What Are the Different LOS Workflows? Why Is It Important to Test Different LOS Workflow?

LOS workflow

The lending process is one of the service pillars of every bank and financial institution. Lending services support financial organizations and allow them to calculate high ROIs. Banks, NBFCs and other financial institutions offer various types of loans to their customers at an interest rate fixed by the organizations. Lending processes before were all about heavy paperwork, which caused unnecessary delays, confusion, and burdens of handling tons of papers and documents of customers. Since technology has taken over, the lending process is more consolidated, organized, and systematic. IT companies and service providers have developed and introduced various Loan Origination Systems (LOS) to support the loan application processing based on specific requirements by banks and financial institutions.

Loan Operating System is a technology-driven process. It controls and monitors the workflow of loan processing and reduces the delays and inefficiencies of handling paperwork. Loan application processing workflows are the most critical feature of the loan origination system. The loan applications monitoring starts at the initial stage and continues till the process is approved. The loan origination system also allows performing the work steps at different locations at the time, maintaining control of flows. Business analysis is also a pivotal stage for loan application.

The process workflow of Loan Origination System:

The approval process of loan origination system goes through many stages. The origination process varies from lender to lender, manual and automatic. Under the whole process loan origination system is responsible for everything that occurs from pre-qualification to loan approval and disbursement. The process workflow in LOS is same as the conventional process. There are only seven stages that are critical components of the loan origination process:

  1. Loan eligibility:

The first stage is pre-qualification stage of the loan origination process, where the borrower must submit important identical documents to the lender such as ID proof, address proof (Aadhar, Voter ID, Pan card), current employment information including salary slip, credit score, bank statement, and previous loan statement to verify the customer profile for loan eligibility. After thorough credit verification and validation of the customer documents, the lender determines if the customer is eligible for availing the loan. After approval, the borrower can move to further process.

  1. Loan application:

It is the second stage of the loan origination process. Under this stage, a borrower files the loan application through online submission of their documents as most of credit companies and financial institutions are following the new technological paperless process.

  1. Application processing:

Processing the loan application is the third stage of the loan origination system. Under this stage, the department receives a submitted application and reviews for its accuracy and completeness. If the application is incomplete, it is returned to the borrower for completing the missing information. The funding is to some corporate sector business analysis is compulsory.

  1. Credit Underwriting:

Credit underwriting is the fourth stage of loan origination process. This stage begins if the application is completed. Each credit department takes into consideration a variety of different components such as the credit score of borrowers, risk scores, and many more. Due to the technological changes, this process is now automated with the help of a rule engine and API integrations with credit scoring engines in LOS.

  1. Credit decision on loan process:

It is the fifth stage of the loan origination system. This stage begins after the results received from the underwriting process stage. Under this process, the application is approved, denied, and sent back to the borrower or originator for additional information. The certain information which is not matched with the rule engine set will be changed according to the parameters fixed. The changed parameters can be in the form of the reduced loan amount or increased interest rates.

  1. Quality analysis:

This is a critical step as the lenders analysis the application a final time before loan disbursal. Under this process, the application is sent to the quality check team for analyzing the critical variables of the application and comparing with internal and external rules.

  1. Loan disbursement:

It is the last stage of the loan origination system. Loan disbursement process starts after checking if all necessary documents are submitted and undergone the requisite quality check. Most of the loans are sanctioned after the documents are signed, but some loans like second mortgage loans, business loans, loans against property, and many more require additional time for legal compliance reasons.

Why is it important to test different LOS Workflow?

It remains a focus for financial organizations to improve the LOS workflow in the current market scenario. An organized LOS workflow improves customers’ experience and return on investment. With digitalization of LOS workflow, it has become essential for organizations to test different LOS platforms. Systems must undergo thorough functional testing (user acceptance testing, UI/UX testing, integration testing, regression testing, and more) and non-functional testing (performance testing, security testing, data migration testing and more) process to perform without any technical errors. Let us see the importance of testing different LOS workflows,

  1. Data entry and verification – Maintaining data integrity and security are the focus of any organization. By testing the LOS workflow, a team can ensure that the data entry process is verified. They can further ensure that there is no misleading or missing information that is mandatory for the process.
  2. Underwriting – The LOS workflows testing improves credit underwriting. The team can easily access the customer data and information to validate their eligibility and decide whether to accept the risk of lending to a particular person or company. Testing the LOS workflow allows the team to make credit-related decisions quickly and easily.
  3. Disbursement – Testing the LOS workflow facilitates the disbursement process. After the system carefully analyses the information and the credit approves lending to a particular customer, the disbursement process becomes extremely easy and seamless.
  4. Post Disbursement – After the loan is disbursed, the system still tracks the loan application process. The system actively validates information like tenure, moratorium, calculated interest rate and more. Testing the LOS workflow process enables post disbursement process.
  5. Reports – A system has the reports used for various decision-making purposes. LOS workflow testing ensures that the report generation is quick and error-free. The testing process also allows the extraction of an accurate report from the system whenever needed. 

Testing the LOS workflow is equally important as testing the Loan Management System. Following is the process flow that is validated during the LMS testing.

  1. Loan onboarding
  2. Loan Repayment
  3. Loan Servicing
  4. Delinquency and NPA processing
  5. Collections

The credit decision is the main feature of every loan process. The system analyzes the information of customers, which helps the lenders make the right credit decision to accept the risk of lending to a particular person or company. A customer portfolio is beneficial to evaluate the capacity of the borrower. It is also important to mitigate the lending risk, and the organization must have the risk management system ready to help revaluate the customer profile. LOS workflow testing validates the system’s capacity to handle the end-to-end lending process.

Reduced turnaround time is an important criterion to test LOS workflows. Hence, the loan origination system is tested to check the system’s response time and the quality of the process. Since the loan originating systems are updated frequently with information, testers must run regression tests to ensure that the frequent changes are not damaging the system performance. Testers must also frequently write functional test cases and test data to incorporate the functional changes in the system. Designing Migration test cases prior to execution is a critical step for a team because it ensures that the system performance remains unaltered during the data migration process.

Yethi’s experience testing different LOS workflows

Yethi’s testing approach is based on complete business analysis. We understand your business strategy, direction, and objective of technology transformation and identify the role of individual processing engines within the bank’s overall IT Architecture. We also make a considerable effort to understand your system integration requirements and the impact parameters.

We build a thorough test strategy and plan to ensure that we meet all your testing requirements. We also design functional test cases based on test data, regression test pack, and migration test cases to test execution. We manage the defects by defect categorization, metrics reporting, and test closure report with an enhancement analysis report for future reference. We also measure the scope of regression and execute the regression scenarios.

Yethi’s fifth-generation codeless test automation solution, Tenjin validates your LOS by automating the testing process. Its intuitive features and the Robotic UI discovery engine work flawlessly across multiple applications. It is a fast and scalable test automation platform that ensures high system quality through end-to-end testing.

Risks Associated with Data Migration and How to Mitigate Them

Let’s begin with some numbers! According to IndustryARC, the global data migration market that emphasizes Cloud-based servers over on-premises ones is predicted to reach an estimation of $10.98B by early 2022. In addition to this stat,  the Cisco Global Cloud Index shows that  Cloud traffic is expected to reach 7680 Exabytes in North America alone! Similar enhancements in modern data management technology bring more efficiency and transparency, which will directly surge the adaptation of application and data migration in small-scale and large-scale enterprises.

Given the risks associated, the question “Is data migration really important?” isn’t unusual. And the answer must always be “Yes!” Delaying data migration while holding onto outdated IT infrastructure isn’t an option with increasing market intrusion from non-traditional competitors who can create more nimble and responsive approaches towards delivering unique products. Because monolithic application systems weren’t designed to quickly adapt to business dynamics, they have to be replaced. Failing which, may pose further risks of losing market share and retention.

Let’s understand data migration first

At its core, data migration is the process of transferring data from one location to another, from one application to another, or from one format to another. This crucial step towards improvising an outdated IT infrastructure is generally taken during the installation of new systems or upgrading legacy ones, which will share the same dataset without affecting live operations. In recent years, the majority of data migrations are executed for transferring actionable data from on-premises infrastructure to Cloud-based options, that too, while undertaking data migration testing.

Concerns with legacy systems

The primary focus of IT infrastructure has already shifted towards better performing, more efficient, cost-effective, and secure solutions. CEOs and IT admins are struggling to maintain or support legacy systems as common challenges in legacy designs are time-consuming to tackle while the technology is mostly unfamiliar to new-age IT personnel. Some of the key concerns of using legacy systems include:

  • Heavy Maintenance Costs: Legacy systems are now obsolete, primarily, because of higher maintenance and operational costs. Further, the poor performance of such legacy systems cannot support new business initiatives.
  • System Failures: With legacy IT infrastructure, system failures are a daily occurrence. Since the professionals who implemented such systems have retired, new-age IT admins lack the skills to maintain legacy systems.
  • The Inability of Processing Complex Data: Within legacy systems lies old technology and computer systems that are fundamentally unable to execute complex enterprise operations with enough speed or reliability.

The increasing challenges to using legacy systems in today’s tech-driven world has led to migrating to new-age systems to keep up with the trend. However, migration to new systems may come with a set of potential risks which the organization should be able to mitigate and yield the best outcome from the migration.

Potential risks of data migration

  • Lack of Transparency: Not allowing key stakeholders to input in the undergoing data migration process is the mistake often made by enterprises. At any stage, someone might need the system to remain operational or would care if the data is being migrated, therefore, it’s vital to maintain complete transparency on the process.
  • Lack of Expertise or Planning: The primary cause leading to unsuccessful data migration is lack of expertise. With modern systems getting complex with millions of data points, it’s essential to evaluate which data points must stay operational. As data migration is more about risk mitigation, any disruption may leave IT admins clueless.
  • Addressing Data Privacy with Proven Migration Plans: When an enterprise doesn’t assess how many people might receive access to the data in the migration process, potential data breaches can occur. Conducting any data migration always requires proven migration strategies that further raise the probability of its success.
  • Defective Target Systems: Projects and vendors must be managed parallelly while flipping the switch from legacy systems to new-gen infrastructure. Suppose an error occurs in either the source system or the target system, it may derail the migration process in the middle of transferring vital data, raising the risk for data corruption.
  • Trying to Execute Complex Data Conversion: Unnecessarily making the migration process complex without any tangible increase in outcomes must be avoided at all costs. Complex conversions add extra steps to the process that just makes it challenging to execute. Only undertaking essential migration steps will surely get it done fast.

Why is data migration more about risk mitigation?

As legacy systems are growing organically, the need to adapt to modern business applications are raising concerns with their data quality. There might be millions of data points that must be assessed before concluding which ones must stay operational for any enterprise-scale migration. Along with regulatory and analytical needs, the data must be Extracted, Transformed, and Loaded (ETL) to modern systems without disrupting major business applications. As datasets get complex, things are no longer so simple!

The importance of conducting data migration testing

Once the data has been Extracted, Transformed, and Loaded (ETL) into new-gen systems, what stops it from being deployed? The answer is Data Migration Testing! As enterprises are swiftly migrating their operations to the Cloud, ensuring the integrity of data is key to ensuring further business applications. Here’s how enterprises achieve it:

Data-level validation testing

With certain data migration testing tools, data-level validation testing ensures that the dataset has been efficiently migrated to the target system without any disruptions. With data-level validation testing, data will be verified at:

  • Level 1 (Row Counts): Verifies the number of records migrated from the legacy system to the target.
  • Level 2 (Data Verification): Verifies data accuracy from any selected portion of the total migrated database.
  • Level 3 (Entitlement Verification): Verifies the destination database setup for users and selected data samples.

Application-level validation testing

In contrast, the functionality of the sample application is validated with application-level validation testing. This ensures the smooth operation of the application with the new infrastructure using specific data migration testing tools.


If you are concerned about the risks associated with data migration, you’d be relieved to know that the benefits far outweigh the risks. The importance of expertise and planning is still evident in data migration and data security concerns. In addition to having an efficient and rock-solid data migration strategy, enterprises must also practice data migration testing. Data migration processes remain an activity with potential risks, successfully testing can drastically reduce the migration errors while optimizing future data migration processes.

10 Best Practices and Strategies for Data Migration Testing

The immense digital growth witnessed over the last decade has moved digitization beyond being a mere business option anymore. It has become the driving force that allows organizations to sustain and succeed in this highly competitive world. Companies are constantly upgrading their legacy systems to cope with the ever-changing and evolving customer expectations. The upgrade often involves migrating the workflow process from an outdated system to a new one. Though migration might sound easy to achieve, migrating without losing any data is quite challenging. With advancement in technology, data has proven to become one of the most valuable assets of any organization. Data provides the right insights that help organizations make better decisions with a fact-based, data-driven approach. Hence, losing data while migrating to new systems can adversely affect the company; this is the key reason why data migration testing becomes vital.

Data migration testing is a verification process to ensure successful data migration to new systems with minimal disruption and data loss while maintaining data integrity. It also helps in ensuring that all the functional and non-functional objectives of the transition are achieved post-migration. However, not all data migration testing is effective enough to ensure all the goals are achieved. Following the best practices and strategies are essential as well as using appropriate data migration testing tools is important to make the migration successful. These best practices will help companies get the desired results from the tests.

Best Practices and Strategies for successful Data Migration Testing

Data migration errors can lead to adverse business and compliance risks that can make any company easily vulnerable to cybersecurity attacks. A thorough data migration testing can minimize the chances of errors and make the new systems more resilient. Here are the best practices to follow while performing data migration testing:

Multidisciplinary team approach

Having a comprehensive testing team is the foundation to ensure successful data migration testing. The team should consist of IT, management, and user community experts. The team members should have the right skillset and expertise to conduct the testing. To get the best outcome, it is important to verify their experience and train them on the source and destination systems.

Data backup

Data backup is critical during migration. In an extreme scenario, when the migration doesn’t go as expected, the process may lead to data loss. To avoid any potential data loss, it becomes mandatory to have a backup that will help in restoring the data under unexpected circumstances.

Analyze the Risks

Conducting an in-depth risk assessment and analyzing the results form the basis of a data migration testing strategy. The assessment should cover all the systems that would be involved or impacted by the migration. This will help analyze if the business would be hampered after the transition to the new system. Additionally, it will also help discover risks and implement mitigations for the same.

It is also recommended to have meetings with the right stakeholders, including business owners, product owners, test managers, analysts, etc., as a part of the assessment.

Create and Review the Scope of Migration

Plan and build a roadmap for the complete scope of migration specifications. Mostly organizations build a roadmap for implementing the migration but do not cover the entire scope. This should be done with the user community, IT team, and testers to include everything/everyone associated with the project. Also, understand that the scope can be reframed as per the needs pre-and post-migration.

Don’t Wait Until the End for Testing

Waiting till the end for testing can cost you a fortune. In case you find errors in the initial stages, the cost is relatively less. Even minute errors found in the end may require a complete roll back and re-migration. Also, during this process, the chances of data leakage and downtime increase significantly. Hence, it is recommended to conduct data migration testing at frequent intervals during each phase.

Detect Likely Sources of Errors

The testers need to detect as well as, to some extent, predict the likely sources of migration errors based on their experience. Hence, it is crucial to have someone with immense experience in the team. The sources and errors will also help define appropriate strategies to mitigate the known and likely errors to help the team stay ahead and be ready for any challenges faced during data migration testing.

Use Appropriate Data Migration Testing Tools

Having the right set of tools is vital for effective migration testing. You can use any of the following types of tools:

On-premise tools: On-premise tools transfer data from one server or database to another within the organization.

Open-source tools: These tools are free to use but require the knowledge of the underlying programming language to use them. Open-source tools are best used when the migration project is small, and the testing data is not enormous.

Cloud-based tools: Cloud-based data migration testing tools are the latest developments in the migration testing fields that enable a secure data migration to the Cloud.

Complete Pre-Migration Testing

Pre-migration testing occurs during the early stages of the migration, even when the migration is not yet initiated. As a part of pre-migration testing, you should cover the following tests:

  • Verify the scope of source and destination systems
  • Define high-level source and destination data requirements mapping
  •  Test the source data against the requirements of destination systems
  • Test unique links between source and target records to ensure a definitive mapping

Complete Post-Migration Testing

After completing the migration, it is essential to conduct post-migration testing, which should include:

  • Testing target systems throughout
  • Compare migrated, and destination systems generated records
  • Compare all the source and migrated data records (Automation tools are  recommended to detect even unlikely errors)

Conduct User Acceptance Testing

It is vital to check if the users for whom the migration is conducted can leverage it optimally. If the migration is making their daily work more challenging, it’s of no use. During the user acceptance testing phase, you should allow the users to interact with the legacy data through the new systems, even before initiating the production migration. This would allow for a quick rollback if the migration were not successful. If the user acceptance testing does not highlight any issues, post migration testing can be considered and data migration testing for it can be performed.

Have Clear Testing Documents

From the first test, it is important to create clear and concise documents for everything. The documents should specify the test approach, scope of migration, tools used, testing methods, methodology, etc.

Why Choose Yethi for Data Migration Testing?

Yethi is a leading QA  provider for global banks and financial institutions. We understand the need and importance of data migration testing that helps us cater to the needs of every business, regardless of the size and complexity of the source and data systems. Additionally, we also have the right expertise and data migration testing tools required to ensure effective testing. With our technical and functional audits, you can rest assured of having fully functional new systems without data loss post-migration.

What Is Migration Testing: How to Efficiently Conduct Testing While Migrating Data?

Migration testing

Technology is ever-evolving with new advancements making its presence every day. Organizations are constantly updating their legacy systems to the new ones to take advantage of these developments and align with the constantly changing end-user preferences. However, the major challenge lies in migrating to the new system without losing data. Data owned by organizations is an asset that provides critical insights to plan future endeavors, hence, organizations cannot afford to lose them.

Though data migration is a tedious process that requires enormous effort and time, it is a significant process that plays a crucial role in application redesign. Data migration cannot yield favorable results without a thorough testing process in place. Migration testing ensures that data integrity is maintained while upgrading, integrating, or transferring the system, while making data migration a success that affects your business positively.

Migration testing ensures the data migration has not resulted in any disruption and all functional and non-functional aspects of applications are retained even after migration. It is extremely important to undertake Data Migration Testing to find out the discrepancies that arise while migrating the data from the parent or legacy database to the new or destination database. Organizations must efficiently conduct testing while migrating data for smooth operations.

The need for migration testing

Testing of the system is critical even when the slightest change is made, to ensure the incorporated change doesn’t create any conflict in the current workflow or make any further unnecessary modifications. Hence, it becomes important to carry out end-to-end testing during the system migration process. Migration testing is essential as it:

  • Ensures continuity and consistency after the platform migration – imagine if your mortgage computation changed because the lender changed systems?
  • Ensures no data loss when you move to the new platform – imagine if your broker could no longer locate your retirement account after her company upgraded their systems?
  • It identifies any defects in the new application and ensures that it works perfectly without any issue.
  • When migrating from one system to another, migration testing ensures proper flow and working of the application, as it was before migration.
  • In addition to testing data retention and functionality checks, migration testing also checks if the application is optimized to the new workflows and environment.

Data Migration Testing Approach

Organizations should incorporate the following strategic approach for data migration testing for best results:

  • Form a specialized team for data migration testing: It is of paramount importance to have a team that possesses the required skill set and experience to carry out data migration testing.
  • Risk and Error Analysis: It is essential to ensure that data migration testing should be smooth and does not disrupt the current business. Data migration testing should focus on high-risk scenarios to validate and mitigate risks.
  • Scope of Migration Testing: Organizations should decide the scope of data migration testing- what is to be tested.
  • Select the Data Migration Testing Tool: An appropriate Data migration Testing Tool should be selected to achieve minimum discrepancies and anomalies.
  • Identify the Test Environment: The test environment should be set up according to the technical aspects of the source and target data system.
  • Migration Test Document: In the end, it is important to prepare a migration test document that states the testing tool, testing methods, schedule of testing, etc.

Data migration testing has assumed a lot of significance in the current scenario, where the availability of the right data at the right time governs the success of any organization. Organizations are investing a huge amount of resources in migration testing to avoid any mishaps later on. Efficient and effective data migration testing combines a systematic approach, prescient risk minimization techniques, and holistic test coverage.

Understanding data migration testing

Efficient data migration testing includes the following two levels of validation testing:

  • Data Level Validation Testing
  • Application-Level Validation Testing

Data Level Validation Testing

When data is moved from several databases to one common database, data level validation testing is done to ensure that there are no discrepancies. It ensures that the complete data is migrated to the new system and no loss was incurred during the migration process. Data Level Validation Testing can be further classified as follows:

Level 1: Counting the number of rows

In his stage, we find out the total number of records that would be moved or migrated.

Level 2: Verification of data

A sample is selected from the migrated data and checked thoroughly to verify the accuracy of the data.

Level 3: Verification of Entitlement

At this stage, the new database is verified for the users as well as samples of data.

Application-Level Validation Testing

Application-Level Validation Testing checks whether the migrated applications function efficiently in the new database or not; a sample application, that was migrated, is tested for its functionality. Following validations are carried out:

  • Verification of sample data by logging into the new application after migration
  • Verification of the status of accounts- whether it is locked or unlocked by logging into the legacy system post-migration
  • Verification of access to legacy systems during migration despite user blockage
  • Verification of instant restoration of user access to the old system in case of failure of migration
  • Verification of denial of access to legacy systems at the time of migration
  • Validation of login credentials for migrated applications

Data Migration Testing Tools

Data Migration Testing Tools can be classified into three categories as follows:

  • On-premise Tools
  • Open-Source Tools
  • Cloud-based Tools

On-premise Tools: When organizations want to transfer data from one server to another or from one database to another within the enterprise, they use on-premise data migration testing tools. In this scenario, it just involves changing the database or integrating databases.

Open-Source Tools: Open-source data migration tools are easily accessible by people. They are free to use. Organizations can use these data migration testing tools if the project is small and the data to be tested is less. Knowledge of coding skills is required if you use open-source tools.

Cloud-based Tools: Cloud-based data migration tools are the latest developments in data migration and testing. These tools enable the organizations to transfer the data to Cloud, which is the need of the hour. Hence, Cloud-based data migration tools are widely used as they are secure, flexible, and cost-effective.

Yethi’s data migration testing

Yethi is a well-known name in the BFSI industry for offering specialized QA services. We understand the importance of testing during data migration process, hence, we assure seamless data migration testing under budget and time constraints. At Yethi, we have the experience and expertise to handle testing of different scales and complexities of the data migration process. With our extensive technical and functional audits, you can be assured of having a perfectly working system, even after the migration process.