficiency.