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.
欧洲 VAT 不是"13% 卖价 × 19% 税率"一道算式，而是 **4 类 VAT 流程并行 + 5 国税率差异 + 1 套平台代扣分离**——进口 VAT（清关时缴纳，可作进项抵扣）、销售 VAT（销售时计提）、IOSS（≤ €22 简化申报）、OSS（欧盟一站式申报），再加上亚马逊、eBay、速卖通对 IOSS VAT 的平台代扣。一笔订单在 VAT 这条线上要"过 4 遍税关"，而多数跨境电商的财务系统只有 1 个"代扣税合计"汇总行——**这就是为什么 43% 财务总监担心 VAT 漏报，38% 在月结前夜发现 VAT 差额对不上账**。本文用 4 张图 + 2 张流程图拆解欧洲 VAT 的 4 类流程边界、5 国税率与平台代扣的对账逻辑，并给出 1 个落地 3 步走的实操方案。
2024 年是电商数据合规的"分水岭年"——国家网信办、市场监管总局先后开出多张千万级罚单，最高单笔 5,250 万元，处罚事由集中在「未告知收集目的」「未经同意跨境传输」「敏感个人信息未单独同意」三类。本文把电商企业数据合规拆成 3 根支柱：**数据隐私**（《个人信息保护法》PIPL，2021-11-01 生效）、**数据安全**（《数据安全法》DSL，2021-09-01 生效）、**跨境数据传输**（《数据出境安全评估办法》2022-09-01 +《促进和规范数据跨境流动规定》2024-03-22 修订），对每根支柱讲清合规义务、判定门槛、电商财务数据的对应口径。文末给出一份 12 项合规自检清单与 3 张合规层级对照表，CFO / CTO / 合规官可直接拿走即用。
用友 U8 是中国市场存量最大的中型企业 ERP 之一——用友官方公开数据显示 U8 系列累计装机超过 50 万家企业，覆盖 V10/V11/V13/V15/V20 五个主要版本，对账集成从星空迁到 U8 最容易踩的三个坑是：① **协议分叉**——V13/V15/V20 提供 U8 API（RESTful + JSON），V10/V11 只能走 U8 WebService（SOAP/XML），同一家企业升级前后协议会变；② **UAP 平台深度耦合**——U8 不是「单据 + 表」的扁平模型，业务单据（销售订单/销售发票/收款单）走业务模块接口、底层凭证（GL_Voucher）走总账模块接口，两个 namespace 不互通，必须通过 UAP 平台的「单据转换」+「凭证模板」桥接；③ **handler 必须按版本号路由**——U8 没有统一 API 网关，每个版本号的字段名、必填项、审核流都有差异（V13 的 `VoucherCode` 在 V15 改名为 `VoucherNum`），集成中心必须按 ERP 版本号分发到不同的 handler namespace（`yonyou-u
服装是 SKU 维度最"密"的行业——一款 T 恤拆 6 个颜色 × 5 个尺码 = 30 个 SKU,一款羽绒服拆 8 色 × 6 码 × 3 长度 = 144 个 SKU。**某 GMV 11.6 亿的服装品牌 SKU 总数达 28 600+,月动销 SKU 4 800**,但其中 **42% 月销低于 10 件、18% 季末变死库存** [来源:某中高端女装品牌 2026 年 H1 SKU 动销明细]。SKU 维度从"款式"爆炸到"款式 × 尺码 × 颜色",再叠加服装独有的"季节性库存 + 季末 5-7 折 + 30-55% 退换货率",**让服装电商的 SKU 级核算,成为消费品行业里最复杂、对账失败率最高的细分领域**。本文拆解 4 个行业级特点 + 3 个反直觉发现 + 1 张行业对标图,讲清楚"服装财务的算账逻辑"为什么不能套用其他消费品行业的模板。
店铺级毛利率 18.5% 看着漂亮，账户里只剩 9% 的现金——中间差的 9.5 个百分点，并不在 GMV 里，也不在"GMV − 平台佣金"的简化公式里，而在 7 层扣减链条的 6 个隐性漏斗中。从 GMV → 商品实收 → 减平台佣金 → 减技术服务费 → 减广告费 → 减物流费 → 减退款损失 → 减公摊费用 → 净利，每一层都有反直觉的口径规则，跨层叠加后误差可放大 5~8 倍。本文按 7 层拆解，每层给出「业务口径 + 反直觉点 + 工程化实现」，并用一份 38 店 × 1200 SKU 的真实案例说明 CFO 月底为什么"账面赚、口袋少"。
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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
