CREDIT RISK MITIGATION TOOLS: THE LATEST INNOVATIONS FOR LENDERS

All lenders seek to reduce credit risk within their portfolios. While some still use manual risk assessment tools, portfolio management spreadsheets, and even good old-fashioned intuition to guide decision-making, savvy lenders are looking to technology and automation as a solution. These days, consumer lenders are adopting cloud-based lending software to assess risk. Such cutting-edge credit risk mitigation tools have made reducing risk more reliable while increasing efficiency.

Unfortunately, higher credit risk leads to increased loan defaults and delinquencies, directly affecting a lender’s profitability. For instance, S&P Global predicts that loan default rates will rise to 5% in the United States and 3.75% in Europe by September 2024, above their long-term historical trends. The rating agency warns that defaults could reach 7% in the United States and 5.5% in Europe if its most pessimistic scenario plays out.

There is hope. With a wide range of risk assessment, management, and mitigation capabilities, lending software allows consumer lenders to quickly identify potential risks to make well-informed decisions. Whether looking for new lending software or simply wanting to manage risk more effectively, lenders should consider a few critical credit risk mitigation tools.

Top 3 Credit Risk Mitigation Tools for Lenders
1) Fraud analytics helps detect and mitigate fraudulent activity while assisting with predictions of future fraudulent behavior.
2) Automated verification services largely eliminate human error while streamlining the application process.
3) Machine learning algorithms that automate the application process provide greater accuracy and speed.

How Lenders Can Reduce Credit Risk

Several obstacles present themselves when looking at ways to reduce credit risk. First, the risk environment is constantly changing. Fraudsters seek to devise new schemes to avoid detection. Compliance regulations frequently change as well, so lenders must keep abreast of these new rules to manage the risk to their portfolios.

Lenders today offer a more varied range of loans, making risk management more complex. Having a diverse portfolio opens many new lending opportunities and increases the lender’s risk. To deal with these more significant risks, lenders require a system that allows them to identify individual risk factors for each type of loan and applicant while carefully managing this risk throughout the loan’s lifecycle. 

In particular, the three credit risk mitigation tools identified in the table above can help lenders reduce risk more effectively. Let’s examine how these tools allow lenders to reduce credit risk.

Credit Risk Mitigation Tools for Lenders

1) Innovations in Fraud Analytics

There are numerous forms that fraud takes in the loan application process, including: 

• Income: Applicants sometimes create fake pay stubs that appear very realistic. This allows them to be approved for loans for which they’re not qualified based on their actual income. 
• Employment: Employment history can also be altered, with phone numbers tied to fictitious employers or references. Sometimes, fraudsters even use phony email addresses and dummy websites. 
• Collateral inflation: Another method of fraud involves inflating the value of a vehicle or other collateral used to achieve more favorable loan terms. While shrewd lenders usually spot this, such situations sometimes get overlooked when manual processes are used.
• Identity: Identity theft is another big problem for the consumer lending industry. While an applicant might look legitimate based on the data entered and documents provided, this type of fraud can be challenging to catch. This is mainly because scammers increasingly employ technology to counter lenders’ techniques for spotting this.

With this vast potential for fraud these days, often supported by their technology, lenders must utilize the most current approaches to stay ahead of the fraudsters. To reduce credit risk, cloud-based lending software should integrate easily with multiple third-party providers whose operations are supported by fraud analytics. With this capability built into their systems, lenders can more easily identify such scams during the application process.  

Rather than relying on loan officers to take an educated guess about whether an application is genuine, fraud analytics providers collect and mine data to identify hidden patterns that indicate possible fraud. For example, innovative fraud detection software helps identify patterns in fake phone numbers, automatically flagging these in future applications. This allows a lenders’ personnel to identify fraudsters, even if they’ve never seen a specific scam before. By keeping a detailed record of all known factors related to these attempts at fraud, loan officers can more easily identify suspicious information or activity within an application.

2) Cutting-Edge Verification Services Help Lenders Reduce Risk

Fraud analytics and verification services go hand in hand to reduce credit risk. The best lending solutions will have the following: 

• Synthetic identity detection capabilities that compare personal information provided on the application to reliable credit databases.
• Cloud-based resources that allow lenders to store and access vast amounts of information to make better lending decisions.  
• Income verification from secure and confidential sources.
• Alternative credit data and trended credit data filters that give lenders a clearer picture as to how risky an application actually is, going beyond traditional credit scores, income reports, or employment history to increase the difficulty of faking this data.
• The ability to verify and refute employment history. 
• Accurate collateral valuations that are history-adjusted, with vehicle valuations also integrated with Black Book and Kelley Blue Book.

To reduce credit risk, lenders can’t simply take applicants at their word. Advanced verification services like these help lenders separate honest borrowers from potential fraudsters.

3) Using Machine Learning to Automate and Manage Portfolios

Reducing credit risk involves more than just detecting fraud and verifying information. It also requires that lenders manage their applications and portfolios effectively. For example, if an account is delinquent, lenders must take immediate action to prevent the loan from defaulting. Automation and machine learning enable lenders to take these necessary steps more quickly and efficiently. 

Risk management automation works by: 

• Assessing how risky it would be to lend to a specific applicant, which then allows lenders to eliminate the introduction of high-risk borrowers into their loan portfolios.
• Structuring loans based on past and projected risk management data.  
• Prioritizing applications with lower risk and high return potential.
• Automatically approving very low-risk applicants. 
• Sending mid-range-scoring applicants to loan officers for manual assessment when approval or denial isn’t straightforward. 
• Automating the delinquency recovery and collections process to reduce financial risk. 
• Using machine learning algorithms to identify patterns and create new decision rules based on these trends.

Not only does this improve the efficiency of lenders’ loan approval, origination, and management processes, but it also gives loan officers more time to focus on tasks that require a personal touch. 

Is Your Risk Management System as Effective as It Could Be?

One of the most important ways for consumer lenders to reduce credit risk is to leverage the wealth of data sources available to them. This is one place where cloud computing offers a real advantage, as it allows lenders to safely store and retrieve vast amounts of data efficiently. However, collecting, managing, and analyzing this data is a time-consuming process, which is why lenders now need cloud-based software that automates risk management to stay competitive. 

To reduce risk throughout a loan’s lifecycle, the best loan origination systems and loan servicing software use automated decision-making and verification services along with machine learning algorithms and fraud analytics. Having all these capabilities in one platform that’s based in the cloud helps eliminate information silos. Using this interconnected data, consumer lenders can better assess risk and take the most critical steps to protect their businesses and borrowers from preventable threats and vulnerabilities.