Lending money comes with its own set of obligations. Businesses and financial institutions face an inherent financial risk that borrowers may default on their loans. It becomes staggering as a credit risk manager looking to tap into the unexplored potential of new-to-credit and thin-file borrowers.

But how to make sure that the umbrella is well-returned? 

The current conventional credit scoring systems have certain obstacles that hinder individuals who are new to credit or have a limited credit history and those who are underbanked or unbanked from obtaining credit. This group of borrowers is frequently excluded due to their lack of credit history. 

The creditworthiness evaluation needs to be revised to integrate these borrowers into the formal financial system. By redefining how creditworthiness is assessed, financial institutions can bring these borrowers into the formal financial ecosystem and unlock new growth opportunities.

This is where credit risk analysis unlocks the opportunity for financial institutions.

We recently spoke to Anita, who oversees a national bank's digital-first instant loans app targeted at younger millennials and GenZ. She faced a challenge where she could not tap into real-time insights into the client’s financial health.

Despite impressive customer acquisition rates, she observed that delinquency rates had increased drastically in the past quarter. This underlined the critical role played by credit risk analysis in evaluating borrowers' creditworthiness, minimising risk exposure, and making informed lending decisions.

By analysing a borrower's credit history, income, and debt-to-income ratio, she assessed the probability of loan default. With data analytics and advanced technologies, she could quickly identify potential credit risks and take proactive steps to reduce them, ensuring the long-term viability of digital-first lending platforms.

The fundamental question that credit risk analysis aims to answer is whether to extend new credit or withdraw current credit facilities provided to a customer. To make this determination, the analysis must consider the five critical factors to analyse credit risk. These factors are evaluated, either explicitly or implicitly, to assess the creditworthiness of borrowers and make sound lending decisions.

With this guide, we aim to explore the key concepts of credit risk analysis and offer practical tips to help you conduct it effectively.

But first, the 3 W's of Credit Risk Analysis:

1. What is Credit Risk Analysis?

Credit Risk Analysis is evaluating a borrower's ability to pay back a loan and determine the likelihood of default. It involves looking at the borrower's credit history, income, assets, and liabilities to assess the level of risk involved in extending credit. 

This vast analysis is covered by assessing multiple factors, such as the borrower's credit history, income, assets, and liabilities, to determine the risk associated with extending credit.

2. Who needs to do a Credit Risk Analysis?

To answer this question in two words: Any pilot.

The pilots of any project, risk mitigation teams, and financial institutions are responsible for ensuring that the organisation is less prone to risks. Just as a pilot's primary responsibility is to evaluate various factors to ensure a safe flight before take-off, the Product heads, Risk heads, and Branch Managers of Banks must conduct a credit risk analysis to evaluate the creditworthiness of potential borrowers and mitigate the risk of defaults.

3. Why is Credit Risk Analysis Critical?

Keeping the inferences from every financial lender we've spoken to in general, they have a common goal: Mitigate financial losses, improve decision-making, meet regulatory requirements, and maintain a strong reputation.

Identifying potential default risks gives you the leverage to make informed decisions and minimise the risk of defaults. And while certain financial institutions are subject to various regulatory requirements, a thorough analysis ensures they comply with them.

How to Analyse Credit Risk: A 5-step Guide.

The nitty-gritties of credit risk analysis are tweaked for each company and/or portfolio depending on the industry. However, on a deeper level, the critical ingredients of serving a smooth credit risk are the same:

Note: The purpose of this risk analysis isn't to eliminate the risk but to understand its effect on your borrower's ability to repay.

1. Know Your Customers (KYC’s) Before extending credit, it's essential to gather and cross-check all the relevant information about the borrower, including their name, address, income, assets, liabilities, credit history, employment history, any record of fraud, and any other relevant information that may be a question on his credibility. 

This information helps evaluate the borrower's creditworthiness and whether they will likely default on their loan or the financial obligation.

2. Analyse non-financial risks Credit risk analysis isn't just about evaluating a borrower's financial data; it also involves assessing non-financial risks, such as geopolitical, regulatory, reputational, operational, and compliance risks. While these factors might seem insignificant in the initial stages, the lack of taking these into account can backfire in the long run. For instance, post-demonetisation banks saw a surge in fraud because of non-financial factors.

As per the report generated by BHA , ‘India saw 83,638 cases of banking frauds in FY21, involving Rs 1.38 lakh crore. Of this, only Rs 1,031.31 crore has been recovered so far.’

1. Understand the numbers Once you've collected all the necessary information, it's time to analyse the borrower's financial data. This step involves understanding the borrower's financial statements, including their balance sheet, income statement, and cash flow statement.

Here are the stages to analyse the borrower’s financial history: 

A quick background check of the auditor → reviewing the quality of the Balance Sheet, Income Statement, and Cash Flow Statement → analysing financial efficiency of the cash flow drivers → developing projections → (optional) analysing the personal finances of relevant parties.

1. Understand the business In the case of B2B lending, in addition to analysing financial data, it's also crucial to understand the borrower's business. This step involves analysing the borrower's industry, market position, competitive landscape, social status, etc. Understanding the borrower's business helps assess the borrower's ability to generate cash flow, repay their loan, and weather any potential economic downturns.

2. Price the deal Determining the appropriate pricing is crucial for a lender as it ensures that they will be adequately compensated for the risk of the deal. Multiple complex factors determine the final rate: interest rates, moratorium period, floating (primarily loan pricings are based on this), or money movement rates; apart from the company-specific, particular marketplace and economic factors play a significant role in avoiding a lousy deal.

Three Ways to Upgrade Your Credit Risk Analysis Process

Credit risk analysis is only as good as the data it uses. Limitations like limited or incomplete data, changing borrower behaviour, and unguaranteed credit scores can hinder your analysis's accuracy. 

You can improve your Credit Risk Analysis by focusing on the following factors:

1. Enlarging the data pool When assessing a borrower's creditworthiness, lenders typically rely on traditional credit reports, which may not provide a complete picture of the borrower's financial situation. Using alternate data, such as social media data, bank account information, and utility bills, can give a more refined picture of a borrower's financial situation. It also gives additional insights to help lenders make more accurate predictions about a borrower's ability to repay a loan.

For example, social media data can help lenders understand borrowers' spending habits, lifestyles, and social connections, which can provide valuable context about their financial situation. Similarly, bank account information can give lenders a more detailed picture of borrowers' income and expenses, while utility bills can provide insights into their monthly expenses.

2. Make AI and ML work for you Machine learning algorithms can identify patterns and trends in large datasets that are difficult to detect using traditional statistical methods. But if gaps or errors exist in a borrower's credit history, it can be challenging to make accurate predictions.

For example, if a borrower has a delinquent account not listed on their credit report, it can be difficult to assess their creditworthiness accurately. Training a machine learning model on current data can identify risk factors that may not be immediately apparent and develop more accurate credit risk models.

1. Monitoring more than just the finances Borrower behavior is bound to change over time, making it difficult to predict future performance based on historical data alone. For example, a borrower making payments on time for several years may experience a sudden job loss or financial setback that affects their ability to repay a loan.

By tracking changes like these in credit scores, payment history, and other indicators, one can identify potential risks early on and take proactive measures to mitigate them, such as adjusting loan terms or offering financial counseling.

The fault(s) in the risk assessment systems

The effectiveness of the credit risk assessment process depends on the accuracy and reliability of the risk assessment system. Unfortunately, the credit risk assessment system is not infallible, and there have been instances where it has failed, leading to disastrous consequences. 

From the financial crisis of 2008 to the recent COVID-19 pandemic, there have been many examples where the risk assessment system has fallen short. The two biggest flaws in our systems are: 

The two-track approach of analysing credit and market risk separately A giant red flag of the risk assessment systems is the tendency to analyse credit and market risks separately when their interdependence is inordinate. Credit risk refers to the risk of default by borrowers, while market risk refers to the risk of losses due to changes in market conditions.

For example, a sudden market downturn can lead to a higher rate of default by borrowers, which can, in turn, exacerbate market conditions.

Relying exorbitantly upon credit scores The most prominent fault of the two-track approach is that we rely excessively on credit scores. While credit scores are the first thought for any credit risk analysis assessment, little do people know that it isn't extensive. Several factors support this. Credit scores do not consider non-credit factors that may affect borrower behavior.

For example , a borrower with a high credit score may still be financially overextended and more likely to default on a loan. 

A borrower with a high credit score may have multiple credit card balances close to their credit limits, a large mortgage payment, and a car loan. While their credit score may be high, they may need help to keep up with their debt payments and may be at a higher risk of defaulting on a loan.

Notably, credit scores can be influenced by external factors beyond borrowers' control, such as changes in economic conditions or errors in credit reporting.

How FinBox fills loopholes for accurate credit risk analysis

A lack of concrete and relevant data will lead to inaccurate analysis. While credit risk analytics might churn out numerical data, it does not give a clear insight into the borrower’s behaviour.

FinBox’s Device Connect is a cutting-edge technology that helps in credit risk analysis by providing a detailed understanding of the borrower's financial behavior .

It enables lenders to assess the creditworthiness of potential borrowers using alternate data by accessing their banking data and analysing their spending patterns, transaction history, and financial health. This allows lenders to decide whether to approve a loan, set appropriate interest rates, and effectively manage credit risk.

Moreover, it monitors its borrowers' financial health on an ongoing basis. Providing real-time access to the borrower's financial data enables lenders to identify any changes in the borrower's financial situation that may impact their ability to repay the loan. This allows lenders to mitigate their risk and avoid defaults proactively .

DeviceConnect works on automation technology that streamlines the lender’s credit risk analysis process and reduces the time and resources required to assess borrowers. Automating the data collection and analysing the process eliminates the need for manual intervention and reduces the risk of errors. This saves time and money and enables lenders to provide a more efficient and seamless customer experience. Is your company equipped to do an in-depth credit risk analysis? With FinBox’s Device Connect, you can be assured that all the measures have been taken into account to maximise the accuracy of the credit risk analysis. 

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