Leaderboard

In Narratives process for US commerical mortgage banking deals, AI(Berkie) is being embedded directly into analysts day to day workflow to help draft core Narratives sections. Original (Pre-AI) workflow and performance expectations Before AI tool called Berkie was used, the Narrative draft workflow followed a manual, time sensitive sequence Document Collection & Review Analyst manually gathered and studied multiple source documents: Offering Memorandum, Appraisal report, Property website, Borrower website/Sponsor details, Rent comps, Sales comps, expense comps from external websites, Crime reports, Google/Google Maps for location & aerial review, and latest financial analysis. Drafting Narrative sections manually Analyst wrote every section from scratch: Property overview, Location overview, Borrower/Sponsor overview, Management overview, Market Overview, Rent/Sales/Expense comps, Strength & Weakness, Risk & Mitigants, and Crime reports Performance expectations Accuracy of extracted data, Consistency in writing style, ability to identify key risks and themes, 100% manual verification, Turnaround time was typically longer(5hours to a full day depending on deal complexity) The process heavily relied on the analysts attention to detail, writing ability, and familiarity with CRE underwriting. What AI (Berkie) now does in the workflow With introduction of Berkie, analysts now use a structured marketplace of prompts for each narrative section. Analysts upload relevant documents (Offering memorandum, Appraisal, reports), URLs where possible(property site, crime data, etc), Screenshots or extracted information where login access restricts data. Berkie's role: Reads the attachments and URLs, Uses predefined prompts for each section, Produces a first draft narrative, structures the content according to the standard CRE narrative format (Freddie, Fannie agency template), Extracts factual information (property details, comps, management info, location attributes, market trends, etc) Analysts role afte AI input: Validate numerical accuracy, Fix missing or misinterpreted insights, Add deal specific perspective, Ensure compliance with underwriting and agency guidelines, and Finalize risks, mitigants, and subjective assessments. This has shifted analysts responsibility from writing everything to reviewing, correcting, and fine tuning. One situation where AI improves Improvement scenario : Enhancing speed & consistency in Market overview section. The market overview section often requires synthesizing market rents, vacancy rents, Employment trends, population growth, local economic drivers, competitors property performance. Before Berkie(AI), Analysts spent significant time pulling this from multiple sources and writing a clear Narrative How Berkie(AI) improves it: Berkie extracts and organizes market stats quickly, generates consistent writing style across deals, Highlights macro trends an analyst might overlook, saves hours of manual research & writing. Impact: Faster turnaround, reduced analyst workload, and more uniform quality across the team. One situation where AI could introduce risk, bias, delay or hidden errors Risk scenario: AI misinterpreting a financial data or comps Berkie may mis-read or mis-interpret Rent comps, Sales comps, Expense line items, NOI or DSCR calculations, Sq footage discrepancies between OM V/S Appraisal, Property photo context or map locations. Example: If Berkie incorrectly intreprets rent comps(e.g mistakes asking rent for effective rent, or uses older data from attachments), the narrative could inaccurately reflect market positioning leading to misinformed lender decisions. Why this creates a risk: Financial misreads may not be obvious during a quick review, Berkie/AI sometimes hallucinates missing data, Analysts may overtrust the AI draft, Incorrect comps analysis affects valuation, underwriting, and risk assessment. Potential outcomes: Undetected errors -> Misleading Narratives, Delays if analyst must significantly rewrite sections, Bias if AI leans toward overly positive/negative language, and Risk missing key red flags( e.g deferred maintenance, tenant rollover, poor crime trends) To manage these risks and bias, the workflow must treat AI as a drafting assistant with clear expectation that analysts must Cross check key data points against source documents, consciously adjust for optimistic marketing language v/s independent data, Document any data conflicts or uncertainities in the Narrative or internal notes. This balance using AI for speed and consolidation, while keeping human analysts fully accountable for accuracy and judgement is what turns AI from simple automation tool into a genuinely collborative part of the Narratives process.

During each month-end close, OpEx Accounting is responsible for posting AP (Accounts Payable) Unposted line entries to the GL (general ledger). AP Unposted lines result from invoices that are placed on hold and not posted to the GL because they can’t be matched to a PO (purchase order) and receipt. This could be because there is no PO# on the invoice, conflicting information on the invoice vs PO. During the month, AP investigates to try and match invoices to POs and determine the right GL coding, so they can be released for payment and posted to the GL as an expense. However, since this can be difficult, coding is often assigned to the wrong cost center, location, or account, and many invoices remain unresolved. Hence, AP will prepare a consolidated report of all non-postable invoices send it to OpEx to use for further investigation and posting the final journal entries. OpEx communicates with Finance Partners (FP’s) who research and reach out to business partners to determine the right coding. FP’s then e-mail OpEx with GL coding change requests and OpEx adjusts the entries one by one. Close to 200 line item adjustment entries are manually created each month. Furthermore, in order to permanently correct coding on future invoices, trouble tickets must be submitted to both AP and Procurement. However, it is observed that many FP’s ignore to update the trouble tickets. As a result, the same invoices end up re-appearing on next month’s report, and are continuously coded incorrectly. This is a time-consuming, unscalable, and error-prone process that undermines operational efficiency, as well as the reliability of financial reporting. Root Causes Analysis: AP Error: -When the invoice is coded incorrectly even if: -there is a valid PO# listed on the invoice -bill to/ship to entities match on both the PO & Invoice -vendor/supplier match on the PO & Invoice -line items, quantities, and $ amounts on both documents match -When the total of invoice distributions does not equal the invoice amount entered in OFA Vendor (Supplier) Error: -When there is no PO listed on the invoice, or the invoice is invalid (may also be listed as 'Invoice over $10,000' <-- invoices over $10K need a valid PO on them to be processed) -When there is no PO, and the PO requestor cannot be found (applies to invoices that don't require PO or are under 10K, also listed as 'Non-Postable: Workflow Needed, or 'Unable to find requestor') -When there is an invalid distribution/bank account for the vendor (also listed as ACH Reject) Business (Amazon's) Error: -When the PO is missing the cost center, tax, or other line items -When the release # for an invoice is not created on a PO (Each release # corresponds to a line on a PO. For each release, the vendor will send the desired amount of goods/services on the PO line corresponding to the release # and also send an invoice for it) -When the business hasn't added more money to the PO, when they know they have placed orders exceeding the remaining PO amount -When the business places an order before having a fully approved PO Unknown: -When the quantity billed exceeds the quantity received (could be business or vendor error) -Unable to match items/judge which lines to match (Unable to match invoice to PO for various reasons) -When the vendor/supplier on the invoice doesn't match the one on the PO (could business or vendor error) -When they are different bill to/ship to entities on the PO vs Invoice (could be business or vendor error) -When the invoice price exceeds the purchase order price (could be business or vendor error) -Duplicate invoice Solution: An automated metrics dashboard was created to assist AP and Procurement with their analysis and efforts to minimize unposted lines. Meetings were held with both teams to ensure OpEx was providing metrics with appropriate root cause analysis (refer below for Root Cause Analysis). The current dashboard is split into 4 areas: Hold Category Metrics, Supplier Metrics, PO Requestor Metrics, and Coding Change Metrics. Users can view the number of unposted lines and $ amounts per hold category, supplier, and requestor, as well as the lines and $ amounts for coding change requests per hold category and root cause. This aids in identifying whom to contact and what problem areas to focus on first. (i.e. if there is a PO requestor with a large quantity of unposted lines under their name, they may need to be trained on how to properly prepare a PO requisition). Benefits •Compliance: The metrics dashboard helps pinpoint root causes of error, so action is taken to prevent future on-hold invoices stemming from the same reasons, ultimately reducing unposted lines. Reducing lines allows more invoices to be released for payment so expenses can be recognized in the period they were incurred in. This increases timeliness and relevance of financial data, and is in accordance with accounting’s matching principle5. •Transparency: The metrics dash board provides a much better audit trail and more simplified way of tracking, as opposed to searching through e-mails in order to recall and justify coding changes. This improves our trust and relationship with stakeholders3, and mitigates audit risk. •Customer Obsession: AP and Procurement no longer have to sift through countless tickets to fix each invoice/PO. This metrics dash board consolidates all errors into one list that can be sent to both teams to ensure coding is fixed for any future or unpaid invoices on the same PO. •Efficiency: Allowing FPs and AP team to review their errors saves the time spent by OpEx investigating and revising. The extra time can now be used to thoroughly review support for coding changes, assist team members with other pressing close issues, and conduct more detailed reviews of journal entries to ensure all are posted correctly. Benefits: Time Saving – Effort saving of 1232 Hours/Annum