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AI for Insurance: Scaling Compulsory Motor Insurance Renewal

Tasco · Posted 1 month ago

Automotive and mobilityRobotic Process Automation (RPA)Predictive Analytics / ForecastingConversational AI / Chatbots
Budget
To be determined
Timeline
Not specified
Closed on
15 Oct 2026, 23:59 ICT

Problem statement

Compulsory motor third-party liability insurance (TNDS BB) renewal in Vietnam remains fragmented across self-service, customer care, telesales and offline channels, creating friction for customers and making renewal difficult to scale efficiently. TASCO is looking for scalable, technology-agnostic solutions that can better identify renewal-eligible customers, reach them at the right time, improve conversion, reduce manual operational effort, and create a more seamless digital renewal journey — without relying on price discounts as the main lever.

Expected outcomes and success metrics

Develop a scalable renewal approach that materially increases the ability to identify, reach and convert renewal-eligible customers while reducing reliance on manual operational effort. Success should include: higher renewal conversion across digital and assisted channels; more proactive identification and engagement of customers approaching policy expiry; fewer manual touchpoints per renewal; a simpler and lower-friction customer journey; an approach that can scale during both normal periods and seasonal peaks; compliance with compulsory-insurance pricing and personal-data requirements; and a realistic path to pilot within weeks using feasible data and integration assumptions.

Current process

Current renewals are handled through a combination of self-service digital channels, inbound customer care and outbound telesales. These channels provide a foundation, but the overall journey remains fragmented. Customer care can support renewal effectively once customers engage, while outbound channels provide broader reach but require significant manual effort. There is also limited proactive capability to consistently identify and surface customers approaching policy expiry across channels. The opportunity is therefore not to replace every existing channel, but to make the overall renewal journey more connected, proactive and scalable.

Current solution

Current renewals are handled through a combination of self-service digital channels, inbound customer care and outbound telesales. These channels provide a foundation, but the overall journey remains fragmented. Customer care can support renewal effectively once customers engage, while outbound channels provide broader reach but require significant manual effort. There is also limited proactive capability to consistently identify and surface customers approaching policy expiry across channels. The opportunity is therefore not to replace every existing channel, but to make the overall renewal journey more connected, proactive and scalable.

Target users and impacted teams

Primary internal users and teams include: Insurance operations and renewal teams Customer care agents Telesales / outbound sales teams Digital product and self-service teams CRM / customer engagement teams Growth and distribution teams Data and analytics teams Partnership / ecosystem distribution teams End customers renewing compulsory motor insurance would also directly benefit from a simpler, faster and more seamless renewal experience.

Data availability and readiness

For the challenge stage, participants may work with a synthetic dataset or representative data schema rather than real customer PII. Relevant data may include vehicle type, policy history, policy expiry date, renewal status, and historical channel touchpoints. The exact scope and format of externally shareable data is still subject to sponsor and legal/compliance confirmation. Teams should clearly state what additional data their solution would require for a pilot, how that data would be used, and any assumptions around data quality, availability, consent, and access.

Integration, deployment and infrastructure

The solution should be designed to integrate with TASCO’s existing insurance and customer-engagement environment, but no specific production systems or cloud providers are prescribed at the challenge stage. Proposed solutions should clearly identify the integration points they would require, such as policy and vehicle data, policy expiry information, customer interaction history, digital/self-service channels, customer care, telesales, or partner channels. Deployment should support scalable operation across both normal and peak renewal periods. Any use of personal or customer data must comply with applicable Vietnamese personal data protection requirements, with teams clearly stating the data they assume access to and the corresponding consent or lawful-access basis. For the challenge stage, prototypes may use synthetic or sanitised data. Production deployment requirements, hosting model, security architecture, and system-specific integrations would be defined during a potential pilot with TASCO.

Relevant AI technologies

Robotic Process Automation (RPA)Predictive Analytics / ForecastingConversational AI / Chatbots