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Mitigating Risk in Debt Factoring: Underwriting Best Practices

Lending Lifecycle Management

Accounts receivable financing or debt factoring is a common business financing practice. Businesses can sell their accounts receivable to a third party, called a factor, at a discount.

However, factoring could be an excellent solution for companies to try to cash in on their account receivables, control cash flow, and keep the business afloat. Factoring carries certain risks even though it may be a helpful tool for companies looking to manage cash flow and raise capital fast.

If factors are to avoid the risks, they will have to do thorough due diligence and establish well-thought-out plans. This article will examine ways of reducing factoring risk through debt underwriting and due diligence.

What is Debt Factoring?

Debt or invoice factoring enables businesses to instantly release funds held in outstanding invoices without waiting for customary payment terms. It works by having a company sell its accounts receivable to a third party at a discount.

  1. Sell Your Invoices: Once you’ve decided to use factoring services, your business will sell all or some of its outstanding bills to a factoring company.
  2. Invoice Submission: Initially, you provide your products and services to the customers (as usual) and then send them an invoice for them.
  3. Factoring Company Assessment: The companies that factor debt are the third parties to which these invoices related to accounts receivable are sold. The factor will evaluate the level of risk by looking at your debtors’ financial health.
  4. Factoring Company Decision: Based on this, they decide what proportion of the invoices they can factor. Factoring businesses usually pay the majority (generally around 80%-90%) of each invoice payment.
  5. Customer Payment: Your clients’ payments then go directly to this third party.

Risks Associated with Debt Factoring

Debt or invoice factoring has some benefits, such as improved cash flow and lower credit risk. However, the factor also has a few risks:

  • Credit Risk: Factors may be exposed to non-payment risk from the company’s customers. The factor may lose money if the customers fail to meet their financial obligations.
  • Concentration Risk: High concentration on a few clients may expose the factor to concentration risk. The failure of a major customer could significantly affect the portfolio of the factor.
  • Fraud Risk: For a factor, invoices sold without proper validation can pose a fraud risk, as it may buy fraudulent invoices or invoices for goods/services never provided.
  • Operational Risk: Inefficient collection processes or errors in underwriting can lead to operational issues and financial losses for factors.

Due Diligence in Debt Factoring

One of the key components of factoring a debt or an invoice is due diligence, in which factoring companies execute their due course in investigating the accuracy, sustainability, and profitability of the accounts receivable proposed to be factored.

This step is important because it allows for the identification of warning signs and risks regarding the receivables. 

Due diligence, hence, reduces the likelihood of financial outcomes in the form of losses since it improves the efficacy of the arrangement regarding factoring.

Mitigating Risk in Debt Factoring through Due Diligence

To mitigate the risks inherent in invoice factoring, factors must prioritise thorough due diligence before acquiring any receivables. This essential process involves:

1. Creditworthiness Assessment

Analysis of the creditworthiness of the company’s customers, who are responsible for payment of the invoices. This assessment determines the likelihood of timely payment and reduces the risk of non-payment or default.

2. Verification of Invoices

Factors verify the authenticity of the invoices issued by the company. This implies checking if the goods or services mentioned on the invoice were actually provided or billed and if the amounts credited are correct.

3. Review of Contracts and Agreements

Factors review the contracts and agreements between the company and its customers regarding what is billable to ensure that the amount billed to the customer aligns with the amount agreed upon by both parties. This helps avoid payment disputes and minimises bad debts.

4. Customer Concentration Assessment

A customer concentration assessment is carried out to check the degree of concentration in receivable accounts among the customers of the company.

High customer concentration may endanger the company’s financial stability if any of the major customers fail to pay as agreed. When diversification of customers takes place, it reduces the risk of concentration.

5. Legal and Regulatory Compliance Review

Factors must ensure that the factoring facility meets all legal and regulatory requirements.

This ensures that the contracts to be factored are legitimate, that the procedures of factoring, in terms of contractual compliance, registration, and adherence to specific industry and consumer protection laws, are carried out, and that the agreements comply with all applicable laws.

6. Industry and Market Factor Assessment

The creditworthiness of the invoices is based on broader industry and market factors that may arise during the customer’s payment. The organisation of the industry, the economic climate, the market condition, and industry-specific risks are all considered while going through the due diligence process.

7. Assessment of Operational and Financial Stability

The factoring organisation may assess the company in terms of the operational and financial stability that would be essential for the debt to be bought from them.

The assessment of financial strength and cash flow projections will take place while making a call to buy any debt from the company.

Debt Factoring: Underwriting Best Practices

In order to further mitigate risks, in addition to detailed due diligence, it should use proper underwriting procedures:

1. Risk-Based Pricing

Variables to be priced according to the perceived risk for every transaction. Higher discount rates might have to be put in for more risky transactions so that concentration and credit risks may offset them.

2. Monitoring and Reporting

Regular reporting, combined with ongoing receivables monitoring, will help identify early signals of default or problems related to collection. Factors have to create communication channels within the organisation so that issues could be dealt with swiftly.

3. Reserve funds

Factors should keep reserve funds to offset losses from non-performing receivables. Such reserves will keep the operations of the factor steady against unforeseen defaults.

4. Technology Integration

Improve the effectiveness and accuracy of underwriting using advanced technologies like automated paperwork and data analytics. High-tech computing systems can be used to analyse large datasets for trends and identify possible future credit pitfalls.

Final Note

Debt factoring is instrumental in liquidity management and maximising business cash flow. However, it is not risk-free. The inherent risks in factoring can be controlled using effective, stringent due diligence and underwriting methods.

Ultimately, proactive risk reduction techniques are necessary for ensuring long-term sustainability and success for businesses in factoring.

Finezza’s innovative products, such as the collection management system, meet lenders’ demands with a higher degree of sophistication and customer-centricity.

This web-based platform automates applications, credit evaluations, and approvals, offering security and compliance and customised analytical solutions to perfect lending decision-making, shorten loan lead times, and efficiently assess creditworthiness.

Register and use Finezza’s cutting-edge technology to transform your financial operations.

10 Ways Automated Lending System Helps Lenders Mitigate Risk

Lending Lifecycle Management

Effective risk management is crucial for lenders as it helps them identify, evaluate and mitigate risks. The rapid growth of credit operations, rising customer expectations for personalised solutions and lower turnaround further emphasise the need for robust risk assessment and management tools.

As per a report in Hindu Business Line, retail loans grew at a compounded rate of 24.8% for the year ending March 2023. This is almost double the growth rate of advances for the year ending March 2022.

In this scenario, automated lending systems help lenders meet customer expectations, offer them personalised solutions and manage the rapidly increasing loan applications more effectively. Furthermore, these tools serve as a valuable asset for risk management and eliminate the problems associated with manual risk assessment.

The blog post focuses on 10 ways automated lending systems help lenders mitigate risk.

10 Ways Automation Mitigates Risk for Lenders

Traditionally, lenders carried out credit appraisal and risk assessments manually. This involves extensive paperwork and dependence on obsolete credit scoring models which rely on subjective rather than objective, data-based inputs. These risk management models often lead to inefficiencies, higher turnaround time, errors in judgement, and may also be biased.

Modern risk assessment tools that leverage Artificial Intelligence (AI) and Machine Learning( ML) do away with the problems associated with manual risk management and offer several advantages.

Here’s how lenders can gain in the following ways when they use an automated lending system:

1. Harness the power of data analytics 

Automated credit assessment systems can process large volumes of data much faster with better efficiency. Robust data analytics can spot patterns and anomalies that are not apparent to humans, resulting in better risk assessment and decision-making.

Intelligent solutions like Finezza’s Analytics suite provide results that improve and get richer and deeper with each usage.

2. Conduct more stringent credit checks

An automated lending system helps lenders scrutinise loan applications more thoroughly. Financial Institutions (FIs) constantly have to walk the fine line between enhancing their customer base and maintaining the quality of their portfolio. They do not want to deny credit to deserving applications and want to promote financial inclusion; at the same time, they want to minimise the default risk.

Filtering customers at the application stage ensures only qualified customers get loans. Loan automation systems evaluate more data points and sources, usually overlooked in manual processing to assess applicants. AI and ML-based risk scores for borrowers help identify potential default risks.

3. Real-time monitoring

Simultaneous tracking goes a long way in mitigating default risk. Credit health is a dynamic concept, as the creditworthiness of an individual may change with time. If the credit quality of the lender’s portfolio deteriorates due to defaults and missed payments, real-time monitoring can help them take timely action.

With an automated lending system, lending companies can track and monitor loans more effectively and act promptly with responses like sending frequent reminders. Real-time risk monitoring helps reduce bad debt risks with real-time alerts and credit reviews.

4. Eliminate human bias

Besides offering a more accurate evaluation of applicants’ repayment and risk capacity, advanced algorithms of automated lending systems help remove human biases from the process.

Automation of risk assessment guarantees uniform and unbiased evaluation for all applicants. This ensures that only worthy applicants get loans, and no deserving applicants are denied credit due to inherent biases.

5. Reduce Errors

Copious amounts of paperwork involved when processing a loan application in conjugation with the pressure of reducing the turnaround time, can result in errors.

Automation eliminates the risk of manual errors occurring while entering data and processing, which could lead to faulty decisions.

6. Utilise alternative data

Automation also helps FIs look at alternative data sources that traditional risk assessment tools and models usually overlook. Cross-product data from internal sources, social media inputs, online buying patterns, utility bill payment details, and more help provide a comprehensive customer profile and aid risk assessment.

7. Better compliance

The lending sector is heavily regulated; adhering to various compliance guidelines set by regulatory bodies is crucial for all FIs. Non-compliance with these guidelines can result in penalties and make the financial institutions susceptible to instability and fraud.

Automated lending systems ensure timely and accurate complaince. They also help lenders align their policies to the regulatory framework.

8. Predictive analytics

Predictive analytics helps FIs identify potential risks which are likely to occur. This analysis can help lenders take preventive measures and make more informed decisions.

The predictive analysis forecasts future outcomes considering current and emerging market trends and identifies anomalies that may go unnoticed by traditional methods.

9. Minimise Strategic Risk

Automation helps FIs align their underwriting decisions with their risk policy seamlessly. Any updation in the risk policy demands a change in the underwriting process too. Automated loan processing systems require a few tweaks to update the approval process.

Compare this to updating the manual risk assessment process, which would require extensive training and ensuring the updated information reaches each team member and they comprehend it well. This could be a time-consuming, expensive process, fraught with teething troubles.

10. Facilitates risk-based pricing

Automated lending systems facilitate pricing loans in line with the borrowers’s risk profile and ensure that lenders get compensated for the risk they are taking on.

Risk-based pricing allows lenders to offer different consumers different interest rates or variations in other terms, depending on the risk profile perceived by the system.

Risk-based pricing models also enable better risk management. For example, a customer who offers collateral could get loans at lower rates, just like a customer with a high credit score.

Conclusion

Risk is inherent to the lending process; automated lending systems help lenders mitigate these risks and enhance efficiency and customer satisfaction. Financial institutions can perform better credit checks and offer customers risk-based pricing by automating their lending process. As a result, automation helps lenders make intelligent decisions based on data, analytics, assessing alternative data points and predictive analysis, which reduces their risk.

If you are looking for a solution that offers all this and more, then Finezza has the right solution for you. Their automated loan process system is a cloud-based, AI-enabled solution that automates the entire loan processing workflow.

Finezza also offers an all-in-one credit evaluation solution that merges data reports from multiple sources with AI-powered predictive analysis to help lenders make risk-free decisions.

Book your demo now!

Managing Credit Risk Analysis and Scoring Using Lending Management Software

Lending Lifecycle Management

Credit risk analysis is a vital process that helps lenders assess the possibility of the borrower defaulting and the loss they may incur if the borrower fails to repay the contractual loan obligations. It often goes beyond credit analysis, primarily focusing on the borrower’s creditworthiness. In contrast, credit risk analysis assists a lender in achieving their business goals by evaluating the cost and benefit of taking credit risk.

A GlobeNewswire report suggests that the global corporate lending market, growing at a CAGR of 9.6%, will reach $11,285.05 billion by 2026, which further stresses the need to manage credit risk effectively for lenders.  Moreover, the global financial crisis in 2008-09 emphasised the risks lenders face when they extend credit to customers with poor credit histories.

One effective approach could be adopting the automation of traditional banking activities, including risk analysis and scoring, using lending management software that relies heavily on machine learning, artificial intelligence, and data analytics. These solutions enable lenders to manage their risks more effectively.

In this blog, we focus on the role of lending management software in credit risk management.

Lending Management Software: Role of Credit Risk Analysis and Scoring

Credit risk analysis helps lenders determine a borrower’s capability to meet their loan obligations and evaluate the probability that they will default.

With smart  Lending Management Software, assessing credit risk helps lenders take on an acceptable level of risk which helps them achieve their business goals. They can use the results to develop policies. It can help them reach specific goals while protecting themselves from potential losses.

Additionally, credit analysis can help lenders formulate policies regarding interest rates. Lenders can set interest rates in line with the risk profile of a borrower. They may set higher rates for applicants posing a higher risk to protect themselves from the loss if a borrower defaults.

How Lending Management Software Aids Credit Risk Analysis and Scoring?

Credit risk analysis and scoring help lenders make informed decisions and mitigate risk. Manual risk assessment is lengthy and time-consuming, resulting in delays for borrowers and is prone to errors and human biases.

Automating credit risk analysis and scoring using lending management software helps deal with these issues and offers additional benefits.

For example, the Bank Statement Analysis tool from Finezza is an automated tool that helps lenders develop a more practical index to reduce NPAs and bad credit decisions.

Automating credit risk analysis using loan management solutions allows lenders to concentrate on more viable loan applications and price risk more precisely based on a more holistic picture of the applicants’ credit health.

5 Benefits of Using a Lending Management Software

Lending management software offers lenders the following benefits:

1. Improved Accuracy

Lending management software uses sophisticated algorithms to analyse data and identify risk factors to improve the accuracy of credit risk analysis and scoring. The use of advanced data analytics and rule-based decision-making reduces bias and human error.

Also, the machine learning algorithms and analytics factor in current market trends and borrower behaviour because of real-time data visibility and help build more comprehensive borrower profiles.

Consequently, the loan management software offers a more accurate evaluation of creditworthiness as it analyses large volumes of data and helps lenders enhance their loan portfolio quality and foresee potential contingencies.

2. Increased Efficiency

Lending management software increases the efficiency of the credit risk analysis and scoring processes. Automation helps in quicker credit risk assessment without compromising accuracy.

It also expedites and simplifies risk assessment lending operations by harnessing the power of artificial intelligence. Features like customised credit analysis based on data from multiple institutions and a view of loan history, repayment history, a summary of borrowings, and an ‘Enquiry to Success’ ratio are readily available, which aid faster decision-making.

3. Comprehensive View

Traditionally credit scoring models focus on a few metrics like the debt-to-income ratio, repayment history and credit utilisation ratios for assessing an applicant’s creditworthiness.

However, in current times lenders want to look beyond these parameters to assess an applicant’s ability to repay loans. Alternative data points help provide a more comprehensive picture of the applicant and can help in better risk assessment.

Bank transactions provide valuable insights into an applicant’s income, expenses and spending pattern. This combined with GST returns data of a business, gives a multidimensional insight into an individual’s or a business’s financial health and the future outlook.

4. Better Fraud Detection

Loan management software with analytics-aided features, digital credit assessment credit-collection analytics, and early-warning systems help devise a robust fraud detection system.

Finezza’s Collection Delinquency Management leverages Artificial Intelligence to protect lenders from risks of fraudulent activities. The system can detect suspicious activities and prevent potential fraud by analysing patterns, historical data and outliers.

Features integrated into the loan management software help identify anomalies and trends that point to fraudulent conduct and risky loan applications. Minimising the risk of granting fraudulent loans helps save time and resources that may have to be used if there is fraud.

5. Manage Risk Proactively

The borrower’s credit health, a focal point at the start of the lending cycle, which can sometimes deteriorate during the loan lifecycle, is often ignored after loan disbursal.

Continuous monitoring can help identify early warning signs of the deteriorating financial health of borrowers. Lenders can protect their interests better and take the required steps to manage credit risk more effectively.

Real-time loan monitoring with loan management software can help lenders be proactive and have a forward-looking approach toward risk management.

Conclusion

Credit risk management is crucial for a lender’s success and growth.  To accelerate this process, lending management software improves the accuracy and efficiency of risk analysis and fraud detection, ultimately enabling lenders to manage risks better and protect their interests.

As a cloud-based platform, Finezza offers a diverse array of solutions like the Loan management system, Collection Delinquency Management and more to allow lenders to manage risk in a more intelligent, data-driven and effective manner.

Reach out to us to learn more about our solutions and how they can assist you!

How Loan Management Software Can Help Reduce Loan Default Rates

Loan Origination System

Lending as an economic activity has come a long way from the days of pawnbrokers to credit cards and, most recently, digital loans. The advent of agile technologies, novel delivery platforms, innovative business models, and competitive players is accelerating the digital revolution in the lending sector. The digital evolution of lending has now reached the point where the entire lending process is automated and streamlined using Loan Management Software (LMS). It streamlines loan processing and allows lenders to manage their portfolios easily. Borrowers also benefit because it gives them more control over their personal and financial data.

With its wide-ranging capabilities, loan management software have assisted lenders in efficiently managing recovery and lower defaults while delivering new competencies.

This article explores how loan management software can help you lower default rates in your lending portfolio.

Critical Issues in Loan Life Cycle Management: Delinquency, Defaults, and NPAs

The primary factor determining the lending firm’s profitability is the quality of assets in its portfolio. Therefore, they must prudently and actively manage the life cycles of loans to ensure and sustain asset quality.

Credit delinquency

For a credit product, its health correlates with repayments as per its amortisation schedule. Defaults and delinquencies represent missed repayments of equated monthly instalments (EMIs) but vary in severity.

If the required payment is not made on time or even slightly past the due date, the loan is said to be delinquent. It will be regularised with the payment of instalments plus applicable interest and fees.

Default Stage

A loan enters in default stage if it continues to be delinquent for an extended period. This has dire consequences for the lender and the borrower, affecting their relationship.

It impacts the borrower’s credit score limiting his/her ability to avail further credit.

RBI’s Direction on NPAs

This poses a grave issue for lenders as well. The Reserve Bank of India(RBI) has stipulated that loans past their dues for more than 90 days must be classified as non-performing assets (NPA) and handled as per prevalent regulations.

An NPA is a double-edged sword for the financial institution as it:

  1. No longer generates revenue and
  2. Becomes a loss-making liability because of the provisioning requirement as per Income Recognition and Asset Classification (IRAC) norms.

Impact of High NPA Levels

High NPA levels impede a financial institution’s ability to lend due to declining revenues and regulatory constraints imposed on lending activities due to such bad loans.

All these variables make loan defaults a ticking time bomb that could blow into a calamity. The need for loan management software becomes apparent in this situation.

Lowering Default Rates: Role of Loan Management Software

Loan management systems employ cutting-edge technologies such as Artificial Intelligence (AI) and Machine Learning (ML) to manage loan recovery efficiently.

Compared to manual credit management practices or legacy software, the features of such software serve as superior tools for banks and FIs in handling loan defaults.

To prevent repayment defaults, loan management software performs three types of functions:

1. Preventive functions

Preventive measures begin with the loan application process. One of the primary goals of credit appraisal is to determine the applicant’s eligibility and repayment capacity for the proposed credit facility.

Another goal is to weed out habitual and potentially wilful defaulters and fraudulent applicants.

To establish applicants’ creditworthiness, manual and legacy systems rely on credit reports from credit information firms. However, these credit reports are often outdated, or the data cannot accurately calculate repayment ability.

For credit appraisal and underwriting, loan management software employs Robotic Process Automation (RPA) and machine learning (ML).

These tools verify the applicant’s identity, pull information from numerous credit reports, analyse real-time transaction data for assessment, and even leverage data from the applicant’s social networks to get an accurate credit score for processing the application.

2. Predictive function

Continuous monitoring aids in detecting early warning indicators and enhances risk management activities.

However, it is an area that is frequently overlooked during the loan’s life cycle. Competition necessitates business expansion, therefore, the emphasis shifts to maximum credit disbursement.

Also, a point to note is that a borrower’s credit profile could deteriorate during the loan. His financial health will affect the loan repayment. Therefore, it is necessary to monitor the loan to protect the lender’s assets constantly.

The Role of MIS

Through comprehensive Management Information Systems (MIS), loan management software aids in quickly and easily identifying these warning signs. These MISs regularly create extensive reports on potential NPA, special watch accounts, etc., to help businesses monitor these loans better.

A smart system will also notify lenders when regulatory requirements like stock statements or acknowledgement of debt (AOD) are due from the borrower, without which the loan may slip into NPA even with proper repayments.

It would also send automatic notifications to loanees when repayments are due, informing them of upcoming payments.

3. Proactive function

Loan recovery and delinquency management is a major aspect of lending that demands considerable effort over extended periods.

Rigorous due diligence, real-time credit appraisal, active monitoring, and dynamic action reduce loan default rates in a lending institution’s portfolio.

Final Note 

Loan management software can help with automated decision-making, early warning systems that use predictive analytics, identifying problematic loans, developing collection plans, allocating resources for recovery action, and other tasks.

Moreover, they provide preemptive and remedial solutions to help reduce loan defaults.

Finezza offers a comprehensive and agile Loan Management System that supports multiple loan types. We also provide an advanced and customer-centric Collection Delinquency Management Suite for streamlining your debt collection activities.

Finezza makes it easier to recall loans or restructure credit in a mutually convenient way for the lender and the borrower.

Contact us here to know more.

Working Capital Lending Risk Assessment: Best Practices & Pitfalls

Supply Chain Financing

In the current financial year, businesses are expected to require Rs 11.2 lakh crore in working capital, up from Rs 8.7 lakh crore in the previous year. As a result, businesses will need more capital to meet their operational expenses, such as inventory purchases, salaries, and other expenses. As the lending sector becomes increasingly competitive, it is also crucial for lenders to understand the importance of risk assessment in working capital lending.

In this article, we will discuss the meaning of risk assessment in working capital and the best practices and pitfalls to avoid when assessing risk in working capital lending.

What Is Working Capital? 

Working capital is the amount of money needed to run a firm on a daily basis, such as paying for goods, employees, rent, and other operating expenses. It is the sum of a company’s current assets and current liabilities.

Cash, accounts receivable, inventory, and other short-term assets comprise a company’s current assets, while current liabilities comprise accounts payable, short-term loans, and other debts due within one year.

Working capital lending is borrowing funds to support a company’s short-term operational needs, such as wages, rent, and inventory purchases.

Why Do We Need Working Capital Loans?

Here are some of the reasons why working capital lending is necessary:

  • Managing cash flow

Cash flow changes are expected in businesses due to a variety of factors such as seasonality, economic cycles, and unanticipated expenses. Working capital lending assists businesses in managing their cash flow by giving access to funds when needed.

  • Supporting growth

As they expand, businesses may need to invest in additional inventory, equipment, or personnel. Working capital lending assists in financing these expenses, allowing businesses to develop without depleting their cash reserves.

  • Keeping operations running

Unexpected expenses or a decrease in revenue may occur in various instances. Working capital lending helps bridge the gap between income and expenses, allowing firms to continue operating despite tough times.

  • Avoiding cash shortages

Businesses that lack sufficient operating capital may struggle to pay their payments on time, resulting in late fees, penalties, and lowered credit scores. Working capital lending allows businesses to avoid cash constraints and maintain a solid financial status.

How Does Risk Assessment Work in Working Capital Lending?

Risk assessment in working capital lending is the process of evaluating a borrower’s creditworthiness and determining the possibility of payback of a loan used to finance the company’s operations.

The assessment examines several aspects that may affect the borrower’s capacity to repay the loan, including their financial situation, credit history, collateral, and industry/market trends.

Risk assessment aims to assist the lender in determining the right amount of credit to issue to the borrower and to reduce the chance of default.

A lender, for example, may utilise risk assessment to examine a small firm that requires a loan to purchase inventory. To estimate the likelihood of repayment, the lender may examine the company’s financial statements, credit history, and industry/market trends.

What Are the Best Practices for Risk Assessment in Working Capital Lending?

Working capital loan risk assessment is a  process that evaluates a borrower’s creditworthiness and determines the right amount of credit to offer.

The steps involved in risk assessment in working capital financing are as follows:

  • Initial Screening

The first phase in the risk assessment process is to screen the borrower to see if they match the lender’s basic eligibility requirements. Verifying their identity, business registration, and creditworthiness are all part of this process.

  • Data Collection

The lender then gathers information on the borrower’s financial situation, credit history, collateral, and industry/market trends. Examining financial documents, credit reports, and collateral values is part of this process. In addition, the lender may undertake site visits and interviews with the borrower’s employees.

  • Risk Analysis

Lenders analyse the borrower’s creditworthiness, credit scores, collateral values, and industry/market trends to evaluate the borrower’s creditworthiness and potential risks. Additionally, the lender assesses the borrower’s ability to generate cash flows and repay the loan.

  • Credit Decision

Based on the risk analysis, the lender makes a credit decision on whether to approve or deny the loan application. If the loan is accepted, the lender calculates the right amount of credit to offer and the loan’s terms and conditions.

  • Monitoring and Reporting

Once the loan has been disbursed, the lender will conduct continuous monitoring and reporting to ensure that the borrower is abiding by the loan’s terms and conditions.

Examining financial documents, credit reports, and collateral values is part of this process. The lender also monitors the borrower’s credit history and any changes in their financial situation.

The overall risk assessment in working capital lending involves gathering and analysing data to determine a borrower’s creditworthiness.

Avoid These Common Risk Assessment Pitfalls in Working Capital Lending

In the lending industry, risk assessment is critical to working capital lending, but if not conducted thoroughly, it can lead to significant pitfalls, including:

Over-reliance on collateral

To secure the loan, the lender should not rely primarily on collateral. In the event of a default, collateral may not be adequate to cover the loan amount.

Ignoring market and industry trends

The lender should not overlook market and industry trends. Failure to consider industry and market developments may result in lending to a company with limited future possibilities.

Inadequate financial statement analysis

The lender should not depend simply on the borrower’s financial statements without undertaking a thorough examination. Inadequate analysis may result in a loan being made to a company that cannot repay it.

Failure to undertake site visits

Without site visits, the lender should not depend exclusively on the borrower’s financial statements. Site visits allow you to learn about the borrower’s operations and management procedures.

Summing Up!

Risk assessment in working capital lending is critical for lenders to ensure timely repayment of loans. Analysing the borrower’s financial accounts and reviewing the borrower’s credit history are effective practices for risk assessment. Effective risk assessment strategies can help prevent defaults and reduce losses for lenders.

Fintech tools can help in risk assessment by leveraging advanced algorithms and machine learning techniques to analyse large amounts of data, identify trends and patterns, and generate insights that can help lenders make informed decisions.

Finezza is a fintech tool that offers risk management services to banks and NBFCs, enabling them to offer their customers a complete, fast, and error-free lending solution. Providing end-to-end loan management and credit evaluation solutions for lending businesses,

Finezza’s advanced document identification and verification capabilities you can easily extract information from verification documents such as PAN cards, balance sheets, and much more. In addition to saving time, this automated feature increases workflow speed with no chance of human error.

Hence, financial institutions can better anticipate financial and non-financial risks, such as strategic, funding, legal, political, etc.

Sign up to learn more about Finezza!

6 Ways Digitisation Can Help Curb Loan Defaults in 2021

Lending Lifecycle Management

Lending is the engine that powers the financial growth of all businesses and individuals all over the globe. As the global economies get interconnected and interdependent more every day, the need for funding has also increased enormously.

The number of retail borrowers, SMEs, and business borrowers has surged multifold in the last decade. Though this upward spiral has translated to more income and profit for most financial institutions, not all is green.

In recent times, there is an increasing number of loan defaults, and this has already started taking a plunge at the bottom line of some of the financial institutions.

Depending on the amount involved in the default, the impact multiplies. What can the financial institutions do to curb loan defaults? Well, the answer lies in digitisation.

Key Impacts of Digitisation in Lending on Curbing Loan Defaults

Digitisation of the loan management systems can help curb loan defaults at different stages of the loan cycle. Though most of the lending institutions are in some stage of the digitisation journey, it becomes imperative to expedite the journey to leverage digitisation’s maximum power.

The top 6 strategies that a financial institution can adopt to curb loan defaults are as below:

1. Digital Customer Screening

A good start is half done! Since the customers’ profiles have become more complex nowadays, financial institutions should have robust digital software that can handle the customer screening the best. Depending on the financial organisation’s size and need, they can either go for fully homegrown software or go for off the shelf, SaaS-based onboarding software.

Whatever option the financial institution may choose, the software should have capabilities to do thorough Know Your Customer viz. KYC, document screening to check if there is any forgery, AML check, blacklist check against nationwide and work wide databases.

2. Integrate with Credit Scoring Agencies through Realtime APIs 

Financial Institutions should make sure their lending system is integrated with credit score agencies like CIBIL to make informed decisions before approving loans. Since credit rating agencies like CIBIL have a tie-up with most banks and financial institutions worldwide, a higher credit score can be used as an indicator of the customers’ past repayment behaviour.

Since the credit market is very competitive, sometimes the lending institute skips this important step and repent later. Sometimes the lending systems refer to old records to check credit scores, but this is also not effective. It becomes essential for the digital lending platform to check credit score in realtime to check against the latest data. This should be set as a mandatory gatekeeping step in the loan approval process.

3. Use Technology to do a Thorough Background Check for Business Loans

The loan amount involved in SME and corporate lending is very high compared to individual lending. Hence the bank should have a highly sophisticated digital platform to onboard business customers. The standard, blacklist check, and credit score checks may not be sufficient.

The software should have advanced capabilities to check the business’s creditworthiness ness by doing a detailed study on their past balance sheets. Such a detailed study to get into the nitty grittier can be done more efficiently by software than humans. The digital software should have capabilities to screen through the Key Result Areas or the businesses’ KRAs for the past several years. This should include screening their revenue, assets registries, liabilities, EBITDA, GST filing, etc., for the past several years.

Post-screening all the KRAs, the software should generate a customer risk score, which will act as a guide for the lending institution to decide on the customers’ creditworthiness.

4. Make Repayment Methods Hassle-Free

For recurring EMIs like home loans, personal loans at the time of issuance itself, set up recurring auto-payments on a defined day of the payment cycle. This will reduce the chances of customers forgetting to make EMIs and will naturally curb defaults.

For other types of loans like mortgage loans, gold loans, vehicle loans, etc., the loan repayment is flexible, and most financial institutions do not mandate a fixed EMI. For such loans, where the customers have flexibility in payment, financial institutions should provide enough digital channels like online banking, mobile banking apps, payment apps, etc., to make the payment remotely.

The-Features-of-Peer-2-Peer-Lending-In-India-and-It's-Future-Scope

This will save a lot of time for the customers. For the financial institutions, the more they encourage digital repayments, the financial institutions can optimise the workforce that handles repayment processing at the branches.

5. Timely Reminders Through All Channels

The lending software must have the capabilities to send advance reminders to the customers to maintain sufficient balance on the due date. The reminders can be sent through multiple channels like emails, messages, automated calls, etc., and the customers can plan for the upcoming due accordingly. Reminders can also provide repayment links to enable customers to make repayments easily.

6. Embrace Artificial Intelligence Through the Cycle

AI technologies like machine learning and deep learning can be implemented at different stages of the lending cycle. This could include risk rating, setting personalised interest rates, understanding the customers’ repayment trends, creating custom recovery methods for each borrower, etc.

AI technologies could also be used to set custom reminders, flexible EMI, flexible EMI tenors, etc, depending on the nature and amount of the loan. This way, some of the complex tasks are automated, and the results are predictable. This will help enormously in reducing the loan defaults.

To sum up, with the number of retail and business borrowers increasing in big numbers across all the sectors across the globe, complete digitisation of the lending platforms is the way ahead for the lending institutions to make the repayment easy for the customers and curb loan defaults.

Using AI technologies to do more screening of the customer behaviours will only aid the financial institutes to make their revenue stream from loans more predictable. This also allows financial institutions to deploy their human potential in better revenue-generating areas.

Contact Finezza to know how you can improve your loan default rates with a comprehensive loan management system.