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How Digital Footprint Data is Shaping the Future of Credit Scoring?

Lending Lifecycle Management

With changes in the way transactions are processed and payments are made, the system for accessing risks or loan underwriting must also change. Lending companies are undoubtedly a susceptible target of fraudsters, especially in the times of digital banking. Where traditional credit scoring fails miserably during digital lending, digital footprints can help make up for missing data.

Legacy Credit Scoring Model

All credit bureaus perform credit scoring for risk assessment. Credit scoring helps lenders figure out how the borrowers can pay back the loan in future. Legacy credit scoring models factor in income statements, spending habits, debt history, and card usage patterns. With credit scoring, credit bureaus aim to create a model that is for to predict how safe it is to lend to a particular borrower.

Here are some of the reasons why the legacy credit scoring model needs an upgrade:

  1. Data from traditional credit bureaus can be falsified or stolen. For example, identity fraud and synthetic ID fraud are the biggest deterrents to traditional credit scoring models. Cybercriminals can easily take someone’s real ID, including someone’s name, social security number or bank account and use it in combination with fictional data to fulfil their plots. They often take leverage of details of individuals who have a spotless transaction history.
  2. They leave no stone unturned in borrowing money from financial lending companies who lack security measures.
  3. Advanced fraudsters go to the extent of testing data through their background check systems like paid services for credit checks online. Fraudsters usually build their application file using a stolen credit card and then access themselves using such services available online. They purposefully use means to score high on credit rating and apply for loans to ascertain that their repayment rate is higher than other loan applicants.
  4. There have been instances in the past, where fraudsters even get access to the questions from background check companies, and craftily manufacture the betting user profiles to meet all the enlisted criteria for loan approval.

Legacy systems and their archaic processes limit banks and Fintechs. Most of the traditional credit scoring algorithms use logistic regression to churn scores and have the capacity to handle only about 30-50 variables at best, which keeps them from assessing comprehensive data for risk assessment.

What is Digital Footprint Data?

A digital footprint can be described as any collection of information that can indicate the same user as social media profiles, web cookies, or even specific patterns in online behaviour. When it comes to fraud prevention, factors like purchase history, browser used, and IP address also make useful components of the digital footprint data of an individual.

Limitations of Digital Footprint Data

Although the penetration of financial services has seen a surge in recent times, some still do not have access to them. For example, when it comes to developing countries of the world where a chunk of the population is still unbanked, there is an acute shortage of digital footprint data for loan companies to access. Even in developing nations, some people still can’t afford to pay banking fees, do not trust financial institutions, do not have adequate documentation and just use a friend’s or family member’s account for banking transactions.

How is Digital Footprint Data Shaping the Future of Credit Scoring?

How-to-use-Finezza's-Credit-bureau-data-analysis-to-do-Early-warnings-wrt-to-borrower-quality-and-save-money

1. Alternative Data for Credit Scoring

Digital footprint analysis is a process that uses information from individuals’ Internet usage patterns to understand who they are. As the adoption of digital mediums is increasing day in day out, this can be an invaluable source of useful data, even in countries where the percentage of unbanked citizens is high.

KYC is an excellent example of how a digital footprint can be accessed. The device used to fill out a KYC form can be useful for lenders. The equipment used to connect to the lending website presents info about whether an applicant is using private mode or an emulator. Through KYC form, lenders can also assess email profiles and check if it is from a suspicious domain or if it requires any additional verification during the signup process. Phone analysis allows lenders to check if applicants are signing up with a real phone number, regardless of whether it is from a fixed line or mobile and also if they are using that number for messaging services. KYC also facilitates IP analysis that verifies the origin of the connection.

Although using alternative data making up for digital footprint may seem extra, it can help ward fraudsters away. Newer credit score evaluation algorithms can easily multivariate and can mine, structure, weigh, and use rich data for futuristic credit scoring. Modern lending companies analyse various data sets to assess the creditworthiness of consumers applying for loans. Other alternative data sources include online presence, smartphone metadata, psychometric data, social media data, remittance history, utility bill payments, e-commerce merchant rating, etc.

2. Predict Credit Behaviour

Borrowers in emerging financial markets often have a negligible credit history. Digital footprint data can help lending institutions sanction loans to such individuals. Moreover, the use of advanced data helps Fintechs leverage a mix of historical data with new consumer data to predict credit behaviour aptly. With the use of data analytics, these diverse data sets can be used to derive relevant consumer insights.

Use of Artificial Intelligence (AI) and Machine Learning (ML) technologies has helped Fintechs shape a new wave of credit accessibility for consumers and small businesses alike. AI can help financial institutions ‘learn’ more about their consumers to make intelligent credit decisions. Combined with ML, AI can help screen the prospective borrowers by predicting their spending and saving behaviour accurately. Lenders benefit from lower NPAs, easy turnaround times for processing loans, and better ROIs.

3. Better Customer Experience

Lenders struggle to find a competitive edge in the markets with an increase in the number of players. Trying to deliver the best in class customer experience, Fintechs can leverage digital footprint data for personalisation and high-quality customer experience. Efficient data mining and analytics help loan origination systems in assessing the risk level that a prospect faces and can suggest ways to personalise a loan product to suit their needs in the best possible manner. Financial lending institutions can also devise collection strategies according to the customer’s credit risk level.

Key Takeaways

Fintechs are trying to build a robust credit scoring model that’s viable with their scaling operations. However, there is a universal distrust in the information they acquire through standard processes.

Finezza is a brilliantly designed analytics suite that incorporates various ways for credit scoring using digital footprint data. The SaaS platform facilitates data sourcing based on interactions and needs of different lending companies who decide to leverage its conveniences.

The Finezza ecosystem uses a winning mix of both traditional and alternative sources of data for efficient credit scoring. The platform conducts an in-depth analysis of underlying financial transactions of the applicant and supplies a practical index that reduces the NPAs for lenders.

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!

Streamlining Loan Approvals with Automated Credit Underwriting Software

Loan Origination System

Recently, the RBI’s June Financial Stability Report revealed that retail loans in the Indian banking sector experienced an impressive compounded annual growth rate (CAGR) of 24.8% between March 2021 and 2023. A pivotal reason for this growth is the ongoing transformation of the traditional and time-consuming manual loan approval process, driven by the adoption of automated credit underwriting software.

This technology speeds up loan processing, saving time and resources. It quickly assesses borrower profiles and evaluates creditworthiness through advanced algorithms and data analysis.

This article delves into the evolution, benefits and implementation of the process of credit underwriting software, highlighting its impact on streamlining loan approvals and improving the overall lending experience.

The Evolution of Automated Credit Underwriting Software:

Credit underwriting is the procedure through which lenders evaluate a borrower’s creditworthiness prior to providing credit.

Traditionally, this method entailed extensive paperwork and human judgement and was prone to errors. Nevertheless, the advent of cutting-edge technology and machine learning algorithms has led to significant advancements in credit underwriting software.

The rise of automated credit underwriting software represents a substantial transformation in the lending sector. Lenders started harnessing technology to simplify the underwriting process, enabling them to gather and analyse a more extensive array of data, thus empowering them to make more informed lending determinations.

Benefits of Automated Credit Underwriting Software

Automated credit underwriting software offers a multitude of benefits, not only to lenders but also to borrowers.

1. Speed and Efficiency

One of the most significant advantages of credit underwriting software is the speed at which it operates. Traditional underwriting processes could take weeks, delaying the loan approval process. With automated software, decisions can be made in a matter of minutes, drastically reducing the time it takes to approve a loan.

2. Consistency and Accuracy

Human underwriters can be influenced by personal biases, leading to inconsistent decisions. Automated software, on the other hand, operates based on predetermined algorithms and data, ensuring consistent and objective assessments. This results in fairer lending decisions for all applicants.

3. Enhanced Customer Experience

For borrowers, the streamlined loan approval process offers an improved experience. The convenience of quick decisions and reduced paperwork makes applying for loans more user-friendly. This, in turn, can lead to higher customer satisfaction and loyalty.

4. Cost Savings

By automating the underwriting process, lenders can significantly reduce their operational costs. There is less need for extensive manpower, which translates into savings on salaries and benefits.

Implementing Automated Credit Underwriting Software

Lenders interested in implementing credit underwriting software must take several steps to ensure a smooth transition.

1. Selecting the Right Software

Choosing the appropriate credit underwriting software is paramount. Lenders should consider factors such as the software’s compatibility with their existing systems, scalability, and ability to meet their specific underwriting needs.

2. Data Integration

Lenders must integrate the software with their data sources to effectively use it. This involves securing data connections to access credit reports, financial statements, and other relevant information.

3. Training and Compliance

Lender employees need to be trained in using the software effectively. Additionally, lenders must ensure compliance with all relevant regulations and data privacy laws.

4. Continuous Monitoring and Updates

Credit underwriting software is not a one-time implementation; it requires continuous monitoring and updates to stay effective. Lenders should regularly review their algorithms and data sources to maintain the accuracy and relevance of their underwriting process.

Optimising Loan Approval Process with Automated Credit Underwriting Software

Streamlining loan approvals with automated credit underwriting software involves the use of technology and data analytics to assess the creditworthiness of loan applicants efficiently and make lending decisions.

Here’s how the process typically works:

1. Data Collection

Loan applicants provide their personal and financial information when applying for a loan. This information may include income, employment history, credit score, assets, liabilities, and more.

2. Data Verification

Automated credit underwriting software verifies the information provided by the applicant. To ensure accuracy and authenticity, it can cross-check the data against various sources, including credit bureaus, bank statements, tax records, and more.

3. Credit Scoring

The software generates a credit score or credit risk assessment for the applicant based on the information collected and verified. Traditional credit scores from credit bureaus (e.g., FICO or VantageScore) are often used, but alternative data sources may also be incorporated into the analysis.

4. Risk Assessment

The software assesses the risk associated with the loan applicant. It considers factors such as the applicant’s credit history, income, debt-to-income ratio, employment stability, and any other relevant information. It may also use predictive modelling to estimate the likelihood of default.

  • Decisioning: The software generates a loan approval decision based on the risk assessment. This decision may include the following outcomes.
  • Approved: If the applicant meets the lender’s credit criteria and is deemed a low-risk borrower, they receive approval for the loan. The system may specify the loan amount, interest rate, and terms.
  • Conditionally Approved: In some cases, an applicant may be conditionally approved, pending the submission of additional documentation or information.
  • Denied: The loan application is denied if the software determines that the applicant does not meet the credit criteria or poses a high risk

5. Document Generation

If the applicant is approved, the software can automatically generate loan documents, including the loan agreement, promissory note, and any other required paperwork.

6. Digital Signatures

Many lenders now offer the option for borrowers to electronically sign loan documents, streamlining the application process and reducing the need for physical paperwork.

7. Funding

Once the loan documents are signed, and all conditions are met, the funds are disbursed to the borrower’s account electronically.

8. Monitoring and Reporting

Automated underwriting software continues to monitor the performance of loans in the portfolio. It may also generate reports for risk management, compliance, and performance analysis.

Final Thoughts

In the contemporary digital era, automated credit underwriting software is indispensable, offering rapid, accurate, and efficient solutions that benefit lenders and borrowers alike. Lenders should embrace such technology for a competitive edge, leading to cost savings and enhanced risk management.

The transition to automated credit underwriting is an investment in technology and an assurance of delivering efficient, fair, and customer-centric financial services facilitated through Lending Lifecycle Management Platforms and Credit Evaluation Services.

Finezza, the cutting-edge micro loan management software, streamlines lending processes. It automates applications, credit assessments, and approvals, ensuring security and compliance. It harnesses AI technology to provide tailored analytical solutions, enhancing loan decision-making, reducing lead-to-loan time, and effectively evaluating creditworthiness for faster loan approvals.

Sign up to learn more about Finezza and revolutionise your financial operations with advanced technology.

Can a Virtual Credit Card Affect a Customer’s Credit Score?

Loan Origination System

The rise in online transactions has increased security threats to critical financial information. Virtual credit cards have become popular in recent years as a preventive method. These cards allow consumers to use their credit cards online without the risk of exposing their credit card information.

Consumers, however, are also concerned about the impact virtual credit cards may have on their credit scores.

In this article, we will explore how virtual credit cards work and how they can impact a customer’s credit score.

How Does a Virtual Credit Card Operate?

Virtual credit cards work similarly to traditional credit cards, with a few key differences.

When customers sign up for a virtual credit card, they are issued a unique, temporary credit card number linked to their existing credit card account. The virtual card number can be used for online purchases like a traditional credit card.

However, it comes with an additional layer of security.

For example, the virtual credit card number is temporary and can be cancelled anytime. It is rendered useless if a cybercriminal accesses the credit card information. This measure protects the customer’s actual credit card number from being compromised.

Additionally, customers can set spending limits and expiration dates for their virtual credit card numbers, which helps minimise fraud risk.

What Does a Credit Score Stand for?

A credit score predicts your credit behaviour based on information from your credit reports, such as whether you will repay a loan on time.

Businesses use credit scores to decide whether to offer you a mortgage, credit card, auto loan and other credit products, as well as for tenant screening. Using the score, credit limits and interest rates are also calculated.

Credit scores range from 300 to 850, representing your credit risk. Creditors and lenders use the credit score to determine whether to approve a new account application.

How Does a Credit Score System Work?

If you are eligible for a loan or other credit account, your credit score may affect your interest rate and other terms.

A scoring model determines a credit score range, but it is generally characterised by the following:

  • 300-579: Poor
  • 580-669: Fair
  • 670-739: Good
  • 740-799: Very good
  • 800-850: Excellent

No “magic number” guarantees approval for a new credit account or a specific interest rate from a lender.

Higher scores, on the other hand, typically indicate that you have demonstrated responsible credit behaviour in the past, which may give potential lenders and creditors more confidence when evaluating a new credit request.

Why Do Credit Scores Matter?

When you apply for credit, the credit issuer may check your credit score first. The procedure is the same when applying for a credit card, a loan, or a mortgage.

Therefore, credit reports and scores are reliable indicators that allow a financial institution to assess your ability to repay the debt on time. They may also use internal scoring mechanisms to determine whether an individual is creditworthy.

How to Maintain a Good Credit Score: Insights from Finance Experts

Here are some tips to help you maintain a healthy credit score when using a virtual credit card:

1. Monitor spending

First, Keep track of your virtual credit card spending and stay within your credit limit. This will help prevent over-limit fees, which can negatively impact your credit score.

2. Pay bills on time

Late payments can significantly impact your credit score, so it’s important to pay your virtual credit card bills on time. Automate your payments so you won’t miss a payment.

3. Keep credit utilisation low

Your credit utilisation ratio, or the amount of credit you use relative to your credit limit, can impact your credit score. Keep your credit utilisation below 30% to maintain a good credit score.

4. Review your credit report regularly

Reviewing your credit report regularly can help you identify any errors or potential fraud and monitor your credit score. You are entitled to one free credit report from each major credit bureau every year.

What Is the Impact of a Virtual Card on a Credit Score?

Virtual credit cards have no direct impact on a customer’s credit score.

Credit scores are determined by payment history, credit utilisation, length of credit history, types of credit used, and recent inquiries. Whereas the virtual card generates a one-time-use credit card number for each transaction, so the virtual card does not impact a customer’s credit score.

However, if a customer uses a virtual credit card for a transaction and the merchant reports the transaction to the credit bureaus, the transaction may appear on the customer’s credit report and could affect their credit score.

The main benefit of virtual credit cards is that when a customer uses his or her virtual credit card, the transaction is reported to the credit bureaus just like any other credit card transaction.

Thus, using a virtual credit card can assist in building or maintaining a customer’s credit history, one of the most critical factors in credit scoring.

Final thoughts

The use of virtual credit cards for online purchases is convenient and secure. Although they can adversely affect a customer’s credit score, it depends on how they use them. The added security provided by virtual credit cards can help customers maintain or even improve their credit scores.

Credit scores must be used in the lending process because they provide lenders with information about a borrower’s creditworthiness.

Lenders are using fintech solutions to evaluate credit scores and make lending decisions. It also helps them analyse more data sources and provide a more comprehensive assessment of creditworthiness.

Finezza: Seamless Lending Lifecycle Management and Faster Credit Evaluation

Finezza is one such fintech platform that provides a suite of cutting-edge products, such as the Credit Analysis solution that allows you to evaluate various combinations of data from multiple credit bureaus to provide detailed information about a candidate’s creditworthiness.

Besides improving your credit score, this will also allow you to provide credit to a broader range of clients, which you would have previously rejected based on one credit score.

In addition, reducing the time it takes to process a loan application will result in quicker disbursements and improved loan management.

Talk to us today!

What Is the Main Purpose of the Top Credit Bureaus in India?

Lending Lifecycle Management

The desire to own valuable assets and track debtors have persevered for millennia, and credit lines continue to play a decisive role in the health of economies.

For instance, consumer credit has existed since the days of ancient Babylon. Today, as the Information Age progresses, credit has evolved to fit seamlessly into the financial fabric of society and modern banking structures.

While credit is a widely known concept, the perception of a credit bureau is still new to the masses. What is it? Is it a policing agency that comes after credit defaulters?

This article will see the top credit bureaus in India and their functions.

What are Credit Bureaus, and When were they Established?

Birthed due to the need for sophisticated consumer credit tracking, credit bureaus emerged as information repositories.

These independent agencies collate financial and credit histories, analyse the information and assign credit scores that determine the creditworthiness of individuals. These reports are then sold to lenders, for whom it is critical for making informed lending decisions.

Although credit bureaus have been commonplace worldwide for many years, they are relatively new to the Indian financial structure. TransUnion CIBIL was the nation’s first credit bureau to appear in 2000, while Experian, Equifax, and CRIF High Mark arrived in 2010.

Also read: Top 4 Credit Bureaus in India that Every Lender Trusts

All About Data: Where does it come from, and Who has Access?

Unsurprisingly, a wealth of historical data is required to create comprehensive credit reports that indicate an individual’s financial behaviour and responsibility.

Credit bureaus build a detailed credit history based on personal identifying information, account information, utility bills, and rent payments.

This data is obtained from multiple sources such as creditors, banks, debt collection agencies, lenders, and public information houses. These sources either sell, report, or share the data in question with credit bureaus.

How are Credit Bureaus Regulated in India?

Data sharing is critical for credit bureau functioning and is an essential tool in maintaining financial transparency, preventing bad loans and fraud, and ensuring equitable credit sanctions. It is equally crucial to debt recovery processes and debtor tracking.

However, the sheer amount of sensitive data in question is bound to set off alarm bells, which is why all Credit Information Companies (CICs) in India are licensed and registered by the Reserve Bank of India (RBI). RBI regulates these bureaus to curb any malpractice while sharing data.

Data privacy, the types of data shared, and data sharing formats are standardised according to what is mandated in the Credit Information Companies Act, 2005, to protect consumer and data privacy rights.

Additionally, the Act also requires that all financial institutions with an asset base of more than Rs 100 crore must become members with at least one credit bureau. This mandate binds the institution to report consumer data accurately.

Why are Credit Bureaus Important for the Indian Economy?

The credit information synthesised and shared by Credit Bureaus has multilayered impacts on the society it works upon. But, primarily, individuals and retail owners stand to gain the most from the ensuing reduction in information asymmetry.

With low household credit penetration and approximately 70% of SMEs being underserved, credit democratisation proves to be a revolutionary method of bolstering entrepreneurship in developing economies.

While information asymmetries exclude the population from formalised credit, credit reporting solves this problem by facilitating the free flow of objective data.

With more accessible interactions between consumers and lenders, these financial institutions are empowered to make better lending decisions.

Furthermore, this democratisation creates a more competitive credit market and simultaneously lowers interest rates. The ripple effect of the work done by Credit Bureaus does not begin and end merely with transactional benefits.

Credit reports, scoring, and the benefits that come along with it act as a disciplinary tool that encourages fiscal responsibility and cleaner repayment practices.

A shift in mindset coupled with access to credit can break generational poverty cycles.

Secondly, credit bureaus enhance efficiency on an institutional level since data transparency renders the lending process more transparent.

As a result, financial institutions benefit from lower default and risk rates and have the opportunity to expand services by creating derivative products and a secondary capital market.

Simply put, more credit equals more growth. On the other hand, regulators benefit equally from credit information sharing, as this helps in lender supervision and risk and trend monitoring.

Lastly, the advantages of credit bureaus are far-reaching and can have significant macroeconomic impacts. For instance, large-scale entrepreneurial success fosters the growth of the SME sector, thereby boosting economic growth.

Moreover, the economy is cushioned against recessions, inflation, and market failures with more outstanding capital and adequate liquidity.

Additionally, extensive credit data allows for robust trend-monitoring, thereby helping to disperse and mitigate risk, inform policy changes, and maintain financial stability.

Finally, credit scoring can also help reduce the volatility of business cycles and assists in faster bounce backs from periods of high unemployment.

Why Automate and Digitise Credit Risk Management?

Loan Origination System

Some of the most pressing factors to an urgent need to digitise credit risk management are competition from digitally strong, emerging fintech firms, highly volatile market conditions and cyber security threats that can lead to data privacy issues.

The pace of technological disruption has never been stronger than now, and organisations need to ramp up their credit risk processes to match up with the ever-growing market demands. Here is a detailed insight into why digital credit risk management is the only way forward and some compelling evidence supporting the same.

Why do you need to Automate and Digitise Your Credit Risk Management?

While credit loans are known to be the most vulnerable and potential risk for lenders, they are also one of the significant drivers of revenue for them.

By going digital, innovative lenders are not only ensuring the overall financial health of their loan portfolio but also meeting the different threats coming in from regulators and new competitors.

A study conducted by S & P Global Market Intelligence named ‘Digitization in Credit Risk Management’ includes opinions and data from over 200 professionals worldwide, which provides a good lens on the trending credit risk practices. Here are some of the findings from the survey:

Digitisation Boom Began Pre-Covid

The credit and risk management teams were already necks deep to digitise and improve operating procedures to arrive at better and quality decisions before the pandemic.

A majority, 75% of the professionals, had their complete focus on digitisation efforts even before the pandemic hit to benefit from the same, the report said.

A resounding 70% of the respondents said the pandemic accelerated these efforts even further. This happened due to the rapidly changing environment around us and the increasing bankruptcies that challenged the traditionally managed credit risk systems.

Not surprisingly, a good 71% of people also reflected on the positive outcomes of digitisation being better control over risks and much better management overall, which only adds to the organisational profitability.

Embracing Technology

A long list of delinquent loans and bankruptcies during the Covid-19 pandemic hit quite a few lenders hard to have essential credit information at their fingertips. This was mainly for the MSMEs for which there is a need to have systematic and crucial financial information to make better lending decisions.

Almost half of the respondents, i.e. 51%, are on the lookout for considering a range of new approaches that can be combined with traditional data mining approaches to estimate and predict credit risks.

For example, Artificial Intelligence (AI) and Machine Learning (ML) are the two valuable techniques being eyed by professionals to extract better data points.

More Focus on Automated and Dynamic Reports

Sixty-four per cent of the professionals surveyed are ready to focus on developing more automated and dynamic reports for executive-level decision making.

Since an applicant’s creditworthiness is based on a series of factors, detailed monitoring is required, which is impossible to comply with by relying solely on manual efforts.

In light of this, dynamic and automated reporting is emerging as a ‘must-have’ for any savvy lender of today.

How Companies are Taking Digitisation Efforts 

Digitisation has excellent growth potential in the credit risk management arena. Lenders realise the various challenges and opportunities that come with it and rely on the following ways to achieve digitally sound credit workflows.

Going Agile

Lending organisations are making digitisation efforts a priority. They’re deploying sets of cross-functional, full-time teams that are solely dedicated to producing quality credit decisions to deliver agile projects on time.

By focusing their attention on the seven-step digitisation process, they are on the path to achieving it shortly. The seven steps are:

  • Data management
  • Advanced analytics and automated decision making
  • Smart interfaces
  • Culture
  • Workflow automation
  • Flexible Infrastructure
  • External ecosystem

By completely overhauling their front and back-end capabilities, along with offering unique digital services to their customers, these organisations are on the road to revamping their credit risk management systems.

Partnering with Fintechs

One of the familiar frustrations faced by lenders across the sector is the lack of legacy IT systems. As a result, many fintechs have come to their aid in strengthening their digitisation efforts and, as such, helping resolve issues and create innovative solutions.

As many as 80% of the top global banks have partnered with fintech at some point, out of which 16% pertain only to lending.

Some of the essential and valuable services these fintech to the lending institutions include API integrations, business and SME loans, digital banking, credit scoring, creditworthiness assessment, fraud detection and prevention, short term cash advances etc.

The Road Ahead

The new norm for credit and lending processes is digital, and therefore going digital for managing your credit risk systems is key to thriving. How the organisations manage to achieve that by opting for which route is an individual call that remains solely their discretion.

However, those who will harness the right tools to digitise their credit risk management systems will benefit exponentially and emerge as a true winners.

By introducing proper risk management capabilities, they are ensuring fraud prevention and retaining quality customers.

Finezza’s fraud prevention tools and other services such as loan management services, API integration, credit scoring, bank statement analyser, etc., can help lending organisations manage their lending portfolios seamlessly and efficiently.

Contact us today if you’re an MSME, fintech, or any business looking to streamline loan origination harnessing agile technology.

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