BPJS
Customer late payment behavior model & prediction using ML and AI
Scope of Work
Design · Development · Implementation
Solutions
Web Dashboard · Machine Learning Models & Predictions
An Outlook at Overdue Payment
Badan Penyelenggara Jaminan Sosial (BPJS) is Indonesia’s national health services. Every month half of their customer bills are left unpaid, that prediction for names with overdue potentials for next month became a vital need in BPJS.
To provide a comprehensive view on this matters, Mars Indonesia Digital collaborate with BPJS to look upon solutions for the overdue payment.
In-Depth Lateness Analysis &
Predictions
Late payment is a behaviour influenced by several factors and variables. To understand their significance, MID analyze variables related to customers’s profile and transactions with varying degree of significance.
We look upon those that gives most impact, and provides predictions for profile with most potentials of overdue in the following months.
Know Early, Act Early
From analysis, BPJS data brough out lot of findings to be anticipated. Systems infers those findings and recommending follow-up actions to best solve those problems
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