P2P

P E E R T O P E E R M A G A Z I N E · S P R I N G 2 0 2 6 33 The shift is from managing what happened to shaping what happens next. IMPROVING CASH FLOW: PREDICTING PAYMENT BEHAVIOR One of the most fundamental financial questions in any firm: when will invoices be paid? Traditional systems can show when invoices were issued and when they were eventually paid. Predicting payment behavior has historically required institutional memory and manual analysis: experience with specific clients, historical averages, and educated guesswork. AI makes this prediction far more precise. By analyzing historical billing and payment behavior across thousands of invoices, AI models identify patterns that humans cannot track at scale. These models estimate expected payment dates at the individual invoice level, accounting for client payment history, billing attorney, practice area, invoice characteristics, seasonal trends, and other signals embedded in years of billing data. Aggregated across the firm's entire receivables portfolio, these predictions provide a continuously updated forecast of when accounts receivable will convert to cash, replacing broad assumptions with a dynamic, invoice-level view of expected inflows. SMARTER COLLECTIONS: TRIAGE AT SCALE Forecasting is only part of the opportunity. Collections management is where AI can most directly improve financial outcomes. Most firms today drive collections through aging reports. Finance teams review invoices at 30, 60, and 90 days past due and determine where to focus follow- up. While useful, this approach treats overdue invoices as roughly equivalent. In reality, they are not. Some clients consistently pay late but reliably. Others may appear current but show early behavioral signals of financial risk. Industry benchmarks suggest that a relatively small percentage of outstanding invoices account for a disproportionate share of eventual write-offs. Yet, without deeper analysis, finance teams spend time chasing invoices that would have been paid anyway while missing the accounts genuinely trending toward loss. AI allows firms to approach collections more strategically. By analyzing historical payment patterns and client behavior, AI models generate relative credit risk scores that reflect the likelihood of payment delays or nonpayment. When a client misses an expected payment date, the system immediately surfaces accounts with the most concerning risk profiles. Clients with reliable payment histories require less urgent intervention; those with elevated risk move to the top of the queue. One important and often overlooked dimension: this approach also protects client relationships. Finance professionals and partners alike are sensitive to the relationship cost of unnecessary collections outreach. AI-driven prioritization means fewer touchpoints with clients who will pay on their own timeline and more focused, timely attention where it actually matters. Earlier, more targeted outreach reduces the likelihood that invoices age into disputes, preserving both realization and the relationship. A single collections professional responsible for hundreds or thousands of active invoices cannot manually evaluate that portfolio in any meaningful depth. AI can continuously monitor it, identify the accounts where immediate action is most likely to pull cash forward, and allow collection teams and partners to focus where intervention changes the outcome. AI allows firms to approach collections more strategically.

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