Speed became a key characteristic in the financial experience, especially in the area of credit. If a client has to wait days to receive a response to their request, they may delay the underwriting process, look for another institution, or interpret the delay as a sign that the process is not going well.
But it is also true that accelerating credit processes without compromising the quality of the analysis can be complex: Traditional models depend on manual processes, multiple sources of information and static rules that can limit the ability of institutions to evaluate large volumes of applications.
This is where artificial intelligence is redefining underwriting. Through AI credit scoring, alternative data analysis and machine learning models, financial institutions can assess risk more quickly and build automated underwriting processes capable of adapting to different customer profiles.
Key Takeaways
- Speed is crucial in credit processes, as delays can lead clients to seek alternatives.
- AI credit scoring improves underwriting by analyzing alternative data and automating processes for better risk assessment.
- AI-powered models can predict loan performances more accurately than traditional methods, especially for thin-file customers.
- Human oversight is vital to ensure transparency and equity in automated credit decisions, avoiding algorithmic black boxes.
- The future of lending lies in real-time decisions, integrating AI with human judgment for effective risk management.
Table of contents
From traditional analysis to AI-powered underwriting
We have two realities converging: on the one hand, traditional underwriting combines financial information, credit history, income, debt level and predefined rules.
Of course, these elements are still relevant, but they have a limitation: they do not always capture the entirety of an applicant’s financial behavior.
On the other hand, AI-powered underwriting systems can analyze large volumes of structured and unstructured information, identify non-linear relationships, and update risk assessments more frequently.
Research from the Bank for International Settlements (BIS) noted that AI can expand credit scoring using alternative data such as banking transactions, rent payments, utilities and telecommunications.
So, with this the fundamental question changes: instead of asking whether a client has sufficient credit history, institutions can analyze what available signals allow them to better estimate their payment capacity and behavior.
AI in underwriting lending: more data, but also better decisions
The implementation of AI has allowed financial institutions to work with clients that were traditionally difficult to evaluate.
According to the World Bank, alternative data that can be obtained through the use of AI can complement the information from credit bureaus and be especially relevant for clients with limited or non-existent histories, known as thin-file customers.
The growth of digital wallets, electronic billing, payment platforms and e-commerce is generating new sources of information to evaluate consumers and small businesses.
The idea is not simply to approve loans faster, but to build credit risk AI models capable of better differentiating between risk profiles and expanding access to financial products without depending exclusively on traditional variables.
Evidence points to more predictive models
Credit models built with machine learning and non-traditional data are able to predict losses and defaults better than traditional models, especially during periods of economic stress, the BIS research also revealed.
Another investigation by this organization on loans to small businesses found that fintech lenders could use their internal models to predict delinquencies more accurately than certain traditional credit scores, with particularly relevant improvements in areas with higher unemployment.
More recent research is also delving into this evolution. An academic review published in 2026, which analyzed 118 studies on AI-based credit evaluation, identifies hybrid models, alternative data, real-time learning, Explainable AI and equity mechanisms as the main lines of evolution.
This points to a new generation of underwriting: systems that not only automate existing rules, but also learn from large volumes of data and can adjust their evaluations as behavioral patterns change.
Automation does not mean eliminating human control
Human supervision remains a fundamental aspect in any operation with AI: a credit process cannot be an algorithmic “black box” in which no one understands the reason for the decisions.
Although automated decisions can be fast and accurate, financial institutions need to know why a model rejected a request, what variables influenced the decision, and whether the result can be reproduced and audited.
Equity is also critical. Federal Reserve research notes that advances in machine learning can expand access to credit, but warns that the benefits are not necessarily distributed evenly across demographic groups.
Therefore, a business automated underwriting model must incorporate validation mechanisms, explainability, bias monitoring, traceability and human review from its design when the risk level requires it.
The real challenge: building a smart credit system
Any financial institution looking to implement AI should start with this question: “what part of the decision process do we need to improve?”, rather than simply asking what model they can use.
A mature architecture can combine internal data, external authoritative sources, credit scoring models, business rules and machine learning capabilities within an automated flow.
We give you this example: a request can be received digitally, enriched with available financial information, evaluated by a risk model and classified according to different levels of exposure. Low-risk cases can advance automatically, while more complex requests can be sent to an analyst for review.
This approach allows AI to be used to accelerate decisions without making it the only decision mechanism.
The Financial Stability Board warns that the adoption of AI in financial services also introduces risks related to data quality, dependence on third parties, cybersecurity and model risk.
Therefore, the goal should not be simply to automate underwriting. It must be building a faster, predictive, explainable and governable process.
The next competitive advantage of lending
The objective of financial institutions is for credit to evolve towards a model in which the decision occurs practically in real time, using more information and requiring less manual intervention.
This can result in shorter approval cycles, better segmentation capabilities, screening of customers with little credit history, and a much more competitive digital experience.
While speed alone is not innovation, true transformation occurs when AI credit scoring, alternative data, automation and governance are integrated into an architecture capable of consistently making better risk decisions.
It is key to understand that AI does not replace financial judgment: it amplifies it, and for institutions that still rely on fragmented processes and manual decisions, the question is no longer whether underwriting will change with AI, it is how long they can afford to wait to change it.
