Credit unions are not-for-profit organizations that strive to offer personalized financial solutions to their members in an equitable and inclusive manner. However, fair lending decisions become complicated when relying solely on credit scores. A 2022 analysis by Oliver Wyman, conducted on behalf of Experian, revealed that roughly 28 million Americans are credit invisible, and another 21 million are ‘unscoreable’ due to insufficient credit history. Additionally, 57 million have subprime or lower credit scores. These individuals, who represent nearly 30% of the US population, when judged solely by their credit scores, may struggle to access crucial loans needed to buy a car or a home—opportunities that credit unions aim to provide.
Credit scores overlook crucial factors like income stability, employment history, and savings habits. Additionally, they fail to account for regional differences in spending patterns or cost of living, leading to assessments that often miss the full financial picture. This issue is especially problematic for borrowers with mid-tier scores (between 580 and 700), a group that includes 33% of the U.S. population and many Gen Z individuals. Consider two borrowers, both with mid-tier credit scores around 700: Emily, a nurse with stable income and manageable debt, and James, a freelance artist with variable earnings and higher expenses. Despite sharing the same score, their financial realities are vastly different—yet traditional scoring models would assess them the same. This one-size-fits-all approach can result in risky loans being approved while low-risk borrowers, like Emily, are overlooked.
Making risk assessments solely based on these scores is not only limiting but can be debilitating, excluding individuals who may actually be low-risk but fall outside traditional credit metrics. As a result, many of these mid-tier applicants either face rejection or require manual underwriting, which slows down the process and introduces inherent biases.
The figure above outlines the major shortcomings of traditional underwriting methods. These processes are manual and slow, often causing delays in decision-making. Additionally, they rely heavily on credit scores, which makes it difficult to assess borrowers with thin credit files or those who are credit invisible. These methods tend to provide short-sighted, inconsistent, and biased analyses, with limited capacity to predict future risks or accommodate unique borrower profiles, resulting in missed opportunities for more inclusive and accurate lending decisions.
As lending decisions become more complex, to truly support their members, credit unions need a smarter, faster approach to credit risk assessment—one that goes beyond simplistic scoring and considers the full picture of a borrower’s financial health. This is where AI-driven underwriting comes in, offering a transformative solution that combines data depth and automation to deliver smarter, inclusive and more consistent lending outcomes.
Bridging the Gap with AI: A Smarter Approach to Underwriting
AI-driven underwriting offers the precision and flexibility needed to address the challenges of traditional lending methods, by leveraging a broader, more nuanced set of data points. Unlike credit scores, AI models assess borrowers holistically, considering factors like income stability, employment history, spending habits, and even alternative data such as utility payments or rental history. This provides a more comprehensive view of a borrower’s financial health.
AI also eliminates the biases often introduced in manual underwriting. It processes applications faster, standardizing risk assessments based on data rather than human judgment, ensuring more consistent and equitable lending decisions. For example, mid-tier applicants, like freelancers or gig workers, who may have been penalized by traditional scoring systems, are better understood through AI’s ability to assess non-traditional income patterns and long-term financial behaviors.
Thus, by capturing a more complete picture of risk, AI empowers credit unions to make smarter, faster lending decisions—opening doors for borrowers who may have been overlooked in the past while ensuring that high-risk loans are carefully managed. This shift not only improves accuracy but also strengthens credit unions' ability to support their members more fairly and inclusively. However, successful implementation of AI-driven lending requires careful planning and attention to several critical factors.
‘Must-Have’ Features of AI-Powered Underwriting Solutions
An effective AI-powered underwriting solution should include the following must-have features to ensure accurate, fair, and efficient lending decisions:
Automation of Document Processing and Validation: AI should automatically extract and process data from documents such as income and investment statements, tax returns, credit reports, and existing loans. This capability reduces manual effort and speeds up decision-making. Furthermore, AI must validate the information and identify any suspicious or fraudulent activity, ensuring a secure and efficient underwriting experience.
Risk Prediction and Monitoring: Beyond assessing current risk, the solution should evaluate future risks based on comprehensive data analysis. This allows credit unions to anticipate potential financial challenges by assessing both the borrower’s future risk and the impact of previous lending decisions on the credit union’s financial health.
Flexibility and Customization: The system should be adaptable to the unique policies and needs of different credit unions, offering customizable risk models and scoring mechanisms that align with their specific objectives.
Bias Mitigation: AI systems must be designed to minimize or eliminate biases related to race, gender, or socioeconomic status, ensuring that decisions are equitable for all borrowers.
Explainability and Transparency: The AI system should provide clear explanations for its decisions, enabling underwriters to understand the rationale behind approvals or rejections. This fosters trust among both credit unions and borrowers.
Scalability: The platform should be capable of handling varying volumes of loan applications as the credit union grows, without compromising speed or accuracy.
These features ensure that AI-powered underwriting delivers more accurate, inclusive, and efficient results, while addressing critical operational needs.
Akaike’s Smart Lending Solution: Making the ‘Must-Have’ Features a Reality
Akaike’s Smart Lending Solution harnesses advanced AI technologies, combined with user-friendly interfaces, to empower credit unions at any level of technological maturity. For credit unions that manually process financial documents, Akaike provides a digital platform for seamless borrower profile management and document uploads, streamlining the underwriting process for improved efficiency and accuracy.
Smart Processing and Validation of Multi-Modal Financial Data
Documents used to assess credit risk can be both structured and unstructured, spanning multiple modalities such as text and images. For example, structured documents include credit reports, loan applications, and pay stubs, while unstructured documents might include scanned tax returns, bank statements, and collateral documentation with images like property appraisals or ID proofs.
Akaike's Smart Lending Solution efficiently handles both structured and unstructured data, whether it's textual information or image-based documents. By automatically extracting relevant features from these diverse sources, Akaike builds comprehensive metrics, validates the authenticity of the documents, and detects any suspicious or fraudulent activity—ensuring a robust and secure underwriting process.
You can also engage with an AI assistant in natural language to grasp the essence or delve into the details and analysis of any specific document uploaded.
Comprehensive Risk Assessment and Prediction
Akaike’s Smart Lending Solution goes beyond traditional risk evaluation by offering multi-faceted risk assessment and future risk prediction capabilities. It enables credit unions to evaluate risk at various levels—business risk, individual borrower risk, and credit union-wide risk—based on historical lending decisions, borrower profiles, business valuations, and institutional policies.
Business Risk Assessment:
Akaike evaluates businesses using a comprehensive approach that considers various factors, including location, years of operation, valuation, and type of business. The solution analyzes key financial documents such as balance sheets, income statements, and cash flow statements to automatically assess the average cash inflow and outflow balance. This automated analysis provides credit unions with valuable insights into a business's financial health, operational efficiency, and overall risk profile.
Akaike also tailors financial ratios and metrics to better reflect the unique circumstances of small businesses. For instance, traditional profitability metrics like Earnings Before Interest, Taxes, and Amortization (EBITA) might not be suitable for assessing SBA loans. Instead, other factors such as cash flow, revenue stability, and industry-specific benchmarks could provide a more accurate picture of a small business's financial health and capacity to repay loans.
Individual Borrower Risk Evaluation:
Akaike employs a multifaceted approach to individual borrower risk evaluation, integrating a diverse range of metrics to assess both financial discipline and overall creditworthiness. While traditional metrics such as credit scores, credit utilization, average income, and missed or delayed EMIs play a crucial role, Akaike goes further by calculating smart scores that offer a more comprehensive view of a borrower’s financial standing and borrowing intent. These smart scores are derived from a combination of standard and alternative data points, providing a deeper understanding of the borrower's profile. Smart scores include:
Trust Score: Derived from payment consistency, accuracy of submitted information, legal history, and the trustworthiness of secondary applicants, this score highlights the borrower's reliability.
Affluence Score: Evaluates property and asset ownership, overall household income, and lifestyle indicators to assess financial stability and capacity.
Investment Score: Considers current investments, diversification across assets, and readiness to invest further, reflecting the borrower’s financial acumen.
Social Influence: Analyzes the borrower’s social and professional network, indicating potential support systems.
Geo Score: Examines location stability, trustworthiness, and the median household income of the area to assess the borrower’s environmental factors and default probability.
Additionally, Akaike incorporates the profiles of secondary partners, such as guarantors, co-applicants, and referrers, to provide a more comprehensive evaluation of borrower risk.
Credit Union Risk Analysis:
Akaike also empowers credit unions to assess their overall risk exposure by analyzing past lending decisions, existing policies, liquidity levels, and external regulatory requirements. This helps them evaluate their financial stability and adaptability in changing market conditions, ensuring that they maintain a healthy balance between risk management and growth. Additionally, Akaike's system accounts for industry regulations, ensuring compliance while managing long-term risk.
Flexibility and Customization for Tailored Risk Evaluation
Akaike's Smart Lending Solution offers unparalleled flexibility, allowing credit unions to adapt the system to their unique needs. Whether adjusting business or individual borrower metrics, credit unions can easily add, remove, or modify risk evaluation criteria to align with their internal processes, institutional rules, and regulatory guidelines. The platform also enables seamless integration of custom metrics for a more precise and personalized underwriting approach.
Additionally, with the "Ask AI" feature, credit unions can interactively explore and analyze every data artefact, gaining deeper insights into specific documents or metrics. This powerful tool facilitates a dynamic and informed decision-making process, ensuring that credit unions maintain full control over their lending strategies.
Transparent and Explainable Smart Lending
Balancing explainability and transparency with ease of use is a critical aspect of Akaike's Smart Lending Solution. The platform not only provides a consolidated score but also breaks down how each individual feature contributes to that score. Credit unions receive a detailed risk summary that highlights both the strengths and weaknesses of a borrower’s financial profile, such as pros and cons across accounts, loans, and investments.
This allows loan officers to avoid relying on a "black box" algorithm. Instead, they can make informed, human-centered decisions. The tool further enhances this process by identifying potential red flags and suggesting specific areas for further clarification, guiding more effective follow-up conversations and face-to-face interviews with borrowers.
Embracing the Future of Credit Risk Assessment
In a rapidly evolving financial landscape, Akaike’s AI-powered Smart Lending Solution emerges as a game-changer for credit unions. By automating processes, enhancing risk assessment, and providing a transparent and customizable framework, this solution empowers credit unions to make informed and efficient lending decisions that benefit both the institution and its members. As credit unions navigate the complexities of modern lending, embracing these innovative technologies will be key to fostering inclusivity and paving the way for a more equitable financial landscape where all borrowers have the opportunity to thrive.
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