Case study · P&L assembly automation
Automating P&L Assembly for Self-Employed Tax Resolution Cases
Rima turned a week-long, preparer-dependent P&L task into a two-hour guided review with consistent classification across every case.
Templates
11 business types
Before
~1 week per P&L
After
~2 hours per P&L
Output
Guided review
The challenge
Every self-employed case started with a financial picture that had to be built from scratch.
Self-employed taxpayers make up a significant share of IRS collection cases. Unlike W-2 wage earners whose income is documented on a single form, self-employed clients require a full Profit & Loss statement to establish their income and allowable business expenses. That P&L is not optional. It feeds directly into IRS Forms 433-F, 433-A, and Schedule C. Without it, the case cannot move forward.
The raw material is a mess
The starting point for every self-employed P&L is a stack of bank statements, sometimes 12 months or more across multiple accounts. The firm's case managers had to read every transaction, determine whether it was business revenue, a business expense, a personal expense, or a balance sheet movement like a loan payment or transfer, and assign it to the correct category. For a typical client, that meant classifying hundreds of transactions by hand. For clients with high transaction volumes, the number reached into the thousands.
Classification is judgment, not data entry
The difficulty was never just volume. Every transaction required a decision. Is a payment to a big-box retailer a business supply purchase or a personal grocery run? Does a deposit from a factoring company count as revenue, or is it already captured in a settlement sheet? When a client pays for fuel with a business account, does it go to Vehicle Expenses or Cost of Goods Sold? The answers depend on the client's business type, the firm's conventions, and IRS rules, and they change from one self-employed client to the next.
No two preparers did it the same way
The firm's forensic analysis of its own P&L workbooks revealed the core problem: inconsistency. Different case managers used different category names for the same expense types. Column structures varied from workbook to workbook. Some preparers built cash flow reconciliations and labeled them as P&Ls. Settlement sheet revenue was sometimes double-counted against bank deposits. Date formatting errors corrupted transaction records. The work product depended entirely on who prepared it, not on any firm standard.
The downstream cost was invisible but compounding
Every inconsistency in a P&L cascaded into the IRS collection forms it fed. An incorrectly classified expense meant a wrong number on the 433-F. A missing revenue source meant an inaccurate income figure on the 433-A. These errors did not surface until the IRS reviewed the submission, at which point they triggered requests for additional information, delays, and in some cases rejected resolution proposals.
The solution
Rima AI made P&L assembly repeatable, reviewable, and industry-aware.
Rima AI's P&L Assembly engine replaces the manual, preparer-dependent process with a config-driven wizard built on a keyword classification system spanning 11 business-type templates, each developed from forensic analysis of real client datasets.
Business-type templates that encode industry context
When a case begins, the system loads a classification template matched to the client's business type: owner-operator trucker, solo trades contractor, employer-based services firm, retail e-commerce, food service, rideshare/gig, and five others. Each template carries its own category set, keyword rules, and profile flags that activate the correct classification logic for that industry. A trucker's fuel purchase routes to a different category than a restaurant owner's, automatically.
Three-tier classification waterfall
Every transaction runs through a structured classification sequence. First, deterministic rules handle transaction types that are unambiguous regardless of context: transfers, loan payments, and balance sheet movements. Second, keyword matching against 2,000+ merchant patterns classifies the transaction by payee name. Third, transactions that cannot be resolved by rules or keywords are flagged for human review, with the specific reason for the flag and the preparer's most likely category suggested.
Revenue source hierarchy
For clients who receive payments through both direct bank deposits and settlement or factoring companies, the engine applies a revenue source hierarchy that prevents double-counting. Settlement sheet revenue takes precedence, and corresponding bank deposits are automatically excluded from revenue totals. This eliminates one of the most common and costly errors in manual P&L assembly.
Reconciliation and review report
The completed workbook includes a reconciliation tab that ties the P&L back to source bank statement balances and a review report that lists every flagged transaction, every classification override, and every gap the preparer needs to address. The EA reviewing the P&L sees exactly where the system made decisions and exactly where human judgment is still needed.
The results
From a week-long task to a two-hour review.
Rima standardized the most judgment-heavy part of self-employed case preparation and gave the firm a repeatable output that holds up downstream.
Scale: 11 business types, one engine
Rather than training case managers on the nuances of each self-employment category, the firm codified those nuances into templates. A new case manager can produce a P&L for an owner-operator trucker or a solo consultant without needing years of experience classifying transactions in that industry. The knowledge lives in the system, not in the preparer's head.
Accuracy: consistent classification across every case
The classification system auto-categorizes the majority of transactions without human intervention, with remaining transactions routed to review alongside context and suggested categories. More importantly, the system eliminated the category of errors that manual preparers never caught: inconsistent naming, structural misclassification, and revenue double-counting.
The biggest impact: P&Ls that hold up downstream
The real payoff was not in the P&L itself but in everything the P&L fed. When the income and expense figures on a P&L are accurate and consistently derived, the 433-F and 433-A forms populated from that P&L are accurate too. The firm reduced the cycle of IRS information requests and resubmissions that had been adding weeks to case resolution timelines for self-employed clients.
The bottom line
The construction work became a repeatable system.
Self-employed cases are the hardest cases in a tax resolution firm's portfolio, not because the IRS rules are more complex, but because the financial picture has to be built from scratch. The firm was asking its most skilled people to spend their time on the construction work instead of the analysis. Rima AI's P&L Assembly engine moved the construction into a repeatable, auditable system and gave EAs back the hours they need to actually resolve cases.
This capability works in concert with the firm's existing use of Rima AI for IRS Form 433-F preparation, creating an end-to-end automated pipeline from raw bank statements through completed collection forms, all built on the same rule engine and review workflow.
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