About Us & Role

We are a financial technology infrastructure & collateralized loan-based platform. We work with the largest financial institutions in Indonesia. Focused on large item collateralized loans, and are looking to expand the platform to enable back-to-back-channeling with key financial partners to replicate the risk appetite (RAC) of the financial institution and build an AI-based risk assessment to increase approval speeds and approval ratings, effectively creating in-principle pre-approval. We are looking for someone who understands how to develop RAC scores from a large item collateralized loan within the banks and has executive relationships with banking partners. The role requires extensive use of data analytics to derive insights critical to evolving our fraud mitigation and credit underwriting practices. The role also has a focus on process automation, aiming to automate operational tasks whenever feasible.

Key Responsibilities

• Creating risk appetite framework (RAC), asset assessment, and implementation for large item collateralized loans in current Indonesian banking context
• Loan program creation and/or Portfolio management, cross-sell policies, etc.
• Risk appetite for the channel – profitability, NPL, participation in stress tests
• Conduct new tests up to approved thresholds
• Product programs, deviation/breach tracking
• Portfolio monitoring, review of performance thresholds, and timely interventions
• Build & create essential requirements to advise & partner closely with internal data science, engineering & product teams to productize AI-based RAC engine
• Building long-term relationships with banking executives and partnering with champions in the banks to drive integration

To succeed in the role, you must be proficient in English, have solid experience in credit risk and portfolio management, and someone from the Banking Industry.

Key Requirements

• Bachelor's degree with exposure to consumer risk management in previous roles for at least five years
• Analytical mind, strong numerical capability, understanding of statistics
• Strong understanding of risk management, credit analysis, and credit policies, fraud mitigation
• Expertise in handling databases and analytical tools – SAS, SQL programming, Tableau, e-Miner, Decision Tree, Python (a plus), etc.
• Robust data visualization skills, with the tendency to create clear, concise exhibits
• Exposure to building risk scorecards and models
• Current relationships with risk departments of banks, as well as bank executive relationships