Building an Integrated Business‑Finance Platform with Big Data and Cloud Technologies

Theory of Business‑Finance Integration

Business‑finance integration (业财一体化) means deeply merging business management with financial management to achieve information sharing, process collaboration, and data consistency, forming a key pillar of enterprise digital transformation.

Modern finance shifts from a back‑office accounting role to a value‑service orientation, requiring finance staff to understand business and vice‑versa. Traditional ERP limitations lead to a new agile model centered on a financial middle platform, which provides three core capabilities:

Fusion of business and financial processes to build an automated accounting system and deliver statutory, management, and custom reports.

Creation of data channels linking internal systems with third‑party finance systems (cash, tax, ledger, expense, travel, asset management), unifying data entry and reducing dependency on suppliers.

Extension of customized capabilities for external procurement systems, managing master data sharing to ensure consistency and improve operational efficiency.

Deep Process Integration

Integration requires precise identification of every business action that can affect revenue or cost, matching each with complete financial rules that comply with accounting standards and adapt dynamically to business changes.

Contract performance chain

Minimal transaction unit master‑data management (product quoting, equipment cost, settlement details)

Settlement detail = Minimal transaction unit (product) × Income type × Accounting period.

Technical Practice

Revenue accounting flow is split into real‑time, asynchronous, and batch processing stages to handle high‑throughput, IO‑intensive calculations using big‑data resources.

Application Layer Design

Divide by complete functional units to define clear boundaries, shorten communication paths, and reduce coupling.

Separate online, near‑line, and offline layers to match performance, precision, and latency requirements.

Computation Flow Splitting

Real‑time: revenue allocation per thread lifecycle. Asynchronous: use big‑data resources for IO‑intensive allocation. Batch: settlement processing.

Architecture Design

Layered architecture addresses system complexity and supports modular development.

Rule Matching Capability

Financial rules are visualized and managed via configuration, avoiding hard‑coded logic and enabling continuous iteration without code changes.

public void builder(){
    List<IotReceivableAllocRulePO> list = iotReceivableAllocRuleMapper.selectAvailableList();
    if(CollectionUtils.isEmpty(list)){
        log.error("收入分摊规则-RECEIVABLE,没有有效数据，请检查配置");
        return;
    }
    // Build rule models and maps ...
}

Dynamic rule matching based on data features:

public List<IotReceivableAllocRulePO> match(IotReceivableAccountPO accountPO){
    if(allocConfigMap == null || allocMap == null){
        log.error("请检查分摊规则是否构建完成。");
        return null;
    }
    Map<String,String> params = buildParams(accountPO);
    return compute(params);
}

Programming Patterns

Adopted patterns include:

CMP (Composed Method Pattern) – high‑level public methods read like step summaries, with details in private helpers.

SLAP (Single Level of Abstraction Principle) – keep each method at a consistent abstraction level.

Pipeline – modular stages process data sequentially, enabling flexibility, low coupling, and parallel execution.

Examples: task‑oriented dialogue bots, Tomcat and Netty pipelines.

Conclusion and Outlook

From traditional ERP to a cloud‑native, big‑data‑driven financial middle platform, the evolution enables real‑time data interaction. Future AI and large‑model technologies will shift finance from post‑event recording to predictive and proactive control, demanding cross‑domain expertise and clean, maintainable codebases.