When AI Starts Taking Over Financial Audits, Companies Truly Need Finance

AI moves from pilot to core audit work

Chinese retailers and chains are quietly handing routine audit work to AI — and that forces a rethink of what finance teams actually do. At a recent Huxiu (虎嗅) Think Tank seminar, executives argued that once AI can absorb high-volume, rule-driven checks, the real business question becomes: who defines the rules and who owns the exceptions? The debate is not about whether AI can read invoices; it is about governance, responsibility and the managerial work that remains when machines do the repetitive tasks.

Case studies: Haoxiangni and 合思

Haoxiangni (好想你) has been one of the early adopters. It has been reported that Haoxiangni’s pilot — focused first on high-frequency, clear-rule scenarios such as travel and entertainment expenses — boosted audit efficiency roughly threefold and improved risk-control metrics by about 85%. The implementation was led by Dou Yanyan (豆妍妍), Haoxiangni’s deputy general manager and CFO, and powered by platform provider Hesī (合思), whose founder Ma Chunquan (马春荃) told attendees that industry differences matter: restaurant chains face many small, high-frequency claims; retail chains worry more about scale-driven anomalies like duplicated promotion claims.

Human roles: from bookkeepers to rule designers

Panelists including Gao Shijie (高世杰), vice‑president of Xinmingzhu (新明珠), stressed that technology alone is not the limiter — data quality, process design and cross‑departmental rulesetting are. As AI handles “deterministic” checks, finance staff are being redeployed from paper‑pushing to budget control, pre‑emptive monitoring and business partnering. The crux is governance: AI can apply rules, but people must define the business logic, boundaries and escalation paths. Otherwise automated errors cascade through cash, compliance and legal responsibilities.

Limits and the wider context

Caveats remain. AI effectiveness depends on clean data and mature workflows; messy digitization yields poor outcomes no matter the model. There is also a geopolitical layer: firms building enterprise AI must navigate export controls, semiconductor supply constraints and cloud software limits that affect access to advanced models and chips. Market enthusiasm for “AI agents” may be cooling, but practitioners say sustainable value will come from embedding AI into disciplined processes and upgrading human roles — not from hype alone.