Monk has launched its latest version of Cash Forecast, a machine learning-enabled software that forecasts receivables with an up-to-date, invoice-by-invoice breakdown of cash inflows expected to be collected. Traditional methods rely heavily on collection rate assumptions, so the new platform looks individually at payment patterns for each customer, engagement status, and account history. As customers continue payment, raise disputes, or settle their payments, a finance team will constantly see their forecast update with the actual money, potential amount of money at-risk and forecasted ranges that are real-time visible. Forecasting is connected with collections in the solution, which integrates automatically all signals coming from promises to pay, partial payments, credit memos, refunds, and write-offs, guiding finance teams on the best collections to focus on.
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This introduction happens during a timeframe when organizations are looking for tangible returns on AI investments, In particular in financial areas such as cashflow prediction and invoice-to-cash workflows. “Boards have told finance to use AI, and they have named cash forecasting as the place to start, but most teams still cannot produce a forecast they trust,” said George Kurdin, Founder and CEO of Monk. “That is the gap we built Cash Forecast 2.0 to remove. When the forecast runs itself and reflects how customers pay, a finance leader stops rebuilding spreadsheets and starts making decisions. Cash flow becomes the metric they manage the business on.” The new solution aims to improve forecasting accuracy, reduce manual spreadsheet work, and enable finance leaders to make faster, data-driven cash management decisions.
