For any lender the importance of credit risk measurement (CRM) is paramount. It is the basis for which a lender can calculate the likelihood of a borrower defaulting on a loan or meet other contractual obligations. More broadly, credit risk management attempts to measure the probability that a lender will not receive the owed principal and accrued interest, which if allowed to happen, will lead to a loss and increase costs for collecting the debt owed.
In simple terms, credit risks are calculated based on a borrower’s ability to repay the amount lent to them. Before a bank or an alternative lender issues a consumer loan they will assess the credit risk of the individual on what is more commonly known as the five C’s: credit history, capacity to repay, capital, and finally the overall loan’s conditions and collateral.
For other debt instruments, such as bonds, investors will also assess risk, often by reviewing its credit rating. Ratings agencies like Moody’s and Standard & Poor use various CRM techniques to evaluate the credit risk of investing in thousands of corporate and state-backed bonds on a continual basis. Ratings agencies use a relatively simple method for conveying the credit worthiness of a bond, with investors looking for a safe investment likely to lean towards purchasing AAA-rated bonds which carry a low default risk. Meanwhile, investors that have a strong appetite for risk, may look at lower rated bonds, more commonly referred to as junk bonds, which carry a significantly higher chance of default in exchange for higher yields than higher rated, investment grade debt.
Increasingly, companies and financial institutions are investing heavily in credit risk measurement, with many spending significant levels of capital to create in-house teams that focus solely on developing CRM processes and tools to better assess credit risks. Over the years, with the rise of fintech, new technology has empowered businesses to better analyse data to assess the risk profile of various investment products and individual customers. But it is important to note that it is impossible for any lender to ever fully know whether a borrower will default on a loan or not. However, by applying relevant risk modelling in tandem with the latest credit risk measurement technology and CRM techniques it is possible to keep default rates low and reduce the severity of losses.
Knowing your customer
In any line of business, it is always worth having a strong understanding of, and good relationship with, your customers, but it is essential for a company looking to succeed in creating reliable credit risk management processes. Assessing an individual or company’s credit profile is only possible if the data that is collected on them is accurate and up-to-date. It is also worth establishing strong relationships with clients as it will ensure that the customer keeps coming back, as well as helping in creating CRM techniques and models that are supplied with rich data sets that will help improve credit risk measurement methods over time.
When opening lines of communication with a company looking for credit, it is important to paint as full a picture of the business as possible. For a lender, it is worth gathering information about the company’s various products and services and its balance sheet, as well as data on the business’ management team, ownership structure and general history. It is also a good idea to build a better understanding of the sector the company operates in and the challenges that it, and other companies in the industry, are likely to face over the coming months and how these may potentially impact the business’ performance and its ability to repay to any loan or credit facility.
Micro and macro risk
When thinking about credit risk it is vital for a lender to understand scale, because the concept of risk management is applicable to both a single loan (micro) or to entire portfolio of loans (macro). For this reason, credit risk managers should regularly check and see how an identifiable risk in an individual loan may have adverse effects on the wider portfolio. By carrying out effective risk management in this manner, it will allow a lender to either growth a portfolio further or limit the size of its loan book to avoid over exposing itself and inadvertently suffering rise in default rates. Therefore, for any lender it is it is crucial to understand that credit risk management is a never-ending process and must be kept a close eye on at both the micro and macro levels to ensure that the institution adheres to three simple, but important principles:
- The institution’s debt-to-capital ratio remains at an acceptable level.
- The lender has priced its credit risk appropriately to ensure it is adequately compensated.
- And that there are adequate CRM processes in place to make sure that credit risk is tracked on a continuous basis to minimise the possibility of default.
One recent example that highlights the importance of CRM and just how detrimental credit risk mismanagement can be for a business is what has happened to Metro Bank in late-January this year after the UK challenger bank revealed to investors that it lacked sufficient capital to support commercial loans on its book following a major risk classification error.
In the bank’s full year 2018 trading update, the lender announced to its shareholders that its risk-weighted assets had increased to £8.9 billion, up from £7.4 billion levels at the end of September last year, with the bank more exposed to riskier loans, including commercial mortgages.
The increased size of its loan portfolio is the result of planned growth, but its book also grew due to commercial property and other specialist loans becoming riskier after the lender mistakenly included them in the wrong risk band, which has impacted its capital ratios.
The UK challenger bank’s total capital ratio is expected to be around 15.8% as of December 31 prior to the announcement, with the recent credit risk blunder removing in excess of 200 base points from its CET1 ratio and sending Metro Bank’s share price falling nearly 40%, wiping away around £800 million off the value of the UK lender.
Credit risk management platforms
Mistakes like the one suffered by Metro Bank are easier to make than many realise. Thankfully, there are numerous CRM software applications that offer a suite of CRM tools. These platforms are used by banks, financial services providers and multinational corporations to help them accurately assess and manage credit risks.
Many of these fintech platforms, of which there are plenty to choose from, offer the same core software features, including tools that help lenders in capturing and spreading financial statements, as well as being able to sit on top of existing IT systems to create bespoke internal credit rating and scoring models. These platforms, by analysing lenders data on a whole host of clients, are capable of in-depth risk assessment that will enable the implementation of complex lending strategies and improved workflows for loan origination and risk monitoring. Numerous lenders are beginning to work alongside technology companies to create advanced credit risk management systems that help them to act proactively rather than reactively to minimise losses and reduce default risk.
In fact, back in December last year, the Dutch lender ING announced that it had partnered with Google and PwC to create an AI-powered credit risk management system capable of crunching large volumes of financial data to find hidden signals that are can alert the lender to heightened levels of credit risk.
The early warning system analyses large streams of financial information to identify how exposed clients are to potential risks, a job that is traditionally performed manually by risk analysts.
“Speed is of the essence in credit risk management,” ING Project Leader Anand Autar said. “The earlier we detect any risk, the quicker and better we can serve clients to prevent losses.”
“Through machine learning, the EWS scans financial and non-financial information, such as news items from all over the world,” he added.
The early warning system being developed by ING is currently capable of processing anything up to 80,000 articles every day from public news sources, with real-time market data from Refinitiv. ING hopes that as its AI-powered system learns over time it will be able to predict credit risks before they materialise.
“The system learns from experience, so in time it will become better at identifying the sentiment of news and developments in the market,” Head of AI and Robotics at ING Görkem Köseoğlu said. “Customers expect more predictive capabilities in their products and services, so for us meeting that customer demand is important.”
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