How to address the challenges of consumer and commercial credit underwriting

As banks and fintechs strive to meet the credit needs of a wider range of consumer and business customer segments, they need innovative technology solutions to help them keep pace with market changes and address the complicated challenges of credit underwriting. Since both consumer and commercial lending require their own strategic approaches, banks and fintechs often become locked in by increasingly complex workflows that require manual reviews.

Below, we unpack consumer and commercial underwriting challenges and explore solutions — like automation and the connection to multiple, alternative data sources — that can help lenders save both time and resources. Additionally, we discuss the need for banks and fintechs to engage in more progressive onboarding practices that standardize decision-making processes and initiate faster credit assessments to boost growth.

Challenge #1: Customer expectations are higher than ever.

Banks and fintechs are facing higher customer expectations due to increased digitization and a much wider availability of personalized services. These factors have elevated standards for convenience, transparency, and tailored experiences across all industries, including financial services.

Consumer lending

From a consumer lending standpoint, customers want the credit application process to be quick and easy. They often expect:

• A digital-first experience that allows them to apply, submit documents, and track the progress of their loan applications in one place

• Quick, convenient pre-approvals

• Flexibility with repayment options, including the option to make extra payments or adjust payment schedules on an as-needed basis

• Personalized credit offerings tailored to their specific financial situation and needs

Commercial lending

From a commercial lending standpoint, in addition to seeking competitive interest rates and terms, businesses expect a streamlined lending process with:

• User-friendly document submission

• Tailored, customized financing solutions

• Timely access to funds

What is the main difference between consumer and commercial underwriting workflows, and how does this affect the ability of financial institutions to meet customer expectations?

The main difference between consumer and commercial underwriting is the depth of data available. Consumer underwriting tends to rely heavily on credit scoring, while commercial underwriting requires a deeper analysis of many factors, including:

• Financial statements — like balance sheets, income statements, and cash flow statements — to understand the business’s financial position

• Cash flow analysis to gain a better grasp of whether the company has sufficient funds to cover the loan payments

• Business strategy, growth prospects, and market position to access long-term viability

• Industry analysis to gauge the competitive landscape

• KYC and fraud checks on the universal beneficial owners (UBOs) of the business

• Credit scoring the business based on the business owners’ personal credit score, type of business, age of the company, etc.

In short, commercial underwriting requires a more complex workflow than consumer, and commercial applications require more time for lenders to process.

Challenge #2: Underwriting processes rely too heavily on manual reviews.

Credit underwriting models tend to require a significant amount of manual reviews for two key reasons:

• Credit assessments require a nuanced understanding of applicants’ financial statements, credit history, and industry-specific considerations.

• The variability of applicants and the number of diverse credit scenarios make it challenging to create streamlined workflows that address all these different circumstances.

However, manual reviews are costly and decrease overall operational efficiency. When a bank or fintech relies too heavily on manual reviews, their volume of lending transactions is constrained, and their decision-making is slower. As processing times increase, their ability to capture more of the market share decreases, and they potentially lose out on borrowers. Also, the risks of human error and compliance missteps potentially increase.

Challenge #3: Credit underwriting workflows are difficult to scale.

It is not easy to balance efficiency with customized underwriting solutions. Legacy and in-house systems often lack the flexibility to add new data sources easily and quickly. These systems are typically built with rigid structures that are difficult to adapt to evolving economic shifts, market changes, and industry trends, in addition to changing laws and regulations — often resulting in data silos and can open the bank/fintech up to potential losses if they cannot swiftly respond to threats and opportunities.

They also lack the scalability to:

• Handle larger volumes of data

• Aggregating data from multiple sources

• Integrate with external data sources

• Implement advanced analytics and machine learning algorithms

Additionally, bringing in all the necessary data to create full applicant profiles is both expensive and time- and resource-intensive.

Challenge #4: The lack of scalability in credit underwriting workflows prevents cross-functional collaboration.

Data silos contribute to the lack of scalability by impeding the efficient integration, sharing, and analysis of data across the underwriting process. This also means that credit underwriting, compliance, and fraud prevention teams operate within these data silos, and might not be able to properly communicate with one another.

The problem is rooted in the fact that, despite all teams being responsible for minimizing the bank or fintech’s risk and preventing direct financial losses, they view their objectives through very different lenses and maintain their own data sets. They probably even have different risk profiles for the same applicant.

However, the best time to identify and stop fraud is during onboarding and loan origination, before any bad actors have been approved. However, credit underwriters are more concerned with assessing an applicant’s creditworthiness than determining if they will commit fraud or if their identity is real.

As they evaluate and factor different types of credit risk into predictive models, the workflows become less efficient, and the process becomes more complicated for applicants. Likewise, fraud prevention teams might not fully grasp their efforts’ impact on the user experience and how it could lead to even more customer friction. (Keep in mind, bad actors are the ones who wait out bad experiences. Good customers will usually leave and find an alternative.)

Instead of collaborating with one another to optimize onboarding and credit underwriting processes and prevent fraud, these teams keep operating within their own silo. As a result, customer experience is decentralized and no one wins — except for the fraudsters.

How you can stop fraud at the flip of a switch

Challenge #5: Most underwriting processes focus on origination instead of automating regular, ongoing credit checks.

Credit risk management is much more difficult in today’s rapidly changing economic environment. Many credit underwriting processes put too much emphasis on origination. Lenders use point-in-time data to approve an applicant and offer them a certain amount of credit, then neglect to check back in on how that borrower’s financial circumstances might have changed.

While the best credit decisions start at onboarding, they do not end there. Without conducting ongoing credit monitoring, banks and fintechs potentially:

• Increase their risk exposure

• Negatively impact their profitability

• Limit their growth

While unaddressed risk exposure could lead to an accumulation of high-risk loans, lenders are also missing out on opportunities to expand their services and tailor additional credit offerings to more of their existing customers. Ongoing credit monitoring allow them to closely monitor current customers’ creditworthiness, identify positive changes — such as improved credit scores, income stability or growth, and on-time payments — and then confidently extend personalized credit offerings with less risk.

How can banks and fintechs overcome these credit underwriting challenges?

There are several ways to account for these challenges. With the right platform in place — like an Identity Risk Solution — banks and fintechs can use automation and alternative data sources to streamline complex credit underwriting workflows. Then, they can take advantage of more growth opportunities without incurring additional risk.

Solution #1: Utilize automated credit decisioning processes to optimize loan origination and reduce manual reviews.

When banks and fintechs leverage automated workflows in their credit underwriting processes, they create more operational efficiency, better consistency, and a sharper competitive edge:

• Automated workflows streamline the credit underwriting process to reduce manual tasks and accelerate application processing times, resulting in faster loan approvals and less customer friction.

• If loan applications increase, automated workflows can help banks and fintechs handle the higher volume and meet growing demands.

• When automated workflows connect to multiple data sources, decisions become more data-driven, reducing the potential for biases based on race, gender, or age, and leading to improved, inclusive lending outcomes.

• Automating the credit underwriting process allows banks and fintechs to match the appropriate credit products with the right borrower and introduce new credit products to meet their needs in a more informed manner.

Remember, the point of automated workflows is not to eliminate manual reviews, but to reduce them. The need for manual reviews will never entirely disappear. However, automating key parts of the process can reduce the significant amount of time lenders spend on administrative tasks and allow them to focus on high-value risk and financial analysis, policy refinement, and complex valuations.

Banks and fintechs can also use Identity Risk Solutions to design automated workflows that ensure that credit underwriting processes adhere to relevant regulations and compliance requirements, while maintaining the flexibility to adjust to any new or developing legislation. (In fact, in Alloy’s recent State of Compliance Benchmark Report, 55% of the surveyed fintechs said lack of automation was the leading barrier to meeting Bank Secrecy Act requirements.) This can help reduce the significant regulatory burden placed on lenders, who are often responsible for upholding compliance programs.

Solution #2: Rethink conventional credit decisioning with alternative data sources.

When banks and fintechs can connect to multiple, alternative data sources, it provides additional opportunities to expand their funnel and convert more customers.

While credit scores are traditionally used for consumer lending, they can be outdated and lack a full picture of the applicant’s financial health. For example, if a potential borrower defaulted on a loan several years ago, it could still lead to an application rejection — even if their financial health has significantly improved. A connection to alternative data sources — such as transactional bank data, online employment verification services, and utility payment history, amongst others — presents a more complete picture of a borrower’s creditworthiness. It opens up the possibility of new customer segments, like traditionally underbanked populations and other thin-file applicants.

How a reliance on credit scores limits your credit decisions

From a commercial lending standpoint, data points from alternative data sources — such as cash flow data, business performance metrics, online presence, social media activity, and vendor payment histories — offer more diverse and up-to-date information on a wider spectrum of businesses. For example, a traditional credit reporting agency might have very limited information on a new startup that doesn’t have an extensive credit history. Alternative data sources can supplement traditional credit data and create a better picture of the startup’s true finances for lenders to evaluate.

By leveraging alternative data sources for both consumer and commercial lending, banks and fintechs won’t just approve applications faster. They can also make more informed lending decisions and engage in more progressive onboarding practices that are better aligned with their growth goals and risk tolerance.

How data orchestration helps you take a non-linear approach to applicant evaluations

Solution #3: Streamline complex workflows into a single view.

When it comes to credit underwriting, lenders often deal with multi-workflow use cases, for example:

• Different products have different underwriting requirements, so lenders might use a different workflow for each product.

• Different businesses, like startups or companies with seasonal revenue patterns, might get flagged for a more thorough assessment due to greater cash flow fluctuations and be funneled into a different workflow.

But how does that affect the customer experience if an existing customer wants to sign up for an additional product? Does it mean that they start the application process from scratch even though they have an established relationship with the bank or fintech? Should a creditworthy startup have to jump through multiple hoops to prove they deserve a loan? Or will they take their business elsewhere?

When technology solutions can consolidate multi-workflow use cases into a single view, all evaluations and decisions that have occurred for an applicant can be viewed in the same dashboard, and all the relevant information is contextualized for the review in a single source of truth. This is especially helpful if lenders need to review multiple applications for the same individual or entity. (It might even increase their cross-functional collaboration with compliance and fraud teams when everyone can access and analyze shared data insights in real-time from the same place — even if their organizational structure made these efforts difficult in the past.)

Consumer expectations for credit products are high, and building a best-in-class credit product is far from simple. But, using consolidated, automated workflows that connect to multiple data sources helps banks and fintechs optimize and personalize the application process for each potential borrower.

Solution #4: Take an ongoing approach to credit management with automated credit checks.

Credit underwriting should not be a one-time decision. Ideally, it happens on an ongoing basis. When lenders continuously assess a borrower’s financial health, they are more likely to strike the right balance between providing better access to credit and managing the associated risks.

A regular cadence of ongoing credit checks provides a more complete picture of a borrower's creditworthiness, which allows lenders to:

• Maintain appropriate credit limits and terms

• Decide if customers qualify for a credit line change based on behavior

• Determine if customers qualify for additional products based on behavior or milestones

• Retain good customers by rewarding them with more credit

• Extend more personalized product offers with less risk

In short, including ongoing, automated checks in credit underwriting workflows helps lenders manage the full credit lifecycle instead of placing the sole focus on origination. This creates more flexibility to offer additional credit products and more opportunities to deepen customer relationships.

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 make faster credit decisions, increase auto-approvals, test new policies before they go live, and underwrite customers on an ongoing basis. With an extensive ecosystem of over 190 data sources, 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.