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How Will Machine Learning Transform Credit Underwriting Software?

Lending Lifecycle Management

Access to credit is a critical element for families, individuals, and businesses to build wealth, achieve economic stability, and climb the economic ladder. The process to gauge the creditworthiness of borrowers or credit underwriting is therefore important to boost the lending business.

Credit underwriting used to be a long and tedious process for borrowers with traditional banking and other offline sources. Today, lending is accessible via digital platforms, which has led to changes in credit underwriting to keep up with the needs of the public. It has eliminated operational bottlenecks, leading to more streamlined lending processes.

The use of machine learning (ML) and other software helps lenders make underwriting decisions faster, with improved accuracy, and with less human involvement. A lender’s ability to draw actionable conclusions from a large pool of data from ML models is key to diversifying and offering high-quality services to clients. In this article, we will understand the importance of automated credit underwriting software and look at how machine learning can help transform current practices.

What is Automated Credit Underwriting Software?

Underwriting is central to the credit business. An automated credit underwriting software helps loan officers calculate an applicant’s credit score based on their annual income, credit history, and assets.

Automated processes like Robotic Process Automation (RPA) and machine learning help Fintech companies collect client information and recommend loan approval conditions based on that information.

Using machine learning underwriting models, many small business owners and consumers can have their creditworthiness assessed and decided with ease.

Recommended for you: Credit Underwriting Made Easy – A Comprehensive Guide

How Machine Learning Simplifies the Credit Underwriting Process

With exponentially increasing data and computing power, machine learning now has a huge impact on our everyday lives and a good example of this is credit underwriting.

Banks and lenders can use ML and AI solutions to evaluate creditworthiness to increase credit availability and/or reduce credit costs. Lenders typically have a lot of data on an applicant when they underwrite the loan, which can be leveraged to improve underwriting models, reduce the cost of customer acquisition, and establish early warning systems. Some of the benefits of incorporating Machine Learning include:

  • Enhancing the accuracy of predicted default risk to provide credit to more creditworthy borrowers.
  • Verifying customer profiles with ease.
  • Eliminating human errors.
  • Minimising defaults and losses.
  • Reduced mispricing because of inaccurate estimations of default risk, along with improved credit terms offered to some applicants.
  • Developing and updating models more rapidly and more cost-effectively.

With alternative data, such as rent or mortgage payments, lenders can provide loans to more people. Although they may be good candidates to repay a mortgage, they might have been rejected otherwise because, for example, they lack a traditional credit history.

Machine Learning in Credit Underwriting Software

Here are some processes machine learning and related technologies can play a huge impact on:

1. Loan Application

With machine learning, lending firms and financial institutions can find complex patterns and relevant variables in the data that were not visible before. Access to this type of information allows lenders to select borrowers for loans.

A credit risk assessment based on such information can be more accurate than conventional rules-based underwriting. ML models can track and monitor all incoming payments to track defaulter loans and calculate a loan default score as time progresses. In cases of high-risk customers, the software prompts loan officers to look for alternatives.

Recommended for you: Automated Loan Processing: What is it and How Can It Benefit Lenders?

2. Documentation

Consumer mortgage lending has always been paper-intensive since thousands of documents are required for each application. In mortgage lending processes, these requirements arise because organisations are dependent on various databases, workflow tools, systems, and reports, which require them to access various resources in order to complete specific processes. Machine learning can automate routine tasks and set criteria for whitelisting potential customers, approving or rejecting borrowers by scanning documents and auto-categorising them without any human intervention.

3. Credit Assessment

ML-based automation allows lenders to program the creditworthiness and risk parameters into an automated lending solution, enabling them to replace conventional credit assessments, which are time-consuming and error-prone. The automated mechanism allows even relatively inexperienced personnel to screen loans quickly, thereby providing a faster turnaround time for customers.

Small businesses that may not fit into conventional approval criteria can benefit from credit risk assessments with extensive customisation to suit different types of loans with the predictive power of machine learning.

4. Loan Disbursal

Usually, banks and non-bank financial companies take a few weeks to approve a loan through traditional lending platforms. Digital lending and the combined power of RPA and ML allow users to receive approval in minutes. These technologies make use of factors like online purchases, utility bill payment history, and social media profiles to approve the customers who meet the criteria.

Wrapping Up

Lending is a vast business, and technologies such as machine learning help harness huge amounts of customer data and find patterns in borrower behaviour to determine creditworthiness without human intervention. This not only helps in assessing risks but also in scaling the loan approval process and reducing delays in processing and credit decision-making time. This can be a boon for lending companies to decrease defaults and increase approvals with the help of custom ML models trained on their proprietary data, thereby driving profitability.

Finezza provides lending lifecycle and credit evaluation platforms that help banks streamline and optimise their transactions. Contact us to know more about the customised solutions we offer.

How Machine Learning Can Transform the Face of Financial Services?

Updates

Machine learning is enthusiastically integrating into our lives and changing how we make decisions every day. Machine learning technologies leverage historical data and user behaviours to predict patterns and make automated decisions. The rapid pace at which the machine learning technology is accelerating is leading businesses to change the way they operate.

Applications of Machine Learning in Financial Services

Machine learning in finance has been making the rounds successfully due to its ability to tailor the user experience to suit the unique needs of the customers. The retail banking sector can thrive with machine learning capabilities like automated fraud detection and creditworthiness assessment, etc.

Machine learning in finance when paired with technologies like predictive analysis, escalate the scope of personalisation, product recommendations based on behaviour patterns as well as facilitate routine transactions like paying bills and account checking.

Although the traditional financial industry was not the front runner when it comes to ML adoption, machine learning in the fintech sector is common.

Here are some of its most common applications:

Robotic Process Automation

The transition of lending companies from traditional approaches to cloud-based systems brings forth a tremendous opportunity for further growth in the industry. Moreover, the advent of machine learning technologies can further automate the client-facing process at a lending firm. Fintechs can attain optimal operational efficiency by integrating machine learning platforms or aggressively data-centric processes and save time as well as resources.

Besides automating client-facing operations, machine learning can interpret documents, analyse complex data and come up with intelligent solutions for complicated problems. Machine learning technology’s predictive powers can identify issues without any human intervention even before they occur. A lot of data searching and sorting, which took hours to perform manually, can now be done in minutes and even in seconds. Through real-time edit for error resolution and regulatory compliance systems backed by machine learning, the lending businesses in India are looking at safer and more efficient services to their customers.

Fraud Prevention & Cybersecurity

Financial lending institutions struggle with managing enormous data, adhering to compliance requirements and enabling security. The service providers are expected to protect their clients against fraudulent activity. Bearing the cost of fraud and the burden of recovery and other associated costs can hamper a financial business’s profitability. Instead of counting on outdated approaches, predicting and preventing fraudulent transactions is the need of the hour. ML-powered sophisticated commercial business management solutions come with the ability of processing high-volume data. Data analytics using machine learning can help firms overcome such challenges by red-flagging unusual user behaviour in case of suspicious activity, and thus minimising the risk of fraud, money laundering, or a data breach.

Businesses can secure their precious financial data using machine learning security solutions. ML-backed intelligent pattern analysis and significant data capabilities make it a better choice than traditional commercial software and security solutions.

Loan Underwriting

Modern financial lending companies seek the help of machine learning technologies to identify risks and set viable premiums on the loans sanctioned. The machine learning technologies make predictions based on historical patterns and current data analysis using intelligent decision-making algorithms. This use of machine learning technologies during loan underwriting can help improve business profitability for financial lending companies.

Digital Assistance

An after-effect of machine learning and deep learning, automated chatbots have taken over the market by storm. The term “robo-advisor”, which was unheard of a few years back, is slowly replacing the need of human financial advisors in India. These are cheaper and more accessible and serve customers 24×7. While this technology is still at the nascent stage, investors are already looking forward to end-to-end digital services, with no dependence on human agents or physical interaction, and a hands-off, data-driven approach to investment selection.

Fintechs and Machine Learning

FaaS-The-Emerging-Global-Trend-of-Fintech-as-a-Service

The unique ability of a computer program to learn by itself and improve over time creates new use cases for financial sectors. While the financial industry continues to leverage the advanced machine learning and predictive analytics based solutions, the volume of data being generated by the financial players keeps growing day by day. With time protecting the data available in large quantities as well as safeguarding other sensitive assets will be the biggest challenges that machine learning in finance will be expected to tackle. Both banks and other financial institutions strive to streamline businesses and improve financial analysis through ML adoption of machine learning technologies.

Today, as many lending institutions realise the varied virtues of machine learning, they look for solutions that incorporate machine learning technology. At Finezza, we believe in finding novel and creative ways of serving our clients and keeping abreast with global trends. We offer a unique framework designed to help Indian lenders digitise the lending process.

It’s common for digital lenders to execute data extraction from documents, especially KYC. Traditionally, such materials are collected and submitted physically at the lender’s office. They are scanned, uploaded, verified and tagged with the help of the manual workforce. There is scope for error in manual data extraction processes and affects the efficiency at the lending firm. As the need for credit expands in the economy, the demand for data extraction will also increase. The process of manual data extraction is, however, inefficient and leads to a wastage of time in executing the simplest of tasks like extracting Address from an Aadhaar Card. Such lengthy processes during loan origination may take the focus of Feet-on-street (FOS) agents away from engaging the customer. Also, with human intervention, a certain monotony sets in with repetition, and can ultimately lead to negligence.

The Finezza framework comes equipped with a machine learning backed document recognition module. The document recognition module is developed using Deep Learning. The software identifies the type of document from the following eight types:

  1. Aadhaar Card
  2. Pan Card
  3. Cancelled Cheques
  4. ITR Acknowledgement
  5. Establishment Photographs
  6. Balance Sheet
  7. GST Returns
  8. Others ( Documents not belonging to the above categories)

Post ML-powered document recognition, our NBFC software also ensures that field detection is executed before Optical Character Recognition (OCR). Here the data extraction module uses Object Detection techniques to extract relevant fields and also crops the image to reduce the area of interest to the text. With Finezza’s machine learning algorithms, the post-processing exercise is just a simple validation that ensures a productive output.

The software, thus, provides an end-to-end system powered with machine learning capabilities that can populate the required fields of a document in 5-10 seconds. It can also identify human biases and correct them during the loan origination process. Lending companies can increase their operational efficiency and reduce customer onboarding time for the business using Finezza, and it’s machine learning capabilities.

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!

Innovations in Lending: Exploring the Significance of Lending Software in India

Lending Lifecycle Management

It is no overstatement that India stands at the forefront of financial innovation in today’s rapidly evolving landscape. Among the sectors witnessing transformative changes, lending has emerged as a key beneficiary, largely driven by lending software in India.

Projections suggest that digital lending in India will approach a remarkable market cap of nearly 350 billion USD by 2023.

This article delves into the significance of lending software in India, highlighting its transformative impact on traditional lending, future prospects, and key evaluation criteria in the Indian context.

Indian Lending Software: A Revolution in the Making

Let us take a closer look at the lending landscape and the impact of technology.

The Traditional Lending Landscape

Burdensome documentation, protracted approval procedures, and restricted loan availability for a sizeable percentage of the population have historically characterised the lending environment in India.

Due to their reliance on conventional techniques, established lenders such as banks and non-banking financial organisations (NBFCs) had difficulty accessing the unbanked and underbanked segments.

The Lending Software Development 

In India, the introduction of lending software has resulted in a revolutionary development. This software uses modern technology to streamline the lending process, including artificial intelligence (AI), machine learning (ML), and big data analytics.

Adoption of such emerging technology has made it possible for financial organisations to increase their reach, improve client satisfaction, and make wise loan decisions.

How Has Lending Software in India Transformed Traditional Lending Processes?

Traditional lending procedures in India have seen a significant alteration thanks to lending software. Lenders and borrowers alike stand to gain from how loans are created, processed, and maintained due to this digital transformation. As we examine how lending software has changed lending practices in India, we can conclude that:

  • Enhanced Risk Assessment

Lending software combines alternative data sources and uses data analytics and machine learning for a more accurate credit risk assessment.

  • Faster Loan Disbursement

With the use of digital lending technologies, loans can be distributed quickly, with money being deposited into borrowers’ bank accounts in only a few hours.

  • Reduced Operational Costs

Lending institutions’ operational costs have decreased as a result of the automation of processes like data entry and document verification.

  • Improved Customer Experience 

With online applications and digital notifications, borrowers enjoy a smooth and convenient application procedure.

What Are the Key Features to Consider When Evaluating Lending Software in India?

The best digital lending software for your business should provide full capabilities that enable lenders to improve client experience, streamline processes, and make well-informed lending decisions. The following characteristics are essential for top-notch digital lending software:

  • Loan Origination

Streamlined workflows cater to diverse loan types (personal, business, mortgage) with customisable data fields and forms. Seamless integration with credit bureaus verifies borrower data from multiple sources, enhancing lending software’s effectiveness.

  • Workflow automation

Automated approval, underwriting processes, and streamlined loan application workflows are vital components of modern lending software. Decision engines driven by rules efficiently handle intricate lending scenarios, enhancing efficiency and precision.

  • Integration abilities

Seamless integration with payment gateways, financial systems, and external data sources, facilitated by APIs, fosters collaboration with other financial institutions and service providers. Ensuring compliance with industry standards such as Open Banking is essential.

  • Reporting and Analytics

Comprehensive analytics tools offer editable dashboards and reports, providing users with robust data analysis and visualisation capabilities for informed decision-making and enhanced insights.

  • Reporting on compliance and audit trails

Automated reporting ensures compliance with regulations, while audit trails monitor user behaviour and loan application changes, enhancing transparency and governance within the lending software industry.

  • Credit Monitoring and Reporting

The integration of credit reporting organisations provides valuable insights into loan portfolio performance, default risk, and consumer behaviour. With predictive analytics, lenders can anticipate market trends and potential loan defaults more effectively.

What Challenges and Opportunities Does the Future Hold for Lending Software in India?

Lending software in India faces a variety of opportunities and problems in the future as a result of things like technological developments, legal changes, and changes in consumer behaviour.

Here is an overview of some of the major challenges and opportunities:

Challenges

Here are some challenges that lending software in India needs to tackle:

  • Compliance with regulations

The lending business in India is highly regulated, and it is crucial to follow ever-evolving rules like the Reserve Bank of India (RBI) norms. It can be difficult to navigate these laws and innovate at the same time.

  • Management of Credit Risk

Accurately determining credit risk continues to be quite difficult. To reduce default risks, complex algorithms and data analytics must be developed to assess borrowers’ creditworthiness.

  • Security and privacy of data

Data security and privacy are crucial because lending software significantly relies on client information. Tighter data protection regulations and growing cybersecurity threats drive the need for ongoing monitoring and investment.

  • Customer Faith

Gaining the trust of customers is essential, particularly in a market where financial literacy levels vary widely. Gaining and keeping clients’ trust depends on transparency and moral lending practices.

Opportunities

Now, let’s evaluate the opportunities for growth:

  • Innovation in fintech

India’s fintech industry has been at the forefront of technical advancement. There are several ways that lending software can extensively leverage blockchain, AI, and machine learning to improve user experiences and streamline procedures.

  • Customisation

Personalised loan products and payback schedules depending on customer financial problems are available through lending software in India. This degree of customisation can draw in and keep clients.

  • Partnerships 

Collaborations between banks or non-banking financial institutions (NBFCs) and lending software vendors can open up new possibilities. Partnerships between banks and fintech are already taking off in India.

  • Governmental  Initiatives

Government programmes like the Digital India programme and the Jan Dhan Yojana fuel financial inclusion. Software for lending can support these initiatives and take advantage of a rising customer base.

What Role Does Artificial Intelligence Play in Modern Lending Software in India?

Artificial Intelligence (AI) plays a pivotal role in modern lending software in India, reshaping the industry’s landscape. AI-driven algorithms analyse vast datasets, including credit histories, bank statements, and alternative data sources, enabling more accurate risk assessments.

This enhances loan origination efficiency by automating processes such as application verification and credit scoring. AI also supports personalised loan offerings, tailoring terms and interest rates to individual borrowers based on their creditworthiness.

Additionally, AI-powered chatbots and virtual assistants provide instant customer support, improving the overall user experience. Overall, it empowers lenders to make data-driven decisions, reduce defaults, and expand financial inclusion in India.

Summing Up!

The lending software in India is a transformative force in the finance sector, enabling greater inclusivity through digital innovation. It empowers individuals and businesses by removing traditional barriers and expanding credit access. As India advances technologically, lending software remains pivotal in driving economic growth and financial well-being. Digital lending tools like loan management software enhance efficiency and speed up loan approval processes.

Finezza, a cutting-edge fintech platform, stands out as a leading lending software, featuring an enhanced Loan Management System that boosts efficiency and promotes responsible lending. With its capabilities, it instantly streamlines debt collection, and with advanced features like automated bank statement analysis and AI-powered integrations with key financial partners, it elevates lending processes, making it the top choice for lending institutions seeking a comprehensive solution.

Book a demo to learn more!

How Lending Lifecycle Management Can Help Lenders Optimise Finances

Lending Lifecycle Management

The lending industry has become extremely competitive over the last few years. Traditional players such as banks and non-banking financial companies (NBFCs) have undergone digital transformation to keep pace with demand and unlock new opportunities.

Several digital banks, neobanks, digital lending apps, and peer-to-peer lending platforms have mushroomed to make credit more accessible to a wider spectrum of individual and business borrowers, such as micro, small, and medium enterprises (MSMEs). Today, even non-financial businesses offer their customers credit.

In such a competitive lending landscape, lenders must bring more efficiency and accuracy to the entire lending life cycle to help optomise operations. Those who do so have a greater opportunity to gain market share.

This is where an automated Artificial Intelligence (AI)-driven lending lifecycle management solution can help lenders drive revenues and profitability. In this blog post, let’s explore the lending life cycle, the urgency of automation and its key benefits.

What Is A Lending Life Cycle? 

The lending life cycle is the end-to-end process from the time a borrower applies for a loan till the time it is paid off. The various steps in the lending workflow can include pre-qualification, submission of a loan application, loan origination process, underwriting or risk assessment stage, disbursement of the loan, collections, and loan closure.

Each step demands diverse resources and skills – from data entry and analysis of financial data to the customisation of loan products.

As lenders attempt to scale their businesses, access to end-to-end lending lifecycle management solutions is helping them optimise their efforts.

What is a Lending Lifecycle Management Solution? 

This loan processing system automates every step of the lending life cycle. The innovative solution leverages a mix of technologies such as cloud management, AI, Machine Learning, and optical character recognition (OCR) at various stages.

The usage of lending management solutions adds more efficiency, accuracy, and speed to the entire lending process. As a result, lending businesses can run their entire operations streamlined and transparently through a single, cloud-based, unified interface.

Also, it comes with many advantages that have a long-term impact on the sustainability and profitability of lending businesses.

Benefits of Automating the Lending Life Cycle

Digitising the lending life cycle, with a combination of cutting-edge technologies is helping lenders drive their business outcomes more effectively. Here is a snapshot:

1. Higher efficiency 

The entire lending life cycle becomes completely automated, eliminating the need for physical paperwork and human data entry requirements, thus reducing the potential for errors. Such a solution has the capability to recognise data in over 700 file formats.

It can segregate all financial transactions into inflow and outflow categories and further segment within minutes. Human bandwidth can be leveraged in tasks of higher value instead of manual tasks.

2. Accurate Underwriting Processes 

A lending life cycle management tool can process large amounts of financial data within minutes. It enables lenders to make their underwriting operations more efficient in terms of speed and accuracy with limited intervention.

Dashboards can be customised to segregate all transactions into specific categories such as revenues, salary payments, loan repayments, and beyond.

3. Enhanced Fraud Detection Capabilities 

Lenders are increasingly vulnerable to loan fraud due to the use of sophisticated methods and technologies to fake transactions and documents. For instance, borrowers may have indulged in circular transactions to boost their perceived trade value or business valuation to impress an investor.

Such transactions may not be visible to the human eye. However, AI-driven automated loan management systems are equipped to detect irregularities and patterns across a large volume of financial data.

It can also recognise fraudulent documents and fake identities by detecting seemingly minute inconsistencies in data. This level of competency makes it an invaluable asset to today’s lending businesses.

4. Data-driven Decision-Making 

Lenders need to make important decisions based on the financial data at hand. The usage of lending lifecycle management tools simplifies the process by offering comprehensive data analysis to help make decisions.

For instance, it offers a financial creditworthiness score that helps lenders put a tangible value to a borrower.

This mechanism is much more accurate than using existing credit scores, which paint an incomplete picture. For instance, bootstrapped, first-time borrowers can benefit from this feature, which paints a thorough picture of their financial health, which may be good even if they have no credit history.

5. Customisation of Products  

Access to the right also helps lenders customise products for borrowers based on their real-time needs instead of offering one-size-fits-all products.

For instance, the loan ticket size, repayment schedule, tenure, and interest rates can be tailored to suit a business’s needs. However, the loan lifecycle management system makes recommendations, which expedites the process.

6. Speedier Loan Turnaround  

Borrowers today seek a simplified loan application process and turn in loan approvals. Those that can deliver on these goals will corner the market. Lending lifecycle management tools help streamline the entire process, thus allowing lenders to bring down turnaround time. This leads to quicker conversions and repeat borrowers.

7. Cost-effective Operations  

Lending businesses can function with lean teams. They can expedite the lending life cycle, serve more customers, and deliver relevant products. A combination of these shifts results in lower costs and higher revenues, thus fostering cost-effective operations.

8. Scale The Business Faster 

With access to an automated lending lifecycle management solution, lenders can bridge the gaps in their tech stack. They will be in a position to grow their business faster, serve a diverse range of customers, increase lifetime customer value, and boost profitability.

The Takeaway

Many lenders believe that making the underwriting process more accurate and efficient is the key to profitability. However, the answer to optimising finances is to automate the end-to-end lending life cycle.

It must start from the time the loan applicant enters the lender’s ecosystem, offering them a seamless, superior, efficient user journey as a borrower.

The adoption of automated lending lifecycle management solutions can facilitate this process. This approach gives lenders an advantage in a competitive credit landscape.

Finezza offers a comprehensive set of tools and solutions to manage your lending portfolio. Our end-to-end lending management and credit evaluation solutions are trusted by brands like ftcash, Hiranandani Financial Services, gromor Finance, and UC Inclusive Credit, to name a few.

Finezza’s intuitive bank statement analysis software is geared to analyse bank statements with speed and accuracy and detect potential fraud.

Book a demo to know more.

Debt Factoring vs Account Receivable Financing: What To Choose?

Embedded Finance

Uninterrupted cash flow is the lifeline for all businesses; however, it is not uncommon for companies to have considerable gaps between a sale and receiving payment, which can disrupt their liquidity.

As a result, ensuring smooth cash flow can be quite challenging for businesses, especially the ones that allow long credit periods to their customers. Also, these piled-up receivables can result in a severe cash crunch and snowball into other business-threatening issues.

As per Entrepreneur report, more than 80% of businesses fail due to lack of cash; 20% of small businesses fail within the first year, while half fail within 5 years.

In this situation, debt factoring and accounts receivable financing are two options that can help businesses balance their liquidity and credit sales for smooth day-to-day operations.

While both are excellent short-term finance options, they differ in a few aspects. In this blog post, this is going to be our key topic.

What is Debt Factoring: An Outline

Debt factoring, also known as invoice factoring, is a finance instrument that converts outstanding invoices to immediate cash. This option allows a business to sell its accounts receivables to a “factor” (third party) at a discount. The factor pays a pre-determined percentage of receivables upfront and takes up the responsibility of collecting the dues from the customer on the due date.

The factoring company usually pay 75%- 90% of the invoice in advance and the remainder after collecting the dues from the customer. After collecting the payment from the customer, they pay the business the remaining amount after deducting their fees.

Debt factoring helps businesses stabilise their cash flows without compromising on customer relations. The firm need not hesitate when extending credit to their customers or wait for customers to pay, and they need not hesitate when extending credit to them. With invoice factoring, they can unlock the funds tied up in the unpaid invoices and use them for their day-to-day functioning.

What Is Account Receivable Financing?

Accounts receivable (AR) financing is also a short-term finance option that uses outstanding invoices to fund growth. Like debt factoring, AR financing provides the business with an alternative to the traditional line of credit to manage its cash flow requirements.

AR financing is asset-based lending; the invoices act as collateral against which the lender, usually a bank, lends funds to the business. The firm retains the ownership of its accounts receivables.

Generally, lenders lend up to 75% to 85% of the AR value to the business. Lenders charge interest on the loaned amount until the business repays the entire amount.

This is to note that companies generally do not use all their accounts receivables for financing; businesses decide which customer’s invoice they want financed, but they do not choose which specific invoices.

What is the Difference Between Debt Factoring and Account Receivables Financing?

Debt factoring and account receivable financing unlock the funds tied up in invoices, which impact the working capital availability of a business. However, these two finance options differ in the following five aspects:

  • Ownership

A fundamental difference between debt factoring and account receivable financing is ownership of the invoices. In the former option, the ownership is transferred to the factor, and the customer pays the dues directly to them. In AR financing, the business retains the ownership of the invoices and is responsible for collecting the dues.

  • Costs associated with financing

When a firm chooses the AR financing option, it pays a fee for the service, usually a percentage of the invoice value. The factoring option requires the business to pay interest on the outstanding amount and a fee for the service.

  • Invoice value

No minimum invoice value is fixed for the AR financing option; a business can get even small invoices financed. Regarding factoring, lenders are willing to finance invoices above a minimum amount. This means a firm can get only larger invoices financed.

Lenders can manage and track their loans more efficiently with solutions like Finezza’s Loan Management System and service their customers better.

  • Eligibility requirements

Eligibility requirements for both options also differ. Lenders are willing to finance ARs if the business has a good credit profile and a strong financial position. For the factoring option, the customer should have a good credit history and be in a position to pay their invoices on time.

  • Time taken

Factoring is a quicker financing option; it usually takes 24 hours to obtain the funds. Invoice financing has a longer turnaround time, and a business might have to wait for days or weeks to get the funds.

The table below gives a snapshot of the differences between the two.

Difference between Debt Factoring vs Account Receivable

Debt Factoring or Account Receivable Financing: Which Is Better?

As the discussion reveals, both debt factoring and account receivables financing help a business manage its cash flow requirements. So, how does a firm choose the right option for them?

Below are four aspects firms should consider when making a choice.

  • How fast do they need the funds?

Both options are faster when compared to traditional business loans. However, debt factoring allows quicker access to funds, so if a business needs funds quicker, then factoring is the right choice. If they can wait for a while, then account receivable financing could be an option for the business.

  • How much funds do they need?

Debt factoring offers a higher percentage of the invoice’s value to the business than AR financing; thus, it provides a firm with more funds vis-a-vis invoice financing.

  • How much are they willing to pay in charges and interest?

Account receivables financing is comparatively less expensive as compared to invoice factoring.

  • What is the credit score?

Account receivable financing norms are stricter; hence, lenders are willing to approve financing only for customers with good credit profiles.

Debt factoring offers more funds to a business, faster access and may be suitable for firms with a high volume of invoicing, though it may cost more compared to AR financing.

Conclusion

For a business considering debt factoring or account receivable financing as options for cashflow management, it is advisable to compare both options and also get quotes from different lenders to compare costs and other terms.

However, one size never fits all; thus, each business needs to look at its requirements before it makes a choice. There may be situations when invoice factoring may be more suitable for a business, and on other occasions, invoice financing may work better for the same firm.

Web-based Lending Lifecycle Management Platform Finezza offers solutions that leverage emerging technology like artificial intelligence and machine learning for efficient lending portfolio management.

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