Saturday, 5 September 2026
Commercial credit underwriting is beginning to change from a largely sequential process built around documents, spreadsheets and separate software systems into a more integrated operating model in which AI can analyse information continuously, verify calculations and help lenders reach decisions faster. Speaking at Ai4 2026 in Las Vegas, Pranjal Daga, Co-Founder and CEO of Accend, argued that the next stage of commercial lending will depend less on adding individual AI tools and more on connecting the entire underwriting process through a common intelligence layer.
The distinction is significant because commercial credit remains highly fragmented at many financial institutions. A borrower may submit financial statements through one portal, while another system handles document extraction. Analysts then move information into spreadsheets, calculate ratios, retrieve credit information, prepare credit memoranda and transfer the results into a loan-origination platform, while portfolio monitoring may sit somewhere else entirely. Each component may work adequately on its own, but information has to move repeatedly between systems. In many institutions, the analyst effectively becomes the integration layer, reconciling formats, checking figures and manually carrying information across applications.
That creates both cost and risk. Daga presented examples from Accend’s own work in which financial information changed meaning during migrations or manual processing. These examples are company findings rather than industry-wide statistics, but they illustrate a familiar problem in credit analysis: an error introduced near the beginning of a workflow can affect ratios, credit memoranda and ultimately the lending decision itself. AI could reduce some of this friction by allowing information extracted at the beginning of the process to remain connected throughout underwriting.
Instead of document intake, financial spreading, credit analysis, memo creation and portfolio monitoring operating independently, the information could remain inside a shared borrower context. A financial figure used in a credit decision could potentially be traced back to the original statement, page or spreadsheet cell from which it was extracted. If an analyst corrects that figure, the change could then flow through subsequent calculations rather than being amended separately across several systems. This is particularly relevant to commercial real-estate lending, where underwriting can involve financial statements, rent rolls, property operating information, borrower accounts and multiple legal entities. The quality of the decision depends not only on analysing each source correctly but on preserving the relationships between them.
Daga described this broader architecture as an AI credit operating system. The concept combines document collection, financial spreading, credit analysis, memo preparation, portfolio management and scoring within one connected workflow. Specialised AI agents perform different parts of the process, while an orchestration layer determines which tasks should be handled and in what sequence. One agent may extract financial information from documents, another classify individual line items and another prepare a draft credit memorandum. Separate verification processes can then check whether financial statements reconcile or calculations remain internally consistent.
The important point is that AI is not expected to make every decision independently. Daga’s model separates activities where automation is relatively safe from those requiring judgement. Routine document processing, classification and reconciliation can be highly automated, while analysts continue to review exceptions and make decisions where experience or risk judgement matters. This could fundamentally change the work of credit analysts, particularly financial spreading, where considerable time is traditionally spent taking information from company accounts and placing it into standardised financial models before meaningful analysis can begin.
As AI increasingly handles that preparation, the analyst’s role can move towards reviewing information, investigating unusual results and deciding whether the underlying borrower represents an acceptable risk. This reflects a broader pattern emerging across enterprise AI: automation removes some of the mechanical preparation while increasing the importance of judgement, verification and accountability.
For banks and other lenders, the more profound change could come after the loan has been approved. Traditional commercial credit analysis often provides a snapshot of a borrower at a particular point in time. Financial statements are collected, ratios calculated and a credit score or internal rating assigned, followed by another formal review months later. AI combined with transaction information and other frequently updated data potentially moves credit monitoring towards a more continuous process.
Instead of relying primarily on annual or periodic reviews, lenders could monitor changes in payment behaviour, cash flows, financial information and other indicators throughout the life of a loan. A borrower’s risk assessment could therefore change as the underlying business changes. That has important implications for commercial real estate, where banks could potentially identify deterioration in a property or borrower earlier by combining conventional financial reporting with rent collections, operating expenses, debt-service performance and other available signals. The same approach could identify improving credit conditions earlier.
Rather than simply determining whether a loan should have been approved at origination, AI could therefore help lenders continuously assess whether the assumptions supporting the original decision remain valid. This could become increasingly important for portfolios containing offices, retail properties and other assets where occupancy, rents, costs and valuations can change materially between formal annual reviews.
Greater automation, however, creates a higher requirement for verification. One of Daga’s central arguments was that lenders should pay less attention to the accuracy of an individual AI model and more attention to the reliability of the complete system surrounding it. A powerful language model alone is not sufficient for high-stakes credit decisions. The architecture also needs deterministic calculations, reconciliation checks, testing, evaluation, source traceability, appropriate context and human review.
This distinction matters because financial analysis contains many areas where conventional software remains preferable to generative AI. Adding numbers, calculating ratios or confirming that assets equal liabilities does not require probabilistic reasoning. These operations can be performed through deterministic rules, while AI is used for tasks such as interpreting documents, understanding context or preparing narrative analysis. The result is likely to be a hybrid underwriting architecture rather than one large model making autonomous lending decisions: AI agents handle selected tasks, traditional software performs exact calculations, verification systems check outputs and humans remain responsible for judgement and exceptions.
This also raises an important competitive question for financial institutions. As major foundation models become widely available, access to the underlying AI itself may become less differentiated. Banks, fintech companies and specialist lenders can increasingly use many of the same commercially available models. The competitive advantage may therefore move towards proprietary data, workflow design and the historical knowledge accumulated through previous credit decisions.
A lender that retains information about how thousands of previous loans were analysed could potentially create a richer context for future underwriting. The system may understand not simply what a financial statement says, but how the institution historically treated similar businesses, which adjustments its analysts normally make and which risk signals have proved most important. Each completed transaction can potentially add information that improves future analysis, provided the system captures the corrections, decisions and outcomes generated by experienced analysts. In that sense, the valuable asset may increasingly be the accumulated institutional knowledge surrounding the AI rather than the model itself.
Auditability will consequently be critical. Every significant figure entering a credit analysis should ideally remain traceable to its original source. Lenders must be able to establish whether a number came from a financial statement, bank record or another data source, whether it was subsequently modified and whether a human approved the final result. This requirement is particularly important in regulated financial institutions, where faster underwriting cannot come at the expense of explainability.
The same principle applies directly to commercial real-estate lending. If an AI system calculates debt-service coverage, property income or borrower liquidity, the lender should be able to identify exactly which financial information produced the result. Automation without traceability could simply create faster errors.
The commercial impact of faster underwriting could nevertheless be substantial. Financing decisions can take weeks when lenders need to collect documents, spread financial statements and circulate credit memoranda internally. Reducing that process could become a competitive advantage for banks, particularly in commercial property where acquisition and refinancing timetables frequently determine whether transactions proceed. A lender capable of providing greater certainty more quickly may therefore compete not only through pricing but through execution speed.
Faster and cheaper underwriting could also influence which transactions lenders are prepared to consider. If analysts can process more applications without proportionally increasing staffing, financial institutions may be able to evaluate opportunities that were previously too expensive to underwrite relative to their potential revenue. This could expand sophisticated credit analysis further into smaller business loans and mid-sized commercial-property transactions.
There are also significant workforce implications. Daga predicted that financial spreading could effectively disappear as a standalone job as manual preparation becomes increasingly automated. The underlying activity will remain necessary, but credit professionals could spend more time supervising systems, reviewing exceptions, interpreting risk and exercising judgement. Expertise therefore does not necessarily become less valuable. It may become more important because experienced professionals will need to recognise when an automated conclusion is technically consistent but economically misleading.
The emerging credit operating model reflects changes occurring across finance more broadly. AI is gradually moving from isolated productivity tools into the underlying architecture through which companies make decisions. For commercial lenders, the next competitive divide may not be between institutions that use AI and those that do not, because most will eventually have access to similar models. The more important distinction could be between institutions that simply attach AI to existing fragmented workflows and those that redesign underwriting around connected information, continuous monitoring, verification and human judgement.
For commercial real estate, that transition could affect how quickly loans are approved, how borrowers and properties are monitored and how early lenders identify changes in asset performance. It could also make financing processes more responsive to changing market conditions by replacing periodic snapshots with a continuously updated understanding of borrower and asset risk.
The next era of underwriting may therefore be defined less by whether AI participates in credit decisions and more by how effectively financial institutions integrate it into the complete life cycle of a loan. The most valuable system will not necessarily be the one that produces the fastest answer, but the one capable of producing it faster while preserving the context, evidence and accountability required to trust the decision.
Source: CIJ.World Research & Analysis Team
