Complete Guide to Risk Management for Credit Startups

In this Golden Age of Fintechs, there’s a lot founders need to know about building a credit startup. While there are many areas founders need to pay close attention to, risk management demands special attention. Ensuring that you’re lending to qualified and dependable borrowers is a cornerstone – if not the cornerstone – of a credit company. Failure to sift through the masses of applicants in a careful and diligent way will result in a high default rate and the shuttering of your budding business. 

And while risk management is a wide, deep, and ever-evolving topic, there are four main areas founder should concern themselves with, especially early on:

1. Evaluating borrower creditworthiness
2. Mitigating fraud risk
3. Monitoring portfolio performance
4. Establishing operational controls

In this post, we’ll take a closer look at each of these key risk management topics and how founders can best tackle them. Read on for a crash course in risk management!

Evaluate Borrower Creditworthiness

The first step in risk management evaluating your borrowers’ creditworthiness. Honestly, this is probably the most crucial aspect of risk management because if you fail to lended to creditworthy borrowers, you will never get to a point where you have to worry about the rest of this list. 

Generally speaking, to assess a borrower’s creditworthiness, you will need to collect and analyze their financial and personal information, such as credit scores, income, employment history, and debt-to-income ratio. This is where your credit scoring model does its job. You can do this with underwriting software or with a custom scoring model. 

Whichever way you do it, there are a few common key steps: 

1. Gather Information
2. Analyze the Data
3. Use Predictive Analytics
4. Consider Alternative Data Sources
5. Make a Decision

Read on to learn more about each of these steps. 

Gather Information

You should, first, collect relevant information about the borrower, including their income, employment status, and credit history. This information can be obtained through various sources, such as credit reports, bank statements, and employment verification. The best way to collect this information is through the loan application form on your website. 

Analyze the Data

Once you’ve collected the borrower’s information, you need to analyze it to determine their creditworthiness. This may involve looking at factors such as their credit score, debt-to-income ratio, and payment history. As mentioned above, this can be done with underwriting software or an in-house scoring model. 

Use Predictive Analytics

To enhance the accuracy of the credit evaluation process, you can use predictive analytics to analyze data patterns and predict the borrower’s likelihood of defaulting on a loan. Machine learning algorithms can identify key indicators of credit risk, such as missed payments or high debt levels and help weed out unqualified borrowers.

Consider Alternative Data Sources

In addition to traditional credit data, the startup can also consider alternative data sources to evaluate a borrower’s creditworthiness. This is what we did at Stilt. We had identified an underserved and low-risk market in green card and visa holders in the U.S. But because immigrants do not have U.S. credit history, we had to use alternative data such as current employment, education, employment potential, and other non-traditional criteria. 

Make a Decision

Based on the analysis of the borrower’s information, you can make a decision about whether to approve or deny the loan application. You may also decide to offer the borrower a loan with specific terms and conditions, such as a higher interest rate or shorter repayment period, if they don’t quite meet the creditworthiness criteria. 

Evaluating Borrower Creditworthiness Example

Now let’s put it all together. Lenderly, a new credit startup, wants to evaluate the creditworthiness of a hypothetical customer, Mr. Borrower. And to do this, the startup decides to use a combination of traditional credit data and alternative data sources.

The traditional credit data includes Mr. Borrower’s credit score, credit history, and payment behavior on loans and credit cards. The startup also decides to analyze alternative data sources, such as Mr. Borrower’s social media activity, to gain additional insights into their credit risk.

To analyze the data, Lenderly employs machine learning algorithms that are trained on historical data to identify the key factors that are predictive of credit risk. For example, the algorithms may identify missed payments, high debt levels, or negative social media posts as potential indicators of credit risk.

Based on the analysis of Mr. Borrower’s information, Lenderly can make an informed decision about whether to approve or deny the loan application. If Lenderly approves the loan, they may offer Mr. Borrower slightly less attractive terms and conditions tailored to the credit risk profile assessed above. 

Mitigate Fraud Risk

Another significant risk for any credit startup is fraud and it is essential to implement fraud prevention measures to minimize the risk of fraudulent loan applications. 

Before we talk about how to manage fraud risk, let’s look at an example of how weak fraud policies can impact credit startups. During the COVID-19 pandemic, the U.S. government issued the Payroll Protection Program (PPP) to help distribute loans to help keep business afloat. Many smaller fintechs acted as mediaries in the loan approval and distribution process, but many had lax or non-existent fraud-detection, which resulted in as much as $64 billion dollars being defrauded from the program. Congressional investigations led to a number of lawsuits being filed against the fintechs involved and, famously, credit startup Kabbage eventually filed for bankruptcy.  

As with evaluating creditworthiness, mitigating the risk of fraud is a topic that could fill a series of blog posts, but there are some core tenants founders should focus on, in particular: 

• Implementing identity verification. New credit startups must Implement identity verification processes, such as document verification or biometric authentication, to ensure that the borrower is who they claim to be.
• Investing in fraud detection software. New startups must also utilize fraud detection software. This software uses machine learning algorithms to detect patterns of fraud and identify high-risk loan applications.
• Adhering to Anti-money laundering (AML) measures. AML measures are a set of policies, procedures, and regulations that financial institutions and other regulated entities must follow to prevent and detect money laundering and other forms of financial crime. Implementing AML measures to prevent money laundering and terrorist financing, such as customer due diligence and transaction monitoring.

Putting into effect just these three fraud prevention measures will immediately position your company to better protect itself from financial losses and reputation damage. 

Monitor Portfolio Performance

Monitoring portfolio performance is a critical aspect of risk management. Founders will regularly want to analyze the company’s loan portfolio to identify potential defaults and take action to mitigate losses. Here are a few steps you can take to monitor loan performance.

• Analyze loan performance. Analyze loan performance metrics, such as default rates, delinquency rates, and charge-off rates. This will help to identify trends and patterns and clue you into when the portfolio is trending in the wrong direction.
• Identify the high-risk loans. Identify high-risk loans, such as loans with high debt-to-income ratios or borrowers with poor credit history, and take action to mitigate the risk of default. In the case of high-risk loans, the best move is to deny the application, though sometimes making an offer with less-than-attractive terms and conditions is warranted. This is, ultimately, up to the lender.
• Stay on top of collections. Manage collections of delinquent loans by implementing effective collections processes and engaging with borrowers to resolve outstanding debts. It’s not the most fun part of the business, but it’s a crucial part of keeping portfolio performance optimal.  

While these are just a few of the things credit startups can do to ensure optimal portfolio performance, they are some of the most crucial. By monitoring portfolio performance, you can identify potential defaults early and take action to mitigate losses, ultimately maximizing profits.

Establish Operational Controls

The final key area of risk management founders should tackle is establishing operational controls. This is an essential aspect of risk management and it involves implementing processes and procedures to prevent errors and fraud within your credit startup’s operations. In a future blog, we’ll take a deep look at all the ways startups can implement operations procedures to combat fraud, but here are three key tactics early startups should leverage: 

• Segregate duties with multiple employees. Implement segregation of duties to ensure that no single employee has control over all aspects of a transaction.
• Set up access controls for sensitive data and systems. Implement access controls to limit access to sensitive data or systems to authorized personnel only.
• Perform regular operations audits. Conduct regular audits of your credit startup’s operations to identify potential weaknesses and improve processes and procedures.

By establishing operational controls, you can minimize the risk of errors and fraud within your operations, ultimately minimizing the risk of financial losses and reputational damage.

Final Thoughts

Risk management is a critical aspect of any credit startup’s success. Founders need to focus on four main areas to ensure optimal risk management: evaluating borrower creditworthiness, mitigating fraud risk, monitoring portfolio performance, and establishing operational controls. Implementing these measures will help credit startups minimize the risk of financial losses, reputational damage, and business closure. By evaluating creditworthiness using predictive analytics and alternative data sources, implementing fraud prevention measures, monitoring portfolio performance, and establishing operational controls, credit startups can position themselves for success in the competitive lending industry.

Credit Startup Risk Management FAQ

Below, you’ll find a few frequently asked questions that credit startup founders may have about risk management, along with their answers.

How can I ensure that I’m lending to creditworthy borrowers?

To ensure that you’re lending to creditworthy borrowers, you should collect and analyze their financial and personal information, such as credit scores, income, employment history, and debt-to-income ratio. You can use underwriting software or a custom scoring model to evaluate this information and make a decision about whether to approve or deny the loan application. Using predictive analytics and considering alternative data sources can also enhance the accuracy of the credit evaluation process.

How can I mitigate the risk of fraud?

To mitigate the risk of fraud, you can implement identity verification processes, such as document verification or biometric authentication, to ensure that the borrower is who they claim to be. You can also utilize fraud detection software that uses machine learning algorithms to detect patterns of fraud and identify high-risk loan applications. Additionally, adhering to anti-money laundering (AML) measures, such as customer due diligence and transaction monitoring, can prevent and detect money laundering and other forms of financial crime.

How can I monitor portfolio performance?

To monitor portfolio performance, you should analyze loan performance metrics, such as default rates, delinquency rates, and charge-off rates. You should also identify high-risk loans, such as loans with high debt-to-income ratios or borrowers with poor credit history, and take action to mitigate the risk of default. Finally, managing collections of delinquent loans by implementing effective collections processes and engaging with borrowers to resolve outstanding debts is crucial to maintaining optimal portfolio performance.

How can I establish operational controls?

To establish operational controls, you can implement processes and procedures to prevent errors and fraud within your credit startup’s operations. For example, you can implement segregation of duties to ensure that no single employee has control over all aspects of a transaction. You can also implement access controls to limit access to sensitive data or systems to authorized personnel only. Regular audits of your credit startup’s operations can also help identify potential weaknesses and improve processes and procedures.

What are some common pitfalls to avoid in risk management?

Common pitfalls to avoid in risk management include relying too heavily on credit scores or underwriting software, failing to monitor portfolio performance, and not implementing adequate fraud prevention measures. It’s also important to stay up-to-date with changes in regulations and best practices in risk management to ensure that your credit startup is operating legally and ethically.

Frank Gogol

A seasoned SEO expert, Frank has a long history of working with and for startups. Starting in mid-2018, Frank served as the SEO Strategist for Stilt, a fintech startup that provided fair loans for immigrants in the US and other underserved markets. While with the company, he scaled site traffic from zero to more than 1.5 million unique visits per month, driving the bulk of the company’s lead generation until it was acquired by J.G. Wentworth in December 2022. As employee #5 at Stilt, Frank was witness to, and part of, the successful building and sale of a fintech company, uniquely positioning him to create content for founders about all things startups.

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