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实例探究 > Accelerated Customer Onboarding and Credit Approvals with Automated Systems

Accelerated Customer Onboarding and Credit Approvals with Automated Systems

技术
  • 功能应用 - 企业资源规划系统 (ERP)
  • 分析与建模 - 预测分析
适用行业
  • 农业
  • 矿业
适用功能
  • 商业运营
  • 质量保证
服务
  • 云规划/设计/实施服务
  • 系统集成
挑战
Mosaic faced significant challenges in their credit process within the order to cash cycle. Administering from North America and Brazil, they had a complex credit approval hierarchy with 7 to 10 layers of approval, which slowed down the process and affected customer experience. Additionally, they had to manually fetch credit data from various sources, which was time-consuming and labor-intensive. Their rigid credit scoring model did not account for country-specific risk factors, making it inefficient. Furthermore, the lack of real-time visibility into negative payment trends and macroeconomic fluctuations hindered their ability to proactively manage credit risk.
关于客户
Mosaic is the largest US producer of potash and concentrated phosphate, serving farmers globally. Headquartered in Tampa, Florida, this Fortune 500 company operates in six countries and reported a revenue of $8.9 billion in 2020. Mosaic employs a large workforce to manage its extensive operations and is a key player in the agriculture industry.
解决方案
Mosaic leveraged HighRadius' Credit Cloud solution to address their challenges. The AI-based solution automated the approval workflow by categorizing customers into different risk buckets, enabling auto-approvals for low-risk customers and redirecting higher value approvals to senior management. The solution also automated the aggregation of credit data from multiple global and local agencies, reducing manual effort. Customized credit scoring models allowed Mosaic to develop credit limits based on specific business units and risk segments. Real-time credit risk monitoring provided continuous oversight of customer portfolios, alerting the credit team to any changes in risk and recommending revised terms.
运营影响
  • The automated approval workflow significantly reduced the number of approval layers, streamlining the process.
  • Auto-aggregation of credit data from multiple sources minimized manual effort and improved efficiency.
  • Customized credit scoring models provided more accurate and flexible credit assessments.
数量效益
  • 50% reduction of credit approval layers.
  • 55.5% reduction in average approval time.
  • 15% increase in auto approvals.

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