LC Vietnam Technology Company Limited
By LC Vietnam Technology Company LimitedCompany founded in 2014, focusing on: Predictive Analytics & Forecasting – Demand prediction, risk scoring, simulations, targeting markets: Financial Services
- Stage
- In pilot
- Industries
- Banking and finance
- Energy
- Manufacturing
- Deployment
- Hybrid
Why it is worth it
Unique Value Proposition DecisionGrid AI transforms customer behavior data into real-time, revenue-generating actions. Unlike traditional CRM and analytics tools that stop at insights, our platform continuously: Predicts customer intent and churn risk Determines the optimal next action based on behavior, context, and timing Automatically executes personalized actions across channels Learns from user response to improve decision accuracy over time What Makes Us Unique From Insight to Execution We close the gap between data analysis and action — turning predictions into automated, real-time decisions. Behavior-Centric Intelligence Our models are built around dynamic behavioral signals, not static customer profiles. Real-Time Decision Engine Instead of campaign-based marketing, we enable individual-level decisioning at scale. Proven in Real-World Environments Our core AI architecture has been validated in live systems requiring continuous data processing and anomaly detection. Core Value Statement We don’t just help institutions understand their customers — we help them act at the right moment to drive measurable business outcomes. In financial services, small timing advantages drive significant revenue impact — our system is designed to capture that moment.
- Financial institutions today face a critical gap between data availability and actionable decision-making.
- Low customer engagement and retention
- Despite large user bases, most MTS (Mobile Trading Systems) suffer from low daily active usage and weak retention. Existing systems rely on static campaigns and generic notifications that fail to adapt to real-time user behavior.
- Fragmented customer data with no behavioral intelligence
- Customer data is siloed across trading activity, app usage, and service interactions. Institutions lack a unified, real-time understanding of customer intent, leading to missed opportunities in cross-selling, upselling, and personalized services.
- Reactive instead of predictive decision-making
- Most CRM and analytics tools are retrospective. Financial institutions cannot accurately predict churn, future investment needs, or behavioral shifts, resulting in delayed or ineffective actions.
- Inefficient manual processes in operations and compliance
- Processes such as accounting reconciliation, anomaly detection, and compliance monitoring are still heavily manual, slow, and error-prone, increasing operational risk and cost.
- Lack of real-time “next best action” capability
- Even when insights exist, there is no automated system to translate them into timely actions (e.g., personalized content, alerts, or investment recommendations), leading to low conversion rates and lost revenue opportunities.
- As a result, financial institutions are operating with “data-rich but decision-poor” systems — where valuable signals exist but are not converted into measurable business outcomes.
Opening the deck…
Page 1
Best customer profile
- Our ideal customer segment consists of retail MTS users with established trading history but declining engagement signals.
- 1. Core Profile
- Retail investors aged 25–45
- Active account for ≥ 3 months
- Medium trading frequency (not beginners, not high-frequency traders)
- Use MTS regularly but show recent drop in activity
- 2. Behavioral Characteristics
- Previously active (consistent login & trading patterns)
- Recent decline in:
- login frequency
- session duration
- trading activity
- Still consuming financial content (news, insights, watchlists)
- 👉 Key insight:
- These users are not lost — they are hesitating or uncertain, making them highly responsive to the right intervention.
- 3. Why This Segment
- High potential for reactivation within short time frame
- Existing familiarity with platform → low friction to convert
- Strong correlation between engagement recovery and trading revenue increase
- 4. Excluded Segments (Strategic Focus)
- New users with no behavioral history
- High-frequency traders (already optimized)
- Fully inactive users (>60 days no activity)
- 👉 These segments either:
- lack sufficient data
- or provide lower short-term ROI for PoC
- 5. Expected Value from This Segment
- Fast measurable uplift in:
- engagement
- retention
- trading activity
- Clear demonstration of AI-driven decision impact within 12 weeks
Notable paying customers
- While our AI decision engine for financial services is currently entering the PoC stage, our core technology has been validated through paid deployments in enterprise environments requiring real-time behavior analysis and anomaly detection. 1. Industrial Manufacturing Clients (Vietnam & FDI factories) Use Case: IoT-based monitoring + AI-driven anomaly detection for energy and equipment usage Value Delivered: Real-time detection of inefficiencies Predictive insights for operational optimization Relevance to Shinhan: Shows our strength in real-time data processing, pattern detection, and decision support systems 2. Smart Operations Deployments (Multiple Sites) Use Case: Deployment of MMM (IoT + AI platform) for monitoring electricity, water, and gas usage Value Delivered: Continuous data collection + AI-based anomaly alerts Decision support for operational teams Relevance to Shinhan: Validates our ability to build end-to-end systems that convert raw data into real-world actions Positioning Statement While our prior deployments focus on operational and energy intelligence, the same core AI architecture — behavior modeling, anomaly detection, and decision automation — is directly applicable to financial customer behavior in MTS environments.
1. Product & Technology Milestones Developed and deployed MMM (IoT + AI platform) for real-time monitoring of electricity, water, and gas usage Built core AI capabilities: Behavioral pattern detection Real-time anomaly detection Predictive insights for operational optimization Designed a Decision Engine architecture that translates data signals into actionable recommendations 👉 Status: System deployed in real environments with continuous data flow 2. Commercial Traction Secured paid enterprise deployments in: Manufacturing factories (Vietnam & FDI clients) Delivered measurable value: Detection of abnormal energy usage Operational cost optimization Ongoing discussions with large enterprise clients for scaling deployments 👉 Status: Revenue-generating solution with enterprise validation 3. Data & AI Capability Maturity Processing real-time data streams from multiple sources (IoT devices, operational systems) Handling high-frequency data updates (near real-time intervals) Built experience in: Time-series analysis Anomaly detection in noisy environments Translating data into actionable decisions 👉 Status: Proven capability in real-time behavioral intelligence systems 4. Strategic Evolution Toward Financial AI Transitioning core technology from: Operational behavior intelligence → Customer behavior intelligence in financial services Designing AI-powered decision systems for MTS engagement and retention Preparing for PoC deployment with financial institutions (Shinhan InnoBoost 2026) 👉 Status: Entering financial domain with validated underlying technology 5. Current Stage From validated AI infrastructure in operational environments → to revenue-driven AI decision systems in financial services. Our traction demonstrates not only technical capability, but the ability to deploy AI systems that operate continuously in real-world environments and deliver measurable outcomes.