There are probably very few business functions that are as complex, and customer focussed as the loan origination system (LOS)!
From when a loan request reaches a lending organization to loan disbursal, the process involves multiple phases and people. Added to this, increasing competition in the industry and cash hungry customers allow no space for mistakes anywhere in the process.
Getting caught in the maze
A loan application can be stuck anywhere within the system for the wrong reasons or may be proceeded with insufficient number of documents or proof. In other words, manual error, intended mistakes and fraudulent activity are all common within the loan origination system.
An efficient loan workflow management system can be the solution to these problems and the catalyst that pushes the lender to a higher level of efficacy, cost management and monetary benefits.
In this blog, we will be discussing loan exceptions with various use cases, the teams that are involved in the workflow and a final note on how to handle the exceptions and make workflow management more efficient.
Loan exception – why is it important?
Every lending organization has a well-defined, clearly written set of loan policies. These policies are important to manage risk within the loan portfolio of the lending organization and are approved by its board of directors. When loans fall out outside the prescribed policies or underwriting criteria, they are called exception loans or loan exceptions.
Loan exception – An example
For instance, a NBFC may not reject a loan application from one of its long-time clients who has been regular with his payments, even if his debt-to-income ratio is lower than the prescribed limit or if the client does not hold all the required documents for loan approval. The loan sanctioned to him is an exception loan.
Loan exception – How does it affect the lender?
A single exception loan may not impact the lending organization significantly but together they can influence the loan portfolio of the organization and can increase its credit risk considerably.
Additionally, there may also be cases when loans that are in 100% adherence to the organizational policies are rejected at the last point (maybe because the job applicant lost his job and has no way to pay back the amount or maybe things did not work well during the negotiation phase).
In both above cases, a deviation from the set workflow process of the loan origination system can be observed. In fact, not every loan application handled by a lender follows the different stages of LOS, seamlessly.
Loan origination system: Teams and workflow
Multiple entities are involved in the LOS workflow.
Let us explore how these teams are involved along the loan origination workflow.
The customer management team:
1. Made up of loan officers/field officers/agents/relationship managers.
2. Acts as the liaison between the customer and the lending organization. All correspondence with the lending organization happens through the field officer.
3. Deals with all the stakeholders and navigates through various layers of the lending organization to close the deal with the best possible terms.
4. Lending organizations have a team of these customer-facing employees who bring the deal to the table. The position, power and reporting standards of the loan or field officer varies from one lender to another
Underwriting team and credit team:
1. Also referred to as the risk team, the credit team evaluates the risk associated with a loan application.
2. With inputs from the risk team, the underwriting team makes its decision (additionally uses other sources of information such as credit report, social information and so on before making a decision).
1. A team member who is well-versed with the legalities of loan processing takes over to assess the documents handled in the transaction.
2. Smaller lenders may not have a dedicated legal team, nevertheless, a qualified member from the lending organization must investigate the validity of the documents submitted for loan approval.
3. This step is especially crucial in the case of commercial loans and mortgage loans.
1. Uses various parameters like income-to-debt ratio, credit score and so on to determine the loan amount (and the specific terms such as repayment period and interest rate) that an applicant is eligible for.
2. The team also determines if the organization can handle the loan, in case of larger amounts.
1. The accounts team takes care of the funds available with the lending organization to service clients as and when required.
2. It is important to note that in the contemporary times, most loans are disbursed only through the main branch and therefore the loan officer must be present to facilitate this transaction between the client and the lending organization.
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Deviations in workflow
The loan origination workflow and the involvement of the different teams within an organization are subjected to multiple cases of deviations in the real world. Handling these deviations efficiently is key to reducing costs, avoiding redundancy, increasing overall efficiency, lowering time taken for loan processing and keeping credit risk under control.
Generally, when a deviation occurs, manual intervention increases and this leads to increased chances of error and bias.
This section will talk about some classic cases of deviation in the LOS, how they affect the lender as well as the borrower, their ability to disrupt the standard workflow and
Case 1: Missing documents
The case: Since loan applications literally move from table to table in the traditional setup, there is an increased chance of missing key documents from the loan file (sometimes borrower fails to submit all the required documents intentionally or unintentionally).
Impact caused: To replace the missing document, the process starts over again and continues until it hits an end. In such cases, the lending organization loses the time of its employees and the borrower is pushed to produce the document yet again. This causes inconvenience and dissatisfaction on the customer’s side and for the lender it implies loss of time and energy.
Solution: The loan origination software can be used to avoid such redundancy. There is one provision of the software that maps each loan product with a list of mandatory as well as optional documents needed for approval. In the case of any one document missing, the software triggers the respective message to the user with no delay. This ensures there is no time or resource lost on tracking and following up with missing documents.
Case 2: Loan request for a larger sum
The case: Lending organizations have a limit on loan amounts. Also, not all branches of a lending company are eligible to approve higher amounts of loan. For instance, a credit score of 750 can make a candidate eligible for loan, but the score may be slightly insufficient for a higher amount of loan requested by the client.
Impact caused: The outcome of this case may be rejection of loan, compromised interest rates, increased time taken for loan approval or request for submission documents. In any case, manual intervention occurs, and chances of error and bias opens.
Solution: With the origination software, rules-based decision making can be enabled. From making simple binary decisions to allowing complex calculations based on the expertise and experience of underwriters, decision rules allow lenders to follow a logical as well as a consistent approach to meet the usual as well as their unique needs.
Case 3: Manual errors in processing the application
The case: While working with paper documentation, lenders are highly prone to defects. The source of the defect may be related to the borrower’s financial qualifications, mathematical errors in underwriting, incorrect categorization of loans and so on. In fact, there are chances of intentional error too. When such errors occur, the workflow is disrupted again.
Impact caused: Computational errors or data entry errors at the early stages of the loan processing can be carried all along the process and finally end up in more time and energy being wasted in correcting the mistake.
Solution: Just like the elimination of errors pertaining to missing documents, the automated data entry of the software handles inefficiencies in document processing too.
Case 4: The main branch alone takes the responsibility of credit decisioning
The case: Sometimes, when a loan officer or the credit team is being held under suspicion of immoral actions, then the credit decisioning process is moved to the headquarters of the lending organization. This causes a variation in the workflow too.
Impact caused: Several banks and NBFCs allow only their head offices to make loan decisions in order to have greater control over credit risk. However, these additional steps prolong the loan sanction period and causes frustration among customers.
Solution: When the loan origination software is used, there is almost no space for error and trust issues are diminished considerably. As a result, the lending organization as a whole feels more empowered and acts faster on all loan applications.
Case 5: Lower collateral value
The case: In the case of mortgage loans or home loans, the loan amount cannot exceed a certain percentage (say about 80% to 90% loan-to-value ratio (LTV) of the property value). The LTV can differ from one lender to another.
Impact caused: In cases, where the borrower has a good credit score and a debt-to-income ratio that is well above the set guidelines of the lender, the perfection in collateral may be overlooked, thus leading to an exception. However, the conditions pertaining to the loan case must be clearly documented for future reference.
Solution: The loan origination software does not reject applications without giving a recourse. The software flags such applications for closer review and opts to analyse additional information to make a quick and correct decision. This is where the importance of alternative credit data comes to play. LOS handles exceptions in such a way that it provides more lending opportunities without increasing the risk quotient of the organization.
Measures to be taken for an Effective Loan Policy Deviation Process
Loan exceptions do not just interrupt the workflow of a lending organization, but also increase credit risk and customer satisfaction is disturbed too. Increased competition in the lending industry and reduced loan growth post the pandemic could further contribute to loan exceptions. Well-defined loan policies and underwriting criteria will lay the foundation to effective handling of loan policy exceptions.
Below is a list of measures that can be used in effective loan policy exception monitoring and management.
1. Since it is practically not possible to have a unique set of guidelines for each type of exception and for each credit product, the loan policies must overall be reflective of the organization’s risk appetite.
2. Automation of the loan processes can go a long way in identifying loan exceptions, as well as in assessing, recording and reporting them regularly.
3. Digitalization eliminates the disadvantages of manual interventional and reduces the chance of intentional errors and bias. With digitalization of the LOS, lenders gain the ability to predetermine the workflow by defining events that lead to the various cases of exceptions.
4. Clear delineation of responsibilities to the different teams and its members. For instance, the underwriting team must maintain a central data system that helps to record exception loan data regularly.
The key takeaway
Every lending organization must understand that less than 10% of loan applications will encounter zero glitches in the loan origination process. Loan exceptions cause workflow disruption and this leads to a number of secondary issues like compliance issues, a bad credit portfolio, lowered customer satisfaction, decreased process efficiency and multiple chances of error and fraud.
To handle these problems, every lender needs an effective credit policy framework. At the heart of this framework lies the loan origination software that automates the entire process and supports every key decision with pre-configured credit policies and gathered credit history data of the lender.