About 45 million American adults are considered “credit invisible” and do not have a credit score, meaning their lack of credit history likely keeps them from securing a credit card, taking out loans, or even being approved for an apartment. One side effect of this is that banks haven’t been as inclusive with their lending practices and have been unable to do business with a considerable portion of the population.
While the traditional method for determining customers’ creditworthiness has been through their credit score, technological advancements like artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) are helping lenders utilize alternative, textual data and provide services to a broader swath of the population with accuracy and efficiency.
Even though balancing risk is still an essential aspect of lending, making NLP-powered credit lending decisions, banks can be more inclusive while maintaining a favorable risk profile.
How NLP Is Enabling Better Credit Lending Decisions
While traditional credit scores are based on factors like length of credit history, outstanding debt levels, and credit mix, NLP helps banks consider alternative data for more accurate credit reporting through speech recognition, machine translation, sentiment analysis, and more. Banks can process much more information and analyze unstructured data to make inclusive lending practices, become more efficient, and offer services to a larger country segment.
1. Underwriting automation
The underwriting process for banks is a time-consuming and tedious task that requires staff to verify specific data from customers that ensures that their income levels and credit history are sufficient to qualify for a loan. With NLP, banks can enhance their underwriting workflows and process loan applications much quicker and more accurately than they could do manually. NLP models can analyze and filter mountains of unstructured and textual data in a way that would be impossible for humans to replicate, helping banks make better credit lending decisions without sacrificing time.
2. Optimizing credit scoring
Traditional credit scoring doesn’t consider alternative or untraditional data like employment history or utility payments. Plus, traditional credit scores aren’t always up-to-date with the latest information, so credit scoring automation that considers a broader range of factors can help banks have a more accurate look at potential customers and whether they are qualified or not.
Banks don’t have the workforce to manually process all this data to take advantage of the plethora of data at their fingertips that could help them make better credit decisions. With NLP-informed credit histories however, banks can be more inclusive with their lending practices and make better lending decisions overall by having a more comprehensive picture of potential clients and their financial data.
3. Streamlining the application process
Lenders can be more efficient with their loan application process and be more profitable per loan by integrating AI/ML/NLP into their workflows. From start to finish, AI can help banks automate the application process and decrease the amount of hands-on time required by staff.
Overall, this technology helps to reduce potential bias and make the entire process more efficient.
AI models can filter through loan applications and determine which customers meet the requirements to qualify for a loan much quicker than when done manually, and NLP can help banks consider alternative data sets.
4. Allowing access to a broader range of financial services/products
A large benefit of using NLP-informed credit lending scores is how it will allow a greater portion of the population to qualify for financial services and products, including credit line extensions. While they may have been disqualified in the past due to their lack of credit history or poor credit score, NLP-backed credit lending decisions can help prove their creditworthiness in new ways that weren’t possible before.
AI/ML/NLP lending analyzes credit histories to ensure consumers have a track record of successful payments by examining data from employment history, utility payments, levels of education, and other alternative datasets. This goes beyond what evaluating the traditional credit score may imply, showing lenders that consumers can repay loans or pay off credit cards.
Populations that have been shown bias in the past, like people of color or those with a lower income, may have faced challenges accessing financial services. However, AI and NLP are making it possible for banks to be more inclusive with their practices.
5. Automating manual tasks
One of the greatest benefits of implementing AI/ML/NLP into the lending process is that these models and algorithms can help banks become more efficient and accurate with their credit decisions. While workflows like loan underwriting and application processing are highly time-intensive and tedious when done manually by staff, making AI-powered credit lending decisions can free up employees’ time for more value-add activities.
Even though AI and NLP algorithms are highly valuable tools for financial services firms, there are still many client-facing duties that humans will perform in the years to come. Therefore, using AI to automate manual and repetitive tasks allows employees to have more time for processes requiring critical thinking and enables banks to become more client-focused.
Using No-Code NLP for Credit Lending Decisions
As the financial services industry continues to be digitized and transformed by emerging technologies, banks can utilize NLP to make better credit decisions. Boosting both efficiency and broadening access to financial services, credit scores that incorporate alternative data will revolutionize how banks qualify customers for banking services, making their services more inclusive and effective.
Luckily, banks today don’t need to learn how to code or hire data specialists to benefit from NLP’s powerful capabilities. No-code NLP solutions, like the one offered by Accern, are making it accessible for financial services firms at all levels to access NLP through its ready-made use cases that are industry-specific and tailored to specific applications.