What's Happening?
PT. Bank Sinarmas Tbk has been recognized at the Indonesia Technology Excellence Awards 2025 for its innovative Mortgage Loan Origination System (LOS). This web-based platform, powered by PEGA, aims to streamline the mortgage approval process by integrating application submission, credit scoring, underwriting, and approvals into a single workflow. The system has significantly reduced manual workloads by 74% and improved accuracy by 80%, leading to faster approvals for customers. Since its implementation, the platform has increased applications by 174% and loan disbursements by 291%, while also boosting revenue by 76%. The LOS has received positive feedback from credit officers and customers alike, with plans for further expansion and integration of machine learning and big data to refine credit scoring models.
Why It's Important?
The introduction of the Mortgage Loan Origination System by PT. Bank Sinarmas Tbk represents a significant advancement in the banking sector, particularly in mortgage processing. By automating and streamlining complex operations, the platform not only enhances efficiency but also reduces errors and delays, benefiting both the bank and its customers. This innovation is likely to set a precedent for other financial institutions aiming to improve their service delivery and operational efficiency. The success of this system could lead to broader adoption of similar technologies across the industry, potentially transforming mortgage processing and lending practices on a larger scale.
What's Next?
PT. Bank Sinarmas Tbk plans to expand the capabilities of its Mortgage Loan Origination System by introducing configurable eligibility rules and a dynamic workflow engine. These enhancements will allow for greater alignment with sector-specific risk strategies and tailored approval paths based on product types and debtor profiles. The bank also intends to integrate more third-party verification processes to strengthen fraud prevention measures. Additionally, the use of machine learning and big data will be explored to further refine credit scoring models, supporting more personalized lending solutions.
