Qeasy Smart Reconciliation · Running in production at large-scale e-commerce companies
Multi-platform statements, refunds, subsidies, commissions, shared expenses — every penny a platform pays you is automatically matched line by line against your own books. Whatever doesn't match comes with the business reason and a suggested action; whatever does flows straight into Kingdee, Yonyou or SAP. No more 'manual exports + row-by-row Excel checks' for your finance team.
Marked as 'matched' on the books, yet the difference was never truly explained — that's the reality for most e-commerce finance teams. Reconciliation was never as simple as subtracting one column from another.
At month-end, three people eyeball six platform statements against one supply-chain export, row by row
Statements from every platform are collected automatically and matched order by order — people only review what doesn't match
Platforms only give you the net amount after deductions — subsidies, coupons and compensation all lumped together
Every deduction type is itemized; each difference is auto-attributed to a concrete business reason with a suggested action
Refund overhang and cross-period settlement: money refunded before goods return, this period's cash for last period's orders
Refund-only, instant refund, FBA refund and cross-period income are auto-identified and attributed to the right accounting period
Shared expenses allocated by gut feel — nobody knows which SKU actually makes money
Shared expenses are auto-allocated to stores and SKUs, revealing true per-item profit after allocation
Reconciliation is only the starting point. Every reconciled datum feeds fine-grained accounting across five dimensions — store, SKU, gross profit, tax and cash — ready for business decisions.
Ten JD stores, twenty Douyin shops, three Amazon marketplaces — all unified into one financial view, with cross-store consolidation and combined statements in one pass.
Trace every order down to each item: per-SKU revenue, cost and allocated expense, with per-item profit precise to the cent — loss-making SKUs at a glance.
Seven deduction layers from GMV to net profit: net receipts → platform commission → service fees → ads → logistics → refund losses → shared expenses. Every layer evidenced.
Platform-withheld product tax, shipping tax and promo-rebate tax are separated automatically; cross-border multi-currency (USD / EUR / JPY), EU VAT and export rebates covered.
Sales collections, refund recoveries, platform rebates, procurement, logistics, commissions, taxes — four inflows and four outflows, finely modeled, with collection forecasts and close cadence under control.
From reconciled to posted: sales, refund, commission and adjustment vouchers generated automatically; provisional receivables pushed down to financial receivables; out-of-the-box integration with Kingdee, Yonyou and SAP.
Many platforms, one set of books
JD nets whole orders; Douyin matches in five rounds; Alipay has three statements (aggregate settlement, account flow, Yulibao); Amazon reconciles receipts and refunds separately; AliExpress has five bill types plus fully-managed JIT. Every platform's rules are built in as ready-to-use capabilities — new statement formats are adapted within days, so your finance team never has to study each platform's statement documentation again.
Promo-season surges, handled
Before the campaign: pre-run reconciliation rules, stress-test data, simulate collections. The first day after: results ready. The platform processes a million statement lines a day and aggregates 100k-order reconciliations in seconds. With the CFO cockpit, sales, margins and collection cycles are monitored in real time, and anomalies like failed matches and refund overhang alert automatically — the 'reconciliation surge' is routine for the system, and no longer overtime season for finance.
AI finance assistant
'How many unreconciled differences are left on Douyin this month?' 'Adapt the new platform's statement format for me.' Business users state needs in natural language; the AI assistant reconnoiters, writes and tests on its own — humans confirm once before launch. When a platform changes its rules, you respond the same day instead of queueing for development.
Measured across 28 mid-sized e-commerce rollouts in 10 industries (annual GMV ¥50M–¥5B): returns come from labor, error and compliance savings, plus the cash-flow benefit of a faster close.
Source: smart-reconciliation rollout sample, Aug 2026 (28 companies across 10 industries: beauty, 3C electronics, apparel, home, mother & baby, food, sports, pet, auto aftermarket, appliances).
One-click import from every platform — no manual exports, no missing statements
Three-way match across platform statements ↔ orders ↔ your books; matches pass automatically
Mismatches are auto-tagged with 23 business reasons, each with a suggested action
Shared expenses auto-allocated to stores and SKUs, writing back true profit
Results pushed to Kingdee / Yonyou / SAP in one click, vouchers auto-generated, fully audited
Supports 'daily clear, daily close': daily reconciliation, weekly margins, monthly P&L, quarterly balance sheet, annual tax settlement — the cadence follows your management needs, not just a month-end sprint.
23 built-in e-commerce variance reasons. Every unmatched line is no longer 'difference ¥245.04, handled manually' but a clear reason plus a clear suggested action — readable by new hires, acceptable to auditors.
Platform-funded coupons and instant discounts, auto-accepted within ¥0.01
Platform compensates the buyer after dispute rulings and deducts from you — auto-linked to the order
This period's refund against last period's order — income reversed in the right period
Refund overhang — money returned, goods not — flagged and tracked
Platform pays in installments — settled and pending amounts booked separately
Postage / price make-up orders identified, differences auto-billed as expense
Zero-price gift lines flagged, with a reminder to check for missed receivables
Product, shipping and promo-rebate taxes itemized separately
Finance states needs in plain language; the assistant reconnoiters, writes and tests on its own — humans confirm once before launch. Four assistants, each with its own job, dispatched from one entry point.
bill-parse-agent
When a statement arrives in a new platform or format, describe it and the assistant learns to read it — validated before it goes live.
reconcile-script-agent
Runs line-by-line matching under your rules, explains every mismatch in business terms, and verifies results before you rely on them.
expense-allocate-agent
Allocates shared expenses to stores and SKUs under your rules, writes back true per-item profit, with verifiable results.
general-assistant
'How many unreconciled differences on Douyin last month?' Ask in plain language — it queries, delegates and answers from one entry point.
The screens below come from the real system running in production (demo account) — what you see is what you get.
Domestic coverage spans JD POP, Douyin Shop, Pinduoduo, Xiaohongshu, WeChat Channels, VIP.com and the Alipay funds system; cross-border coverage spans Amazon, Temu, Shopee, Lazada, eBay, AliExpress, Shopify storefronts and Newegg. Every platform's reconciliation rules are built in, with new platforms onboarding continuously.
JD POP
Douyin Shop
Alipay
Amazon
Pinduoduo
Xiaohongshu
WeChat Channels
VIP.com
Temu
Shopee
Lazada
eBay
AliExpress
Shopify
NeweggPost results in one click: provisional receivables pushed down to financial receivables, expense variances auto-billed as payables, vouchers generated under your chart of accounts. Kingdee Cosmic receivables can join reconciliation at line level — a closed loop across platform statements ↔ business orders ↔ ERP accounting, with no manual data shuttling between systems.
Continuously updated from the production frontline: multi-platform reconciliation manuals, variance-handling playbooks, fine-grained accounting methods, cross-border specials and customer cases.
电商平台的"代扣代缴"从来不是一个动作，而是**5 个时点 × 8 类分录 × 3 个抵扣链条**的复合动作——代扣时点（结算日 vs 销售日 vs 清关日）、计提时点（权责发生制 vs 收付实现制）、缴纳时点（月度 vs 季度）、抵减时点（进项税 vs 税前扣除）、退税时点（出口退税 vs 留抵退税）。任何一个时点写错科目或漏记账，月底对账就会出现「账面金额 ≠ 申报金额 ≠ 平台代扣金额」的三方不一致，触发预警。本文用「5 大时点 + 8 类分录 + 3 个对账系统的联动」把这件事一次性说透——把分散在金税四期、平台规则、行业规范的代扣处理，翻译成 CFO 可执行的标准化分录与对账校验。
双 11、618、年货节、38 节……每一次大促，财务团队都要面对一次「对账洪峰」——平时 5 天处理完的账单量，大促后 48 小时内必须清完，否则赶不上月结。多数企业靠「加人 + 加班」硬扛，但 2026 年覆盖 50 家中型电商财务团队的调研显示：**63% 的财务团队在大促后第 2 周仍有未清完的跨期账单，42% 的 CFO 因「账期不准」被 CEO 反复追问，29% 的财务人员在大促后第一个月离职**。本文给出经过 6 家年 GMV 5 亿以上电商验证的「大促对账黄金 72 小时」节奏表，配套 AI 时代 4 大应对策略（异步任务削峰、AI 沙箱自动扩、23 业务标签码分流、AI 智能体自助诊断），让财务团队从「加人加班」升级为「让 AI 替代财务加班」。
做电商对账,最难的不是某一平台怎么对,而是**怎么让 5 个平台同时跑稳**——京东 POP 用整单轧差、抖店用五轮匹配、亚马逊得分向不轧差、支付宝三表要对、速卖通 5 个 billType 各有差异。这篇文章把 2026 年 9 月 5 大平台全部稳定运行后沉淀的 5 条工程纪律拆开:**平台聚合范式(不抽象基类)+ 平台隔离 BullMQ 队列 + 平台默认脚本+店铺级覆盖 + 23 个 diffReason 跨平台统一 + CAT/SR/ZC 核算项目命名规范**。每条配一段真实事故或压测数据,业务人员与财务经理照搬即可跨过最常见的 5 类陷阱。
一家年 GMV 9.6 亿的多平台电商，2026 年 6 月月结时 CFO 想回答 3 个问题——这个月到底卖了多少、净利多少、库存还能撑多久。财务经理翻了 4 套系统凑出 3 张表，**会议开了 3 小时，问题没答完**——不是没数据，是数据没聚合成「CFO 视角的 5 个数字」。本文围绕 CFO 财务驾驶舱的 **5 大核心指标**：销售额、毛利率、净利率、库存周转、回款周期——给出每个指标的**计算公式 + 5 个同环比口径 + 实时监控阈值 + 异常告警分级**，并回答「5 个指标背后的数据怎么从 T+10 压到 T+1 自动喂数」。文末附「CFO 驾驶舱 12 条硬指标自检清单」，可直接拿去做系统验收。
当一份对账差异表里有 14 行 FAILURE，传统的呈现方式是「自由文本 + 数字」。审计师拿到这张表的第一反应是「我需要把每一行的差异原因重新归类」——这一步往往占去 60-70% 的现场工时。23 个业务标签码（diffReason + diffDisposalSuggestion 双字段）做的就是把这一步从「现场归类」前移到「系统标注」：每条差异在落到数据库那一刻就带上了「机读码」与「中文处理建议」两个标签，审计师拿到的是一张可机读、可分组、可穿透到凭证的差异地图，而不是一锅自由文本。
电商财务对账做到 80% 后撞上的那堵墙，根源不在引擎、不在 ERP，而在「差异原因」字段是不是结构化。本文拆解 v3 对账体系里最容易被忽视、却承担 80% 自动化收益的工程支点——「机读码 + 人读建议」双轨设计：23 个 `diffReason` 给机器消费、决定下游集成脚本按码精准出单；`diffDisposalSuggestion` 给人阅读、保留财务手补痕迹不被自动重跑清空。这对字段采用**不对称落库语义**，正是把对账从「工程实现」升级为「业务规范」的关键设计。
Your ERP keeps your internal books, but platform statements live outside it — how much commission was deducted, which coupons were funded, how much tax was withheld. The platform reconciles platform statements, business orders and ERP entries three-way, then pushes results back into the ERP for posting. It doesn't replace your ERP; it covers the part your ERP can't see.
Fifteen platforms are covered: JD POP, Douyin Shop, Alipay (Tmall), Pinduoduo, Xiaohongshu, WeChat Channels, VIP.com, Amazon, Temu, Shopee, Lazada, eBay, AliExpress, Shopify and Newegg. Onboarding a new platform is a routine capability, not a custom project — statement formats are adapted within days, with the AI assistant doing most of the work.
No. Adaptation rules are visual and version-managed, and the AI assistant helps with the change: describe what changed, adjust automatically, validate before enabling — business users respond the same day instead of queueing for development. Every change is audited and rollbackable.
The AI assistant has no direct write path to your books: all validation runs in an isolated environment; every real execution requires human confirmation; every step is audited and rollbackable; and permissions for query / authoring / testing / execution are strictly separated. The AI proposes; humans decide.
We recommend a single-platform pilot: the 'import → auto-match → variance attribution' loop runs within weeks, then expands platform by platform each month, and finally connects to your ERP for automatic posting. Companies completing the full rollout typically see 8–12× efficiency gains, error rates dropping from ~5% (manual) to around 0.1%, with full payback in a median of 26 months.
Our core product Qeasy focuses on enterprise data integration and application building. This smart reconciliation platform is Qeasy's flagship enterprise AI deployment, serving real financial reconciliation at large-scale e-commerce companies — not a proof of concept, but a pipeline running in production every month, turning reconciliation from a month-end sprint into a daily business routine.
Monthly reconciliation pipelines running in production at large-scale e-commerce companies
15 platforms' rules built in; 23 variance reasons codified
Auditable steps, rollbackable versions, tests isolated from real books
New platforms and rules adapted fast; the AI assistant keeps lowering the bar
