by KJ McAlpin
It is not easy to originate a loan. Digital onboarding is now commonplace, introducing more sophisticated risk factors that continue to grow. As a result, credit underwriting has become increasingly complex and time- and resource-intensive.
Part of the problem is that banks and fintechs find themselves between a rock and a hard place. If they do not put robust credit risk management strategies in place, they cannot adequately protect themselves against financial loss. But if their risk tolerance is too low — and they slow down credit underwriting processes with toomany checks and balances — they risk losing customers. They also risk limiting the range of customer segments that they can extend credit to.
Understanding different types of credit risk, and how they represent distinct aspects of risk exposure, is vital for lenders who want to leverage more effective risk assessment models and allocate resources appropriately. Failing to differentiate and address specific credit risks can lead to poor lending decisions, higher default rates, and increased financial vulnerabilities.
What are the four main types of credit risk for banks and fintechs?
Lenders must consider several key types of credit risk during loan origination:
In short, lenders need to decide if an applicant is the person they claim to be, if they can pay, if it is beneficial to originate their loan, and whether or not the applicant comes with more risk than the bank or fintech is willing to tolerate. Let’s break down each type of credit risk in more detail below.
What is fraud risk?
First and foremost, an applicant's identity needs to be verified to prevent instances of fraud. Fraud occurs any time malicious actors use intentional deception to mislead them and gain access to financial services and products. Banks and fintechs face the challenge of distinguishing legitimate applications from fraudulent ones. They also must be cautious of whether the borrower is likely to commit first-party fraud, where they take out a credit card or loan in their own name without the intention of ever repaying the funds. Fraud risk takes into account all the possibilities that a bank or fintech will face fraudulent activity, and calculates the likelihood of these threats and attacks occurring.
Why is fraud risk management so important?
Fraud often results in significant financial losses. In Alloy’s Annual State of Fraud Benchmark Report, 100% of respondents experienced fraud in 2022, and 96% experienced financial losses due to fraud. 70% lost over $500,000, and 27% reported losses of over $1 million.
These numbers don’t even include the hidden costs of fraud:
In short, one fraud incident can continue to cause significant damage over time.
More sophisticated fraud threats are also on the rise. For example, fraudsters are bolstered by better technology that they use to create synthetic identities. This makes it harder for banks and fintechs to correctly identify fraud, which, in turn, makes them even more prone to future attacks. As a result, lenders are not able to tolerate additional risk, and potentially credit-worthy applicants have a harder time accessing the financial services they need.
What is default risk?
Default risk concerns whether the borrower will be able to meet their loan obligations and pay the agreed-upon amount in the loan contract. Lenders factor in borrower-specific factors — like credit history, credit scores, financial stability, and debt-to-income ratios — in addition to the loan terms and economic conditions.
What is counterparty risk?
Counterparty risk, also known as institutional risk, is a type of default risk that concerns the future exchange of cash flows or other assets between different parties in the contract. If one of the parties cannot fulfill the contract, it could lead to financial disruption for the other party, and cause them to default on the loan.
Counterparty risk can apply to simple purchase agreements between two parties, or more complex transactions like financial derivatives. Lenders need to determine if all parties involved are likely to meet their contractual obligations, assess whether one party is more likely to default than the other, and decide if the bank or fintech is willing to extend a line of credit to these customers based on their level of risk tolerance.
How does systemic risk factor into default risk?
Systemic risk, also known as market risk, is associated with potential losses caused by changes in market conditions or other financial factors that affect the economy. Everything listed below is a type of systemic risk:
Even though systemic risk and default risk are distinct, they are interrelated. Systemic risk can impact default risk because changes to market conditions affect borrowers’ creditworthiness. For example, an economic downturn affects businesses’ profitability and potentially leads to layoffs. Rising interest rates can increase borrowing costs. Both scenarios make it more challenging for borrowers to meet debt obligations.
What is credit spread risk?
Credit spread risk is typically caused by changing interest and risk-free return rates. If the credit spread between riskier assets and risk-free assets — like government bonds, notes, and Treasury bills — widens, the borrower’s credit risk usually increases. This could cause an asset’s market value to decrease and result in losses for investors and lenders.
Credit spread risk is a significant concern for banks, and it affects how they make their lending and investment decisions. If the credit spread becomes too wide, banks often become much more cautious about extending credit, and fewer applicants are able to access funds.
How does credit spread risk affect fintechs?
Credit spread risk is not only a concern for traditional banks — it’s also a major concern for fintechs. Many are online lending platforms that engage in activities like peer-to-peer (P2P) lending, and changes in credit spreads affect their lending portfolios. Just like traditional banks, they face the possibility of borrowers defaulting on loans, and will be less willing to tolerate credit risk if credit spreads become too wide.
If credit spreads widen, it is also more difficult for fintechs to borrow or raise capital. Again, this impacts their ability to offer competitive interest rates to their borrowers and expand their product offerings.
What is concentration risk?
When a significant portion of a bank or fintech’s portfolio is concentrated on a single sector or asset class — for example, if they extend a significant portion of their loans to the real estate industry — a bank or fintech faces a higher risk of adverse events affecting that sector or asset. Without diversifying, concentration risk can lead to substantial losses. Banks and fintechs can combat this by leveraging technology that provides real-time data and analytics to help stress test, analyze scenarios, and conduct ongoing monitoring.
Does concentration risk contribute to systemic risk?
Concentration risk aggravates systemic risk when the failure of a major financial institution or industry causes a domino effect and increases threats to the economic system as a whole. For example, during the financial crisis in 2008, the concentration in mortgage-backed securities triggered a severe global economic downturn. More recently, the collapse of Silicon Valley Bank had a major impact on the technology sector — particularly the large concentration of fintech startups and venture capital funds that held their primary accounts with SVB. If the concentrated risk causes losses for one sector, it can lead to a chain reaction of loss for other industries, cause liquidity problems, and trigger further systemic instability.
How do different credit risk factors complicate the credit underwriting process?
Credit underwriters evaluate the creditworthiness of applicants to determine whether they can repay the loan and if they will repay the loan. They must assess and factor in different types of credit risk to determine those answers. When more types of credit risk need to be factored into credit risk models, the process becomes increasingly complicated.
This is why the workflows for commercial underwriting are more complex than consumer underwriting. It requires more strategic analysis — in addition to more time and resources — to assess the creditworthiness of a business as opposed to an individual consumer.
Lenders also structure loans differently depending on the outcomes of credit risk assessments so they can mitigate any potential losses. For example, lenders typically charge higher-risk businesses with a higher interest rate to compensate for the risk. Or, lenders might request personal guarantees from the ultimate beneficial owners (UBOs). High-risk borrowers could be required to provide collateral to secure a loan and reduce risk exposure, then be subject to more frequent ongoing monitoring.
Different forms of credit risk complicate the credit underwriting process by continuously introducing new variables and uncertainties that require different forms of risk mitigation and assessment. Effective credit underwriting workflows need to be agile and flexible enough to address and monitor these risks, protect the lender from default and financial losses, and balance the needs of deserving borrowers.
How do credit risk assessments potentially impact customer experience?
Customers with limited credit history — like younger consumers, immigrants who are new to the country, or those who struggled with past financial difficulties — often face challenges when it comes to accessing credit products and services. Lenders may impose stricter terms and conditions on high-risk customers — including shorter loan terms, lower credit limits, and higher collateral requirements — that limit their financial flexibility. Or, they could be denied altogether.
Keep in mind, the underbanked population is still a largely untapped market. Banks and fintechs that rely solely on traditional credit policies and data sources risk losing out on these new customer segments.
Also, if onboarding and credit underwriting processes are too lengthy, result in multiple credit inquiries that adversely impact credit scores, or require multiple rounds of document verification — that is not a good customer experience. In an increasingly digital banking environment, customers have many more options to pursue if they find processes too difficult. Banks and fintechs shouldn’t neglect to factor in the risk of poor customer experience, negative reviews, or reputational damage.
By regularly assessing the creditworthiness and financial behavior of existing customers, banks and fintechs can identify customers who qualify for additional credit offers or new product opportunities. Then, they can proactively offer credit opportunities without requiring a formal application process. These pre-qualified offers can be personalized and tailored to the customer's specific needs, for a seamless and convenient experience.
How does data accuracy impact credit risk assessments?
When it comes to credit risk assessments, the accuracy of the data is crucial. Inaccurate or fragmented information only further complicates credit underwriting decisions, potentially leading to:
When banks and fintechs implement data from multiple sources into their credit underwriting processes, they are able to improve data accuracy and enhance their risk assessment capabilities. Data from multiple sources can be cross-referenced and validated for accuracy. Lenders can compare the information from different sources to flag discrepancies, inconsistencies, potential errors, and even fraud.
Some data sources provide real-time information that helps lenders make more timely credit decisions and respond to changes in an applicant's financial situation quickly — not only during onboarding, but throughout the customer life cycle. Also, connections to alternative data sources help present a broader picture of creditworthiness. When lenders have readily available access to transactional bank data, business performance metrics, online employment verification services, and utility or vendor payment history, amongst others, they can rely less on the applicant and help ease customer friction.
How Alloy can help
Alloy is an Identity Risk Solution that provides both Onboarding and Credit Underwriting functionalities in a single, unified back-end platform that helps banks and fintechs better determine credit risks, make faster credit decisions, increase auto-approvals, and test new policies before they go live. With an extensive ecosystem of over 190 data partners, Alloy clients use detailed identity data gathered during onboarding in combination with credit bureau data and alternative underwriting data to offer more credit products, to more people, with less risk.
