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Tag: credit risk

What is Credit Risk Modelling?

Posted on by Imarticus Learning

Credit risk modelling is a financial concept where models are created to calculate the chances of a borrower defaulting on his credit repayment. An example is an individual who has taken a credit card in his name; the risk model will speculate if and how he will default on the monthly card payments. And if he does, the total amount that he owes and the total loss to the lender is also calculated.

The use of this type of models that are created using historical data to gauge the probability of a credit default is known as credit risk modelling. It is an important resource for banks and financial institutions to check the credit holding capacity of individuals and businesses. The goal is to prevent losses.

How Does Credit Risk Modelling Define Borrowers?

A risk model essentially divides customers (borrowers) into two types:

Bad borrower

  • Has defaulted their payments several times over a short period of time
  • Has filed for bankruptcy
  • Last payment was more than 90 days ago
  • Associated accounts being inactive

Good borrower
Anyone who does not fall under the ‘bad borrower’ category is placed here
It should be noted that this classification is general in nature. Different organizations have different credit risk models. In some cases, the calculation mechanism of the models can also differ.

How Does It Work?
Credit risk modelling uses two methods to estimate the probability of a defaulting. Here they are:
In Judgmental Method, several factors defining the borrower are assessed. These also help in creating credit scores.

  • Credit history of the borrower
  • The difference of assets and liabilities of the borrower
  • Presence and value of the collateral such as property or gold
  • External factors such as recession
  • Borrower’s sources of income

Credit professionals look at these parameters to get an idea about the borrower. If none of the parameters yields a positive rating, the credit is usually denied.

On the other hand, in the Statistical Method, as the name suggests, statistics and historical data are used to conclude the credit capacity of a borrower. The advantage of this method is that it does not include the factor of bias in its calculation. Borrowers can be sure that they got an unbiased assessment.

Sources of Data for Credit Risk Modelling

Since it is not possible to collect data on a case-by-case basis, organizations depend on a variety of sources for this task. There are three main sources of data in this type of modelling:

  • Demographic – Personal details that are easy to collect as the borrower will furnish them during application (loan or credit card)
  • Individual History – The historical data about that specific borrower will be collected through partner agencies. If the borrower already has another account in the bank, this data and associated behaviour are also used
  • Credit Bureau – Credit score and other details are sourced from a central database

Credit risk modelling helps banks and financial institutions keep their money lending processes in check. It is one of the most reliable methods to prevent fraud.

Also Read: What is Credit Risk Management

How to Reduce Credit Risk

Posted on by Imarticus Learning

Risk is an inherent part of any business activity being carried out. The reward is directly proportional to the amount of risk taken. It is the part of the parcel and no business is immune to risks. In the most basic sense of things, risk can be understood as the uncertainty around a business.
Even the recession-proof businesses are prone to some level of risk, given the current pandemic situation it is very much evident. Risk cannot be eliminated from a business activity but it can be managed and brought down to minimal levels that have very little influence. Enterprises should focus on the concept of calculated risk.

What is Credit Risk?

Credit risk can be explained as the risk of default that arises when the borrowing party fails to meet its contractual obligations. Not being able to repay the loan amount in the specified time frame is counted as a breach of the contractual agreement between the borrower and the lender. It is the probability of loss that can occur when borrowers fail to repay the loan amount.
Credit risk is multi-faceted and can be categorised into three types. The three types of credit risks include default risk, concentration risk, institutional or sovereign risk. All three types entail different types of defaults on the loan amount. At the core of it all lies the failure to meet the obligations of the debt contract, the underlying reason behind this failure to repay differentiates the types of credit risks stated here.

Reducing credit risk

Managing credit risk in the contemporary landscape is a complex process as it involves multiple variables that might influence the business. In addition to this, the globalised economy faces repercussions from all across the globe. Credit risk managers have a crucial role to play in the banking and finance sector.
A career in this domain is highly rewarding given the growing demand for the skills possessed by credit risk managers. Credit risk certification can help you kick-start your career in this domain. The most rudimentary step in credit risk reduction is evaluating the borrower’s creditworthiness. Let’s dig deeper into how credit managers assess the creditworthiness of the borrower.

Conducting Due Diligence

Knowing your customer is the key to reducing credit risk, especially for companies in the banking and finance industry. You must obtain important relevant information about your customers before doing business with them. This will help you identify a genuine customer. You must ask for their historical financial data, the purpose of the loan and their business registration documents.
Credit risk managers also use the CIBIL score to evaluate the creditworthiness of the borrower. A good CIBIL score can easily help the borrower to obtain a loan as it shows a positive financial track record. A thorough due diligence process also includes conducting a reference check and using industry contacts to find out about the financial standing of the person or organisation seeking funds.

Establishing Credit Limits

Establishing credit limits for borrowers help a great deal to reduce credit risk. Setting credit limits work on similar principles of diversification, at the core of setting credit limits lays the idea of limiting exposure from any one party. To establish a credit limit for a party you need to examine their previous financial records.

You can check out some common financial indicators like sales, net profit, gross profit, working capital ratio, liquidity ratio, etc. In addition to this, you also need to analyse the key economic indicators and conduct industry analysis. This will give you  a comprehensive understanding of the prospects of that business and also help to find out the optimum credit limit for the borrower in question.

What Are Widely Used Underwriting Models in Credit Risk

Posted on by Imarticus Learning

Financial institutions around the globe manage and give loans to companies/businesses that need help. But hey have to manage the records of its clients and has to find out the possibility of non-payment. A good financial institution always has an expert team dedicated to this job.

They analyze the data/information of the clients and based on some attributes; they find out the trustworthiness factor on any particular client. This helps the bank to identify those clients who can ditch them in the future and thus they take measures accordingly.

In this article, let us discuss some famous methods which are widely used by people to calculate credit risk.

What is an Underwriting Model?

Underwriting is a structured process which is used by financial institutions/investors to find out the level/degree of vulnerability in terms of non-payment, late payment of dues can occur. It is a type of analytical job. It helps in reducing the chances of credit risk.

Let us discuss various types of underwriting which are widely used.

Widely used underlying models in credit risk

  • Traditional approach – There are many sites and surveys which determine the potential of risk in different sectors. Agencies like S&P, Moody, etc. determine the level of credit risk in different sectors such as mortgage loans, industrial loans, education loans, etc. financial institutions use this data and view the potential of risk according to them only. There is no specialized analytics conducted at the workplace. Such an approach is not bad because these agencies are highly credited and certified.
  • Rating based system – Its formula is the product of Probability of Default (PD), Exposure at Default (EAD) and Loss Given Default (LGD). It gives us the value of the expected loss. Expected loss = PD * EAD * LGD, Where, EAD is defined as the amount of credit given to any particular client. PD is defined as the low approval ratings and bad records which lead to the possibility of credit risk. For default companies, PD is 100%, LGD is the loss faced by the company/firm. A lot of analytical work is done in these types of approaches but they give more accurate results. Many financial institutions have dedicated workplaces and a highly valued job for credit risk analysts.
  • Advanced rating system – It has two types which are as follows:Calculated internally in the bank whereas EAD & LGD are provided by the bank supervisors who can also use various existing frameworks provided by BASEL to determine these aforementioned attributes. A lot of analysis is based on algorithms in this method.Advanced IRB approach in which all the attributes are calculated internally by the Foundation IRB (Internals Ratings Based) approach in which PD is  Bank but the work is mainly automated through good analytical models and frameworks.

The Five Fundamental C’s of Credit Risk

Five basic attributes are used across each model. These are the Credit history of the customer, Capital, Capacity of repayment, Collateral and Conditions of the loan. These C’s are manipulated into mathematical values and institutions find the potential/vulnerability of the credit risk from any particular customer. There are many accords and regulations such as BASEL III, IFRS 9, etc. which help in determining credit risk.

Conclusion

There are many types of fraud activities witnessed by financial institutions. To protect any such incidents, the institutions try to dig up about the client and conclude that if he is eligible for the loan or not. He will get the loan only if the approvers think that he/she can repay in due time.

This protects banks /investors from losses. There is a credit rating for each borrower which fluctuates based on his repayment. If he/she fails to repay, his credit ratings may go below and he/she may be denied a loan in the future. This article was all about widely used models for determining credit risk.

What is the Credit Risk Fund?

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What is Credit Risk?

Before jumping in the details of a credit risk fund it is important to gain some context by understanding what credit risk entails.

Credit risk can be explained as the risk of loss that can occur when the borrower defaults on the loan and fail to repay the loan amount in a specified time frame.

Credit risk mainly arises when the borrowing party do not adhere to the terms and conditions of the loan. There are majorly 3 types of credit risks; this includes credit default risk, concentration risk, and sovereign risk. Now that we know what credit risk is let’s understand what the credit risk fund entails.

Understanding Credit Risk Fund

Credit risk fund is a type of debt fund that invests a majority of its portfolio funds, around 65-70% in less than AA-rated investment tools.

Let’s understand briefly how the rating works. Any financial instrument that has a higher rating is considered as less risky, the better the rating the lower is the risk involved. When risks are lower the return is also lower, so in case of risky assets, one can gain high returns.

The purpose of credit risk funds investing a majority of its portfolio in lower-rated financial instruments is to obtain high returns associated with high risk low rated tools.

Investment in low rated firms stock has a dual advantage; you gain from the higher interest rate and also gain from capital gains when ratings improve. Given the lower investment duration, the interest rate risk is lower. It also has the potential for investors to gain double-digit yields.

Before investing in any credit risk fund it is paramount to understand the taxation associated with these credit risk funds. Dividends distribution tax of around 29% applies to the scheme. Short term capital gains taxes are also applicable to the returns earned within 3 years.

Factors to consider before investing in a credit risk fund

Before investing in a credit risk fund it is important to analyse the pros and cons to find whether its best suited for your financial requirements or not. Credit risk funds generally have a high level of liquidity risk involved. Investors should avoid the risk of concentration by investing in a diversified portfolio. Looking out for funds with lower expense ratio is also beneficial to the investor. Let’s jump into some important considerations for investing in credit risk funds.

Investing in credit funds using diversified mutual funds is advisable, especially when you don’t have the expertise in the domain. Also, a large amount of investment in credit risk funds should be done after proper consultation as there is a high risk involved. Larger funds are advisable when investing in accrual credit risk funds.

The reason here is that the larger funds provide a safety net in terms of diversification and risk spreading. Choosing funds with lower expense ratio is the key, especially for the first time investors.

In addition to this, you should follow the general best practices in investing. You should go for well-experienced and reputable fund managers and investment firms who specialise in credit risk funds. You should always check the level of concentration of the portfolio; it should not be concentrated in securities from a specific group.

Diversification is the key to mitigating the risk here. Investors should keep the high risk-return profile of this fund and those who are risk-averse should not opt for credit risk funds as an investment tool. People who fall under high tax slabs can consider investing in credit risk funds for saving on taxes.

Credit Risk – Building On a Foundation of Quality Data

Posted on by Imarticus Learning

As larger and increasingly complex stores of information are being gathered and maintained, Data Quality (DQ) often tends to slip. This is indicative of the fact that, while data collection and storing is crucial, it’s only the first step. For data to yield any benefit to an interested party, it must be turned into insightful information that is meaningful and understandable to the right set of people. When it comes to credit risk analysis, the stakes are even higher.

What is Credit Risk?

Credit risk is defined as the risks of loss that have the potential to occur if any party fails to function by the terms and conditions of a financial contract. Primarily, this focuses on the failure to pay off loans due to any lending entity. Any credit risk course worth its salt would attest to the fact that quality data is the foundation of credit risk analysis and mitigation.

Why Does Quality Data Matter?

When it comes to evaluating credit risk, the most critical task is to gather the necessary information for credit risk analysis and reporting in order to correctly. This is necessary to perform the appropriate assessment and review and to use this information efficiently to determine credit risk in order to prevent future losses.

To most industry players, the consistency of data forms the base of the systems dealing with credit risk analysis. It may come from an in-house database or from online outlets, such as websites for businesses. Alternatively, a company may buy performance ratings from a third-party provider for different markets or areas and can offer a detailed review based on fixed criteria.

Quality issues be eradicated at the earliest stage of the credit risk operation. Errors implemented during the credit risk appraisal stage, either due to input errors or a flawed compilation mechanism, will affect the organization on multiple levels– a risk no entity can afford.

Regulators, too, have become much stricter in their scrutiny, forcing risk management companies to pull up their socks and pay closer attention to data quality. Particularly, according to Moody’s, a greater emphasis is sought on data accuracy, traceability and granularity. Their scrutiny will also extend to the maintenance of central stores of data that is auditable.

Hiccups in the Quality Process

New data is continuously emerging, so quality checks must evolve with them. Data quality is not a one-off, open-shut process. It requires continuous tweaks to accommodate new data of differing kinds. With the advancements in technology and more interconnected cities, we might well see new platforms of data emerging, which ideally should mean new vetting processes.

Secondly, data will continue to diversify on many levels, from economic to geographic. Even existing data can be enriched by newly-found information, bringing in perspectives that could make or break credit risk management plans. While this does offer a much clearer picture of credit risk outlooks, it adds a further level of complexity to the system, which will need to be accounted for during quality assurance processes.

Operational Flows to Improve Data Quality

Checks must begin right from the data extraction stage, by verifying the authenticity of platforms used. Logical algorithms can be leveraged to profile these data dumps and create a picture of the overall data quality.

This is also the right stage to identify inaccuracies and define what to do with them. Erroneous data can be removed or modified as the analysts see fit. It’s also vital to identify duplicate data and remove them; allowing such data to pass through the sieve will likely create skewed results at a later stage.

Automated software and tracking algorithms can be created to execute this process over new data, weeding out incorrect occurrences and enriching existing data with new findings.

Conclusion

Accurate credit risk assessments and consequential plans are hinged on the quality of raw data. Therefore, it is imperative that risk-prone organizations collect data from authentic sources and service the right kind of information from the data dumps.

What Is Credit Risk for Banks?

Posted on by Imarticus Learning

What is Credit Risk?

One of the primary functions of commercial banks includes granting loans and advances to its borrowers, the borrowers can be individuals or corporations. Credit risk can be defined as the risk of default or non-compliance to legal contractual obligations on the borrower’s part. Simply put, it’s the scenario where the borrower fails to repay the borrowed amount to the bank within the period agreed upon previously as mentioned in the contract. Banks can also face credit risk situations on account of other cases such as interbank transactions, trade financing, exchange transactions, etc.

In more traditional terms, credit risk means that a lender may not receive the owed interest and the principal amount from the borrower which might lead cash flow interruptions for the lender and increased cost of loan collection.

It is hard to predict the risk of default in many cases but it can be assessed based on the buyers profile and transaction history to make more informed decisions and mitigate the losses. Credit risks are calculated by factoring in the borrowing party’s ability to repay the loan as per the give terms and conditions. The 5C approach is implemented by the lenders in the assessment of the credit risk; condition, capacity, capital, collateral, credit history.

What causes Credit Risk?

Now that we know what credit risk is, let’s find out what causes these undesirable credit risks for banks and other financial institutions.

Credit Concentration

The credit concentration factor is one of the primary reasons why we see cases of high credit risk in banks and financial institutions. In a layman’s term, it can be understood as putting all your eggs in one basket. So what does putting all your eggs in one basket does? Well, it increases the probability of loss because if you drop the basket you might lose all the eggs at once. Similarly, when a bank or a financial institution is focused on providing loans and advances to a specific sector, industry or community it might face high credit risk in case of an uphill event in those sectors or industries.

Let’s understand this situation with an example; suppose that a bank is only providing loans to real estate borrowing needs. Now if an economic downturn happens and there is a slump in the real estate sector, the bank will not be able to recover the amount and since it only provides loans to that sector it’ll run a risk of closing its operations.

The Credit Issuing Process

The credit issuing process is very crucial to the subject of risk assessment. This is the first stage where risks could be mitigated by proper assessment of the borrowing party as per the guidelines and norms of the banking institutions. The loopholes in the credit-issuing process can be due to various factors including the bank’s credit-granting policy and the monitoring process. Let’s find out about some of the loopholes in the issuing process that can cause major credit risk problems.

Inadequate Credit Assessment

Generally, banks take the 5C approach before granting credit to the borrower to asses the credit risk associated with the lending process. The 5Cs includes collateral, capacity to repay, capital, condition of loan and credit history. If any information is missing on these 5 grounds then the Credit Assessment process will fail to predict an accurate picture of the borrower’s ability to repay the loan amount.

Incompetent Monitoring

Monitoring is more like an after-sale service that benefits the bank. Monitoring has two aspects; one includes overviewing the repayments made by the borrower to ensure that it comes promptly. The other aspect includes monitoring the status of the collateral or security pledged to the bank for the credit amount. Generally, people take the loan against property, the value of the property can deteriorate over time and it might increase the probability of default given that the buyer will have less to lose from the default than earlier.

Also Read: What is Credit Risk Assesment

What Is a Credit Risk Analyst?

Posted on by Imarticus Learning

Before jumping over to who is a credit analyst and what are their roles and responsibilities let’s put things in context and understand the basics of the credit analysis process starting with credit risk.

What is Credit Risk?

Credit risk can be simply defined as the risk of default on the debt amount when the borrowers fail to make the required payments as per the contract. The loss accounted could be partial or complete leading to disruptions in cash flow for the lender and increased cost of collecting the loan amount.

In an efficient market, the cost of borrowing varies with the degree of credit risk associated based on the borrower’s profile. A credit check is usually performed by the lender before advancing any credit to the borrower; the credit assessment is based on various parameters that can help determine the repaying capacity of the borrower.

The process of credit analysis

For any lending institution, it is important to evaluate the credit risk profile of the borrower to minimize and cut down on its losses. The process of credit analysis helps in assessing an applicant’s credit request or debt issue from companies to establish the credit risk associated with them. It is a method that aids in evaluating the creditworthiness of an individual or a corporation.

On the technical side of it, the credit analysis process includes applying various financial analysis techniques, creating future projections and evaluating future cash flows. It also involves judging the candidate on multiple aspects such as credit history, collateral provided ability to manage the loan amount, other sources of repayment, etc. The probability of default on the debt and extent of loss in case of default is also calculated by analysts to depict a holistic picture.

Credit Analyst Roles and Responsibilities

Now that we are familiar with the credit analysis process let’s see what does a credit analyst does? To put things in context here, a credit risk analyst is a person responsible for carrying out the whole credit analysis process.

From a broader perspective, the role of a credit risk analyst involves reviewing and assessing the financial history of a person or corporation to determine if they are a good fit for the desired borrower profile. The job of an analyst here is to determine the risk of default to the lending party.

It’s not always black and white while determining the credit risk profile, there is a big grey area in most of the cases. A credit risk analyst can’t simply say yes or no to a loan application. After their comprehensive assessment, the loan is structured depending upon the creditworthiness of the borrower, a risky borrower could be given a loan at a higher interest rate.

The credit analysis process involves a series of steps to be carried out by the analysts to present a holistic picture. The first step deals with relevant information gathering; this includes collecting relevant personal information about the applicant, gathering information about the business for which loan is required and information regarding the source of repayment and the collateral pledged to the lending party.

The second stage of the analysis process deals with analysing the information collected. This includes analysing the accuracy of the information gathered, the financial stability of the borrower, the effectiveness of the project/business, the possibility of repayment of the loan amount.

The final stage deals with the decision making step by the analysts based on the credit risk associated with the applicant, if the credit risk is in the acceptable range then the loans are granted, if not, the request for a loan is denied by the credit analyst.

Credit Risk – The Bank Data Challenge In Frontier Markets

Posted on by Imarticus Learning

The contemporary world is powered by data and advanced technology. The concept of the global village has been further strengthened in the past decade with the proliferation of internet technology. Every industry that relies on the use of modern technology is leveraging data to grow exponentially. Be it the e-commerce industry, the marketing industry or the finance industry, they have all changed drastically in the last decade. Today we have tools like contextual banking and Robo-advisors only because we have a huge database to rely upon. Let’s delve deeper into the role of big data in banking.

Big Data In Banking

Banks and financial institutions today rely heavily on data to make more informed, improved and result-oriented decisions in the business. From analysing customer behaviour to predicting market trends and improving organisational processes, there is a whole lot that the banking industry is using data for. With the advent of AI and Machine Learning, improving internal process and increasing general efficiency has been a piece of cake. Robotic process automation technology also helps to automate a whole lot of work that was earlier performed by using human labour. This helps the organisation to save a lot of time, money and reduce the error to zero which is highly beneficial to players in the banking and finance industry. In addition to this, it also helps in boosting cyber security and eliminating the risk elements.

Data Challenge In Frontier Markets

After the global financial crisis, the banking and finance industry has been very rigid in terms of regulatory requirements to avoid any kind of unfair practice that may lead to future crises. Minimizing the credit risk has been a priority for all financial institutions. From a broader perspective, the process of credit risk assessment includes gathering relevant information about the borrower, analysing the information collected and then making a decision as to whether the credit risk profile of the borrower is acceptable or not. On the technical side of it, this requires applying various financial analysis techniques and predicting future cash flows based on the data obtained.
The whole credit risk analysis process can be only as good as the information collected about the borrowing party. In the case of frontier markets, collecting the relevant data or information is a challenge. The accuracy of information gathered is questionable, which further adds up to this blunder is the challenge of consistent analysis across the credit management system. A truly effective credit risk analysis requires the right kind of information

The Basel committee guidelines have set certain standards and regulations that are to be followed by banks to maintain a healthy global economy. These measures include maintaining the required capital reserve amount, putting a risk evaluating methodology in place and explaining the same to authorities. As a result of these benchmark standards, players in this industry require sufficient data to back their judgment, satisfy the regulatory bodies and maintain their presence in the global markets.

Apart from the seasoned players, there are many newbies in the industry especially in the frontier markets where there is comparatively less competition. The big players already have years of experience and relevant data to adhere to guidelines and make accurate predictions. When it comes to the nascent players in the industry they face the challenge of default data shortage for various asset classes and other relevant data from clients.

Another challenge that the banks or other financial institutions face in the frontier markets is having a proper model in place to analyse both quantitative and qualitative aspects of the data gathered. Quantitative data can be easily evaluated and assessed, when it comes to qualitative measures it’s a tough nut to crack. For example, how do we compare and incorporate the effect of weak management vs. robust management practice? The need to build models that can easily incorporate qualitative aspects of information is paramount.